Name: Business and Data Analysis with SQL
Price: 25 GBP
Availability: InStock

<strong>This course will help you explore the world of Big Data technologies and frameworks. You will develop skills that will help you to pick the right Big Data technology and framework for your job and build the confidence to design robust Big Data pipelines.</strong>
        <p>Do you want a guide that will help you to pick the right Big Data technology for your project? Or do you want to get a solid understanding of the Big Data architecture and pipelines? This course will help you out.

After highlighting the course structure and learning objectives, the course will take you through the steps needed for setting up the environment. Next, you will understand the Big Data logical architecture, study the evolution of Big Data technologies, and explore Big Data pipelines. Moving along, you will become familiar with ingestion frameworks, such as Kafka, Flume, Nifi, and Sqoop. Next, you will learn about key storage frameworks, such as HDFS, HBase, Kudu, and Cassandra. Finally, you will go through the various data formats and uncover key data processing and data analysis frameworks.

By the end of this course, you will have a good understanding of the Big Data architecture and technologies and will have developed the skills to build real-world Big Data pipelines.

All the resources and support files for this course are available at https://github.com/PacktPublishing/Big-Data-for-Architects</p>
        <h2>What You Will Learn</h2><p>Create a Google account and a Dataproc cluster<br>Understand the Big Data architecture and pipelines<br>Study factors to consider while comparing ingestion frameworks<br>Gain a solid understanding of storage frameworks<br>Distinguish between text and binary data format<br>Find the key differences between the Spark, Tez, and Flink frameworks<br>Build a scalable Extract, Transform, Load (ETL) pipeline with Kafka Connect</p>
        <h2>Audience</h2><p>If you are a software engineer, who is looking to build Big Data pipelines or planning to appear for certifications such as CCA175 or CCA159, this video course is for you. A basic understanding of Big Data is needed to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanations, white-board sessions, and interesting activities, this course will make you familiar with the Big Data architecture and technologies. It will make you confident to design Big Data pipelines using modern frameworks.</p>
        <h2>Key Features</h2><p>Get a holistic picture of the Big Data ecosystem * Become an expert in choosing Big Data technology as per the requirements * Get ready to build end-to-end Big Data batch and streaming pipelines</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Big-Data-for-Architects</p>
        <h2>About the Author</h2><h3>Bhavuk Chawla</h3><p>Bhavuk Chawla has over 16 years of experience in IT, more than 8 years of experience implementing Cloud/ML/AI/Big Data Science related projects. He is an official instructor for Google, Confluent, and Cloudera. He has delivered and continues to deliver his training sessions in various companies including Google Singapore, Microsoft Bengaluru (Bangalore), Starbucks Coffee Seattle, Adobe India, EMEA Region, and more. He was recognized by Cloudera as the Instructor of the Year 2016 (APAC) for his exceptionally high ratings received in various training sessions.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Structure and Approach</p><p style='margin: 0'>This video highlights the course structure and explains how to approach the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Pre-Requisites</p><p style='margin: 0'>This video focuses on the course pre-requisites.</p></td></tr><tr><td style='padding: 15px;'><p>3. Course Audience</p><p style='margin: 0'>This video focuses on the course audience.</p></td></tr><tr><td style='padding: 15px;'><p>4. About the Author</p><p style='margin: 0'>This video introduces you to the author.</p></td></tr></tbody></table><br><h4>2. Setting Up the Environment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting up a Google Cloud Account</p><p style='margin: 0'>This video demonstrates how to set up a Google Cloud account.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Dataproc Cluster</p><p style='margin: 0'>This video explains how to create a Dataproc cluster.</p></td></tr><tr><td style='padding: 15px;'><p>3. Google Cloud Platform (GCP) Account Best Practices</p><p style='margin: 0'>This video focuses on the best practices when using a GCP account.</p></td></tr></tbody></table><br><h4>3. Holistic View of Architectures and Pipelines</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Big Data Logical Architecture</p><p style='margin: 0'>This video explains the Big Data logical architecture.</p></td></tr><tr><td style='padding: 15px;'><p>2. Evolution of Big Data Technologies</p><p style='margin: 0'>This video focuses on the evolution of Big Data technologies.</p></td></tr><tr><td style='padding: 15px;'><p>3. Key Big Data Architectures</p><p style='margin: 0'>This video explains key Big Data architectures.</p></td></tr><tr><td style='padding: 15px;'><p>4. Typical Big Data Batch Pipeline</p><p style='margin: 0'>This video introduces you to the typical Big Data batch pipeline.</p></td></tr><tr><td style='padding: 15px;'><p>5. Typical Big Data Streaming Pipeline</p><p style='margin: 0'>This video explains the typical Big Data streaming pipeline.</p></td></tr><tr><td style='padding: 15px;'><p>6. Example 01: Big Data Streaming Pipeline</p><p style='margin: 0'>This video presents an example of Big Data streaming pipeline.</p></td></tr><tr><td style='padding: 15px;'><p>7. Example 02: Big Data Streaming Pipeline</p><p style='margin: 0'>This video presents another example of a Big Data streaming pipeline.</p></td></tr></tbody></table><br><h4>4. Key Ingestion/Dataflow Frameworks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Factors to Consider while Comparing Ingestion Frameworks</p><p style='margin: 0'>This video highlights the factors to consider while comparing ingestion frameworks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Kafka Versus Flume</p><p style='margin: 0'>This video highlights the difference between Kafka and Flume.</p></td></tr><tr><td style='padding: 15px;'><p>3. NiFi Versus Kafka</p><p style='margin: 0'>This video provides the difference between NiFi and Kafka.</p></td></tr><tr><td style='padding: 15px;'><p>4. Sqoop Versus Flume</p><p style='margin: 0'>This video explains the difference between Sqoop and Flume.</p></td></tr><tr><td style='padding: 15px;'><p>5. Sqoop Versus Kafka Connect</p><p style='margin: 0'>This video highlights the difference between Sqoop and Kafka Connect.</p></td></tr><tr><td style='padding: 15px;'><p>6. Installing NiFi</p><p style='margin: 0'>This video demonstrates how to install NiFi.</p></td></tr><tr><td style='padding: 15px;'><p>7. Installing Kafka</p><p style='margin: 0'>This video explains how to install Kafka.</p></td></tr><tr><td style='padding: 15px;'><p>8. Hands-on Kafka and NiFi Integration Background</p><p style='margin: 0'>This video provides a background of Kafka and NiFi integration.</p></td></tr><tr><td style='padding: 15px;'><p>9. Integrating Kafka and NiFi</p><p style='margin: 0'>This video shows how to integrate Kafka and NiFi.</p></td></tr></tbody></table><br><h4>5. Key Storage Frameworks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Factors to Consider while Comparing Storage Frameworks</p><p style='margin: 0'>This video highlights the factors to consider while comparing storage frameworks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hadoop Distributed File System (HDFS) Versus HBase</p><p style='margin: 0'>This video highlights the difference between HDFS and HBase.</p></td></tr><tr><td style='padding: 15px;'><p>3. HBase Versus Kudu</p><p style='margin: 0'>This video explains the difference between HBase and Kudu.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hadoop Distributed File System (HDFS) Versus Kudu</p><p style='margin: 0'>This video provides the difference between HDFS and Kudu.</p></td></tr><tr><td style='padding: 15px;'><p>5. HBase Versus Cassandra</p><p style='margin: 0'>This video highlights the difference between HBase and Cassandra.</p></td></tr></tbody></table><br><h4>6. Data formats</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Text Versus Binary</p><p style='margin: 0'>This video highlights the difference between text and binary.</p></td></tr><tr><td style='padding: 15px;'><p>2. Interoperability</p><p style='margin: 0'>This video focuses on interoperability.</p></td></tr><tr><td style='padding: 15px;'><p>3. Row-Oriented Versus Column-Oriented</p><p style='margin: 0'>This video explains the difference between row-oriented and column-oriented.</p></td></tr><tr><td style='padding: 15px;'><p>4. Splittable Formats</p><p style='margin: 0'>This video introduces you to splittable formats.</p></td></tr><tr><td style='padding: 15px;'><p>5. Schema Evolution</p><p style='margin: 0'>This video focuses on schema evolution.</p></td></tr><tr><td style='padding: 15px;'><p>6. Comparing Data Formats</p><p style='margin: 0'>This video compares data formats.</p></td></tr><tr><td style='padding: 15px;'><p>7. Installing Sqoop on Dataproc Cluster</p><p style='margin: 0'>This video demonstrates how to install Sqoop on Dataproc cluster.</p></td></tr><tr><td style='padding: 15px;'><p>8. Hands-on Big Data Batch Pipeline Using the Avro Format</p><p style='margin: 0'>This video focuses on Big Data batch pipeline using the Avro format.</p></td></tr></tbody></table><br><h4>7. Key Data Processing Frameworks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Factors to Consider while Comparing Processing Frameworks</p><p style='margin: 0'>This video highlights the factors to consider while comparing processing frameworks.</p></td></tr><tr><td style='padding: 15px;'><p>2. MapReduce (MR) Versus Spark Logical Architecture</p><p style='margin: 0'>This video highlights the difference between MR and Spark logical architecture.</p></td></tr><tr><td style='padding: 15px;'><p>3. MapReduce (MR) Versus Spark Performance</p><p style='margin: 0'>This video provides the difference between MR and Spark performance.</p></td></tr><tr><td style='padding: 15px;'><p>4. Spark Versus Tez</p><p style='margin: 0'>This video explains the difference between Spark and Tez.</p></td></tr><tr><td style='padding: 15px;'><p>5. Spark Versus Flink</p><p style='margin: 0'>This video highlights the difference between Spark and Flink.</p></td></tr><tr><td style='padding: 15px;'><p>6. Kafka Streams Versus Spark Streaming</p><p style='margin: 0'>This video highlights the difference between Kafka streams and Spark streaming.</p></td></tr><tr><td style='padding: 15px;'><p>7. Spark 2.x Streaming Versus Spark 1.x Streaming</p><p style='margin: 0'>This video provides the difference between Spark 2.x streaming and Spark 1.x streaming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Spark Core Versus Spark Structured Query Language (SQL)</p><p style='margin: 0'>This video explains the difference between Spark core and Spark SQL.</p></td></tr><tr><td style='padding: 15px;'><p>9. Integrating Kafka and Spark Streaming</p><p style='margin: 0'>This video demonstrates how to integrate Kafka and Spark streaming.</p></td></tr></tbody></table><br><h4>8. Key Data Analysis Frameworks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Factors to Consider while Comparing Analysis Frameworks</p><p style='margin: 0'>This video highlights the factors to consider while comparing analysis frameworks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hive Versus Impala</p><p style='margin: 0'>This video highlights the difference between Hive and Impala.</p></td></tr><tr><td style='padding: 15px;'><p>3. Hive Versus Pig</p><p style='margin: 0'>This video provides the difference between Hive and Pig.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hive Versus Spark Structured Query Language (SQL)</p><p style='margin: 0'>This video explains the difference between Hive and Spark SQL.</p></td></tr><tr><td style='padding: 15px;'><p>5. Hive Versus Hive Live Long and Process (LLAP) Versus Impala</p><p style='margin: 0'>This video highlights the difference between Hive, Hive LLAP, and Impala.</p></td></tr><tr><td style='padding: 15px;'><p>6. Hive Versus KSQL</p><p style='margin: 0'>This video provides the difference between Hive and KSQL.</p></td></tr><tr><td style='padding: 15px;'><p>7. KSQL Versus KSQLDB</p><p style='margin: 0'>This video explains the difference between KSQL and KSQLDB.</p></td></tr><tr><td style='padding: 15px;'><p>8. Hands-On KSQL</p><p style='margin: 0'>This video explains how to work with KSQL.</p></td></tr><tr><td style='padding: 15px;'><p>9. Writing to a Stream and Table Using KSQL</p><p style='margin: 0'>This video demonstrates how to write to a stream and table using KSQL.</p></td></tr><tr><td style='padding: 15px;'><p>10. Streaming Extract, Transform, Load (ETL) Pipeline Background</p><p style='margin: 0'>This video provides a background of how to stream the ETL pipeline.</p></td></tr><tr><td style='padding: 15px;'><p>11. Building a Scalable Extract, Transform, Load (ETL) Pipeline with Kafka Connect - Part 1</p><p style='margin: 0'>This is the first part of the two-part video that shows how to build a scalable ETL pipeline with Kafka Connect.</p></td></tr><tr><td style='padding: 15px;'><p>12. Building a Scalable Extract, Transform, Load (ETL) Pipeline with Kafka Connect - Part 2</p><p style='margin: 0'>This is the second part of the two-part video that demonstrates how to build a scalable ETL pipeline with the Kafka Connect.</p></td></tr></tbody></table><br><h4>9. Delta Lake</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Delta Architecture</p><p style='margin: 0'>This video explains the Delta architecture in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Delta Lake</p><p style='margin: 0'>Let's understand why Delta Lake is important in this lesson.</p></td></tr><tr><td style='padding: 15px;'><p>3. Challenges with Delta Lake</p><p style='margin: 0'>This video talks about the different challenges with Delta Lake.</p></td></tr><tr><td style='padding: 15px;'><p>4. Delta Lake Demo</p><p style='margin: 0'>Let's take a look at Delta Lake demonstration in this video session.</p></td></tr></tbody></table><br><h4>10. Additional Material</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Solr Versus Elasticsearch</p><p style='margin: 0'>This video highlights the difference between Solr and Elasticsearch.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cloudera Search Versus Solr</p><p style='margin: 0'>This video provides the difference between Cloudera search and Solr.</p></td></tr><tr><td style='padding: 15px;'><p>3. Oozie Versus Airflow</p><p style='margin: 0'>This video explains the difference between Oozie and Airflow.</p></td></tr><tr><td style='padding: 15px;'><p>4. KSQL Versus KStreams</p><p style='margin: 0'>This video highlights the difference between KSQL and KStreams.</p></td></tr></tbody></table><br><h4>11. Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Conclusion</p><p style='margin: 0'>This video provides the course conclusion.</p></td></tr></tbody></table>

This course will help you explore the world of Big Data technologies and frameworks. You will develop skills that will help you to pick the right Big Data technology and framework for your job and build the confidence to design robust Big Data pipelines.
Do you want a guide that will help you to pick the right Big Data technology for your project? Or do you want to get a solid understanding of the Big Data architecture and pipelines? This course will help you out. After highlighting the course structure and learning objectives, the course will take you through the steps needed for setting up the environment. Next, you will understand the Big Data logical architecture, study the evolution of Big Data technologies, and explore Big Data pipelines. Moving along, you will become familiar with ingestion frameworks, such as Kafka, Flume, Nifi, and Sqoop. Next, you will learn about key storage frameworks, such as HDFS, HBase, Kudu, and Cassandra. Finally, you will go through the various data formats and uncover key data processing and data analysis frameworks. By the end of this course, you will have a good understanding of the Big Data architecture and technologies and will have developed the skills to build real-world Big Data pipelines. All the resources and support files for this course are available at https://github.com/PacktPublishing/Big-Data-for-Architects
What You Will Learn
Create a Google account and a Dataproc cluster
Understand the Big Data architecture and pipelines
Study factors to consider while comparing ingestion frameworks
Gain a solid understanding of storage frameworks
Distinguish between text and binary data format
Find the key differences between the Spark, Tez, and Flink frameworks
Build a scalable Extract, Transform, Load (ETL) pipeline with Kafka Connect
Audience
If you are a software engineer, who is looking to build Big Data pipelines or planning to appear for certifications such as CCA175 or CCA159, this video course is for you. A basic understanding of Big Data is needed to get started with this course.
Approach
With the help of simple explanations, white-board sessions, and interesting activities, this course will make you familiar with the Big Data architecture and technologies. It will make you confident to design Big Data pipelines using modern frameworks.
Key Features
Get a holistic picture of the Big Data ecosystem * Become an expert in choosing Big Data technology as per the requirements * Get ready to build end-to-end Big Data batch and streaming pipelines
Github Repo
https://github.com/PacktPublishing/Big-Data-for-Architects
About the Author
Bhavuk Chawla
Bhavuk Chawla has over 16 years of experience in IT, more than 8 years of experience implementing Cloud/ML/AI/Big Data Science related projects. He is an official instructor for Google, Confluent, and Cloudera. He has delivered and continues to deliver his training sessions in various companies including Google Singapore, Microsoft Bengaluru (Bangalore), Starbucks Coffee Seattle, Adobe India, EMEA Region, and more. He was recognized by Cloudera as the Instructor of the Year 2016 (APAC) for his exceptionally high ratings received in various training sessions.
Course Outline
1. Introduction

1. Course Structure and Approach
This video highlights the course structure and explains how to approach the course.
2. Course Pre-Requisites
This video focuses on the course pre-requisites.
3. Course Audience
This video focuses on the course audience.
4. About the Author
This video introduces you to the author.
2. Setting Up the Environment

1. Setting up a Google Cloud Account
This video demonstrates how to set up a Google Cloud account.
2. Creating a Dataproc Cluster
This video explains how to create a Dataproc cluster.
3. Google Cloud Platform (GCP) Account Best Practices
This video focuses on the best practices when using a GCP account.
3. Holistic View of Architectures and Pipelines

1. Big Data Logical Architecture
This video explains the Big Data logical architecture.
2. Evolution of Big Data Technologies
This video focuses on the evolution of Big Data technologies.
3. Key Big Data Architectures
This video explains key Big Data architectures.
4. Typical Big Data Batch Pipeline
This video introduces you to the typical Big Data batch pipeline.
5. Typical Big Data Streaming Pipeline
This video explains the typical Big Data streaming pipeline.
6. Example 01: Big Data Streaming Pipeline
This video presents an example of Big Data streaming pipeline.
7. Example 02: Big Data Streaming Pipeline
This video presents another example of a Big Data streaming pipeline.
4. Key Ingestion/Dataflow Frameworks

1. Factors to Consider while Comparing Ingestion Frameworks
This video highlights the factors to consider while comparing ingestion frameworks.
2. Kafka Versus Flume
This video highlights the difference between Kafka and Flume.
3. NiFi Versus Kafka
This video provides the difference between NiFi and Kafka.
4. Sqoop Versus Flume
This video explains the difference between Sqoop and Flume.
5. Sqoop Versus Kafka Connect
This video highlights the difference between Sqoop and Kafka Connect.
6. Installing NiFi
This video demonstrates how to install NiFi.
7. Installing Kafka
This video explains how to install Kafka.
8. Hands-on Kafka and NiFi Integration Background
This video provides a background of Kafka and NiFi integration.
9. Integrating Kafka and NiFi
This video shows how to integrate Kafka and NiFi.
5. Key Storage Frameworks

1. Factors to Consider while Comparing Storage Frameworks
This video highlights the factors to consider while comparing storage frameworks.
2. Hadoop Distributed File System (HDFS) Versus HBase
This video highlights the difference between HDFS and HBase.
3. HBase Versus Kudu
This video explains the difference between HBase and Kudu.
4. Hadoop Distributed File System (HDFS) Versus Kudu
This video provides the difference between HDFS and Kudu.
5. HBase Versus Cassandra
This video highlights the difference between HBase and Cassandra.
6. Data formats

1. Text Versus Binary
This video highlights the difference between text and binary.
2. Interoperability
This video focuses on interoperability.
3. Row-Oriented Versus Column-Oriented
This video explains the difference between row-oriented and column-oriented.
4. Splittable Formats
This video introduces you to splittable formats.
5. Schema Evolution
This video focuses on schema evolution.
6. Comparing Data Formats
This video compares data formats.
7. Installing Sqoop on Dataproc Cluster
This video demonstrates how to install Sqoop on Dataproc cluster.
8. Hands-on Big Data Batch Pipeline Using the Avro Format
This video focuses on Big Data batch pipeline using the Avro format.
7. Key Data Processing Frameworks

1. Factors to Consider while Comparing Processing Frameworks
This video highlights the factors to consider while comparing processing frameworks.
2. MapReduce (MR) Versus Spark Logical Architecture
This video highlights the difference between MR and Spark logical architecture.
3. MapReduce (MR) Versus Spark Performance
This video provides the difference between MR and Spark performance.
4. Spark Versus Tez
This video explains the difference between Spark and Tez.
5. Spark Versus Flink
This video highlights the difference between Spark and Flink.
6. Kafka Streams Versus Spark Streaming
This video highlights the difference between Kafka streams and Spark streaming.
7. Spark 2.x Streaming Versus Spark 1.x Streaming
This video provides the difference between Spark 2.x streaming and Spark 1.x streaming.
8. Spark Core Versus Spark Structured Query Language (SQL)
This video explains the difference between Spark core and Spark SQL.
9. Integrating Kafka and Spark Streaming
This video demonstrates how to integrate Kafka and Spark streaming.
8. Key Data Analysis Frameworks

1. Factors to Consider while Comparing Analysis Frameworks
This video highlights the factors to consider while comparing analysis frameworks.
2. Hive Versus Impala
This video highlights the difference between Hive and Impala.
3. Hive Versus Pig
This video provides the difference between Hive and Pig.
4. Hive Versus Spark Structured Query Language (SQL)
This video explains the difference between Hive and Spark SQL.
5. Hive Versus Hive Live Long and Process (LLAP) Versus Impala
This video highlights the difference between Hive, Hive LLAP, and Impala.
6. Hive Versus KSQL
This video provides the difference between Hive and KSQL.
7. KSQL Versus KSQLDB
This video explains the difference between KSQL and KSQLDB.
8. Hands-On KSQL
This video explains how to work with KSQL.
9. Writing to a Stream and Table Using KSQL
This video demonstrates how to write to a stream and table using KSQL.
10. Streaming Extract, Transform, Load (ETL) Pipeline Background
This video provides a background of how to stream the ETL pipeline.
11. Building a Scalable Extract, Transform, Load (ETL) Pipeline with Kafka Connect - Part 1
This is the first part of the two-part video that shows how to build a scalable ETL pipeline with Kafka Connect.
12. Building a Scalable Extract, Transform, Load (ETL) Pipeline with Kafka Connect - Part 2
This is the second part of the two-part video that demonstrates how to build a scalable ETL pipeline with the Kafka Connect.
9. Delta Lake

1. Delta Architecture
This video explains the Delta architecture in detail.
2. Why Delta Lake
Let's understand why Delta Lake is important in this lesson.
3. Challenges with Delta Lake
This video talks about the different challenges with Delta Lake.
4. Delta Lake Demo
Let's take a look at Delta Lake demonstration in this video session.
10. Additional Material

1. Solr Versus Elasticsearch
This video highlights the difference between Solr and Elasticsearch.
2. Cloudera Search Versus Solr
This video provides the difference between Cloudera search and Solr.
3. Oozie Versus Airflow
This video explains the difference between Oozie and Airflow.
4. KSQL Versus KStreams
This video highlights the difference between KSQL and KStreams.
11. Summary

1. Conclusion
This video provides the course conclusion.

Packt

Hadoop

Cloud

Apache Kafka

Big Data

Kafka

This course will help you explore the world of Big Data technologies and frameworks. You will develop skills that will help you to pick the right Big Data technology and framework for your job and build the confidence to design robust Big Data pipelines.

Big Data for Architects

General Admission

Data Visualization

Security

Data Processing

This course covers the important topics needed to pass the AWS Certified Data Analytics-Specialty exam (AWS DAS-C01). You will learn about Kinesis, EMR, DynamoDB, and Redshift, and get ready for the exam by working through quizzes, exercises, and practice exams, along with exploring essential tips and techniques.

AWS Certified Data Analytics Specialty (2023) Hands-on

<strong>In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.</strong>
        <p>Data cleaning is always a big hassle, especially if we are short on time and want to deliver crucial data analysis insights to our audience. KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.

In this course, we will learn how to use additional helpful KNIME nodes not covered in the other two classes. Solve data cleaning challenges together for different datasets. Use pre-trained models in TensorFlow in KNIME (involves Python coding).

Also, learn the fundamentals for NLP tasks (Natural Language Processing) in KNIME using only KNIME nodes (without any additional coding).

By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.

All the resources and support files for this course are available at https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning</p>
        <h2>What You Will Learn</h2><p>How to use TensorFlow in KNIME<br>How to do data science in KNIME with and without coding<br>How to solve data cleaning and data preparation challenges<br>How to replace Excel and start KNIME for ETL and data cleaning issues<br>Examples of data science machine learning workflows with KNIME</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.</p>
        <h2>Approach</h2><p>This course is practical and consists of a case study where you can and should follow along to solve tasks.</p>
        <h2>Key Features</h2><p>No coding required * Solve data cleaning challenges together and enhance your basic KNIME skills * Learn the fundamentals for NLP tasks in KNIME using only KNIME nodes</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning</p>
        <h2>About the Author</h2><h3>Dan We</h3><p>Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master's degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors.

He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.</p>
        <h2>Course Outline</h2><h4>1. Einleitung</h4><p></p><br><h4>2. Older videos KNIME version before 4.3</h4><p></p>

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.
Data cleaning is always a big hassle, especially if we are short on time and want to deliver crucial data analysis insights to our audience. KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool. In this course, we will learn how to use additional helpful KNIME nodes not covered in the other two classes. Solve data cleaning challenges together for different datasets. Use pre-trained models in TensorFlow in KNIME (involves Python coding). Also, learn the fundamentals for NLP tasks (Natural Language Processing) in KNIME using only KNIME nodes (without any additional coding). By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code. All the resources and support files for this course are available at https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning
What You Will Learn
How to use TensorFlow in KNIME
How to do data science in KNIME with and without coding
How to solve data cleaning and data preparation challenges
How to replace Excel and start KNIME for ETL and data cleaning issues
Examples of data science machine learning workflows with KNIME
Audience
This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.
Approach
This course is practical and consists of a case study where you can and should follow along to solve tasks.
Key Features
No coding required * Solve data cleaning challenges together and enhance your basic KNIME skills * Learn the fundamentals for NLP tasks in KNIME using only KNIME nodes
Github Repo
https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning
About the Author
Dan We
Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master's degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors. He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.
Course Outline
1. Einleitung

2. Older videos KNIME version before 4.3


Data Science

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.

KNIME for Data Science and Data Cleaning

<strong>The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.</strong>
        <p>The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you'll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you'll learn how to execute end-to-end workflows using PySpark.

Right through the course, you'll be using PySpark to perform data analysis. You'll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you'll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You'll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You'll use the Databricks environment to run the Spark scripts and explore it as well.

Finally, you'll have a taste of Spark with AWS cloud. You'll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data.

By the end of this course, you'll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems.

The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>What You Will Learn</h2><p>Learn the importance of Big Data<br>Explore the Spark and Hadoop architecture and ecosystem<br>Learn about PySpark Dataframes and PySpark DataFrames actions<br>Use PySpark DataFrames transformations<br>Apply collaborative filtering to develop a recommendation system using ALS models</p>
        <h2>Audience</h2><p>This course requires python programming experience as a prerequisite.</p>
        <h2>Approach</h2><p>In this learning-by-doing course, every theoretical explanation is followed by practical implementation. At the end of each concept, homework/tasks/activities/quizzes along with solutions are assigned. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.</p>
        <h2>Key Features</h2><p>Relate the concepts and practical aspects of Spark and AWS with real-world problems * Implement any project that requires PySpark knowledge from scratch * Know the theory and practical aspects of PySpark and AWS</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science.

They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Introduction to Hadoop, Spark EcoSystems and Architectures</h4><p></p><br><h4>3. Spark RDDs</h4><p></p><br><h4>4. Spark DFs</h4><p></p><br><h4>5. Collaborative filtering</h4><p></p><br><h4>6. Spark Streaming</h4><p></p><br><h4>7. ETL Pipeline</h4><p></p><br><h4>8. Project - Change Data Capture / Replication On Going</h4><p></p>

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.
The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you'll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you'll learn how to execute end-to-end workflows using PySpark. Right through the course, you'll be using PySpark to perform data analysis. You'll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you'll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You'll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You'll use the Databricks environment to run the Spark scripts and explore it as well. Finally, you'll have a taste of Spark with AWS cloud. You'll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data. By the end of this course, you'll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems. The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS
What You Will Learn
Learn the importance of Big Data
Explore the Spark and Hadoop architecture and ecosystem
Learn about PySpark Dataframes and PySpark DataFrames actions
Use PySpark DataFrames transformations
Apply collaborative filtering to develop a recommendation system using ALS models
Audience
This course requires python programming experience as a prerequisite.
Approach
In this learning-by-doing course, every theoretical explanation is followed by practical implementation. At the end of each concept, homework/tasks/activities/quizzes along with solutions are assigned. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.
Key Features
Relate the concepts and practical aspects of Spark and AWS with real-world problems * Implement any project that requires PySpark knowledge from scratch * Know the theory and practical aspects of PySpark and AWS
Github Repo
https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS
About the Author
AI Sciences
AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
Course Outline
1. Introduction

2. Introduction to Hadoop, Spark EcoSystems and Architectures

3. Spark RDDs

4. Spark DFs

5. Collaborative filtering

6. Spark Streaming

7. ETL Pipeline

8. Project - Change Data Capture / Replication On Going


Java

Apache Spark

Python

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.

PySpark and AWS: Master Big Data with PySpark and AWS

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					<div><p><span>Learn how to manipulate huge amounts of online data and effectively transfer it for personal projects by taking this course on </span><b>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4. </b><span>Using expert guidance and clear instructions, this intuitive coaching will allow you to create an end-to-end GIS (Geographic Information System) application for practical uses and impressive projects. </span></p><p><span>This comprehensive </span>Python <span>tutorial provides the ideal way to learn how to make the most of these data retrieval and utilisation functions. By making use of Django and ETL techniques, your ability to work with information systems will improve considerably and allow your projects to stand out in the crowd. On completion of the training, you will be able to perform complex data transfers with professional routines, regardless of the complexity of the task and the size of the records. </span></p></div>
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					<div><p>This best selling <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p>
<p>The <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p>
<p>After successfully completing the<strong> Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p>
<p>We know that you are busy and that time is precious, so we have designed the <strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p>
<p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Overview
Learn how to manipulate huge amounts of online data and effectively transfer it for personal projects by taking this course on Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4. Using expert guidance and clear instructions, this intuitive coaching will allow you to create an end-to-end GIS (Geographic Information System) application for practical uses and impressive projects. 
This comprehensive Python tutorial provides the ideal way to learn how to make the most of these data retrieval and utilisation functions. By making use of Django and ETL techniques, your ability to work with information systems will improve considerably and allow your projects to stand out in the crowd. On completion of the training, you will be able to perform complex data transfers with professional routines, regardless of the complexity of the task and the size of the records. 
This best selling Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.
The Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.
After successfully completing the Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 will help you stand out from the crowd. You can also validate your certification on our website.
We know that you are busy and that time is precious, so we have designed the Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. 
Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

Course Cloud

Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4

<strong>This course on MongoDB is for absolute beginners and provides an interactive learning experience that reflects the most in-demand skills. The content will help you understand the concepts and methodology with regards to MongoDB in an effortless way. The strong basic understanding you gain initially will help you move toward learning more advanced concepts.</strong>
        <p>MongoDB, a fast and scalable NoSQL database, is extremely popular right now. This object-oriented database is simple and dynamic.

In this course, you start by becoming proficient in the elementary concepts of MongoDB, including installing and setting up MongoDB Atlas, and understanding the database structure, collection structure, document structure, and basic Mongo commands.

Next, you'll explore the Basic, Create, Read, Update, and Delete operations in MongoDB. You'll then explore in detail the MongoDB query operators and project operators. Following this, you'll learn about MongoDB update operators. In the concluding modules, you'll explore MongoDB with Node and Python.

In the final module, you'll sharpen your practical knowledge of the theoretical concepts you learned in this course by working on two mini-projects. In the first project, you'll work on MongoDB with Django, in which you'll develop a CRUD-based application using Django and MongoDB. In the second project, you'll implement an ETL pipeline using PySpark to dump the data in MongoDB.

By the end of this course, you will be able to implement any project from scratch that requires MongoDB knowledge.

The complete code bundle for this course is available at https://github.com/PacktPublishing/MongoDB-Mastering-MongoDB-for-Beginners-Theory-and-Projects-</p>
        <h2>What You Will Learn</h2><p>Introduction to MongoDB and understand its importance<br>Understand MongoDB with Node, Python, and Django<br>Perform CRUD operations with Node, Python, Django in MongoDB Atlas<br>Understand different MongoDB CRUD operations<br>Build APIs for CRUD operations in MongoDB through Django<br>Understand MongoDB with PySpark</p>
        <h2>Audience</h2><p>This course is designed for absolute beginners. As a newbie in this field, you will spend sufficient time - in gaining a thorough understanding of the fundamental concepts. You also need a good understanding of SQL.<br><br>If you want to become a data scientist, database administrator, database engineer, or data engineer, then this course is for you.</p>
        <h2>Approach</h2><p>The course is designed for absolute beginners; hence, it is easy to understand, includes practical with live coding, and is full of challenging quizzes with solutions. You will then make steady progress with a lot of practical implementations where each step will be dealt with in detail.</p>
        <h2>Key Features</h2><p>Relate the concepts and practical applications of MongoDB with real-world problems * Implement any project from scratch that requires MongoDB knowledge * Know the theoretical and practical aspects of MongoDB</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/MongoDB-Mastering-MongoDB-for-Beginners-Theory-and-Projects-</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science.

They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Overview</h4><p></p><br><h4>3. Basic Mongo Operations</h4><p></p><br><h4>4. Basic Create Operation</h4><p></p><br><h4>5. Basic Update Operation</h4><p></p><br><h4>6. Basic Read Operation</h4><p></p><br><h4>7. Basic Delete Operation</h4><p></p><br><h4>8. Query and projection operators</h4><p></p><br><h4>9. Update Operators</h4><p></p><br><h4>10. Mongo with Node</h4><p></p><br><h4>11. Mongo with Python</h4><p></p><br><h4>12. Django with Mongo</h4><p></p><br><h4>13. Spark With Mongo</h4><p></p>

This course on MongoDB is for absolute beginners and provides an interactive learning experience that reflects the most in-demand skills. The content will help you understand the concepts and methodology with regards to MongoDB in an effortless way. The strong basic understanding you gain initially will help you move toward learning more advanced concepts.
MongoDB, a fast and scalable NoSQL database, is extremely popular right now. This object-oriented database is simple and dynamic. In this course, you start by becoming proficient in the elementary concepts of MongoDB, including installing and setting up MongoDB Atlas, and understanding the database structure, collection structure, document structure, and basic Mongo commands. Next, you'll explore the Basic, Create, Read, Update, and Delete operations in MongoDB. You'll then explore in detail the MongoDB query operators and project operators. Following this, you'll learn about MongoDB update operators. In the concluding modules, you'll explore MongoDB with Node and Python. In the final module, you'll sharpen your practical knowledge of the theoretical concepts you learned in this course by working on two mini-projects. In the first project, you'll work on MongoDB with Django, in which you'll develop a CRUD-based application using Django and MongoDB. In the second project, you'll implement an ETL pipeline using PySpark to dump the data in MongoDB. By the end of this course, you will be able to implement any project from scratch that requires MongoDB knowledge. The complete code bundle for this course is available at https://github.com/PacktPublishing/MongoDB-Mastering-MongoDB-for-Beginners-Theory-and-Projects-
What You Will Learn
Introduction to MongoDB and understand its importance
Understand MongoDB with Node, Python, and Django
Perform CRUD operations with Node, Python, Django in MongoDB Atlas
Understand different MongoDB CRUD operations
Build APIs for CRUD operations in MongoDB through Django
Understand MongoDB with PySpark
Audience
This course is designed for absolute beginners. As a newbie in this field, you will spend sufficient time - in gaining a thorough understanding of the fundamental concepts. You also need a good understanding of SQL.

If you want to become a data scientist, database administrator, database engineer, or data engineer, then this course is for you.
Approach
The course is designed for absolute beginners; hence, it is easy to understand, includes practical with live coding, and is full of challenging quizzes with solutions. You will then make steady progress with a lot of practical implementations where each step will be dealt with in detail.
Key Features
Relate the concepts and practical applications of MongoDB with real-world problems * Implement any project from scratch that requires MongoDB knowledge * Know the theoretical and practical aspects of MongoDB
Github Repo
https://github.com/PacktPublishing/MongoDB-Mastering-MongoDB-for-Beginners-Theory-and-Projects-
About the Author
AI Sciences
AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
Course Outline
1. Introduction

2. Overview

3. Basic Mongo Operations

4. Basic Create Operation

5. Basic Update Operation

6. Basic Read Operation

7. Basic Delete Operation

8. Query and projection operators

9. Update Operators

10. Mongo with Node

11. Mongo with Python

12. Django with Mongo

13. Spark With Mongo


Programming

MongoDB

This course on MongoDB is for absolute beginners and provides an interactive learning experience that reflects the most in-demand skills. The content will help you understand the concepts and methodology with regards to MongoDB in an effortless way. The strong basic understanding you gain initially will help you move toward learning more advanced concepts.

MongoDB-Mastering MongoDB for Beginners (Theory and Projects)

<strong>This course focuses on the beginner-level concepts of cloud computing in two different arenas.

The first part is to explore the world of database technologies or DBaaS (Database as a Service) and the second part revolves around IaaS (Infrastructure as a Service) model.</strong>
        <p>Over the last couple of years, many business companies decided to use more and more cloud services as part of their digital transformation, and you already know data is the new gold.

Therefore, this course is predominantly divided into two important parts: database technologies and infrastructure as a service.

The first part is about databases technologies and, even more specifically, how such technologies are offered as cloud database services (DBaaS) by leading public cloud providers-Azure, AWS, and GCP. We will learn the technical concept, market use cases, advantages, and disadvantages of each database technology while following its evolution path.

The next part is about the IaaS model. The IaaS model provided by large cloud providers is helping companies transform their private IT infrastructure into a utility service.

As a first step, you will review the key terms in cloud computing to establish a clear understanding of the big picture, and then we will zoom in on the IaaS model. The look at building blocks, what are the typical use cases, advantages as well as disadvantages, pricing models, and more.

By the end of this course, you will be able to select the right database technology for a specific use case to dramatically affect any aspect of an application and learn about the IaaS model of cloud computing.

The resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Cloud-Computing-for-Beginners---Database-Technologies-and-Infrastructure-as-a-Service</p>
        <h2>What You Will Learn</h2><p>Look at batch and stream processing, operational and analytics data<br>Understand SQL, NoSQL, NewSQL technologies<br>DBaaS portfolio overview - Azure, AWS, GCP<br>Learn the four service models - SaaS, PaaS, IaaS, and FaaS<br>Microservice and cloud-native apps, and different types of clouds<br>Virtualization technologies - virtual machines and containers</p>
        <h2>Audience</h2><p>This course is designed for the software developers, IT professionals, product/project/marketing managers who wish to reduce the footprint of enterprise applications sitting in private data centers and leverage the capabilities in a public cloud environment. Solution architects, technical advisors, telecom experts, and security professionals can also get benefit from this course.<br><br>You don't need to have prior knowledge of cloud computing concepts.</p>
        <h2>Approach</h2><p>This is a hands-on course designed for beginner-level individuals in the field of cloud computing. There are two parts in the course; the first focuses on database technologies and the other on the infrastructure model.</p>
        <h2>Key Features</h2><p>Learn the technical concept, market use cases, advantages, and disadvantages of the DBaaS model * Learn the technical concept, market use cases, advantages, and disadvantages of the IaaS model * Review key terms in cloud computing to establish a clear understanding</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Cloud-Computing-for-Beginners---Database-Technologies-and-Infrastructure-as-a-Service</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data is Everywhere</p><p style='margin: 0'>This video provides an overview of the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Our Learning Objectives</p><p style='margin: 0'>This video provides an extension and focus areas about this course in detail.</p></td></tr></tbody></table><br><h4>2. Basic Database Terminology</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. DBMS, Database Instance and Schema</p><p style='margin: 0'>This video explains the DBMS (Database management System), database instance and schema.</p></td></tr><tr><td style='padding: 15px;'><p>3. Operational Data and OLTP</p><p style='margin: 0'>This video explains operational data and OLTP (Online Transaction Processing).</p></td></tr><tr><td style='padding: 15px;'><p>4. Analytics Data and OLAP</p><p style='margin: 0'>This video explains analytics and OLAP (Online Analytical Processing).</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Warehouse and Data Lake</p><p style='margin: 0'>This video explains about the data warehouse and data lake.</p></td></tr><tr><td style='padding: 15px;'><p>6. ETL and ELT</p><p style='margin: 0'>This video explains ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).</p></td></tr><tr><td style='padding: 15px;'><p>7. Batch and Stream Processing</p><p style='margin: 0'>This video explains the batch and stream processing.</p></td></tr><tr><td style='padding: 15px;'><p>8. Scaling Up and Out</p><p style='margin: 0'>This video explains about scaling up and out.</p></td></tr></tbody></table><br><h4>3. Database Technologies - What, Where, and Why?</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Traditional Relational Database (SQL)</p><p style='margin: 0'>This video focuses on a traditional relational database.</p></td></tr><tr><td style='padding: 15px;'><p>3. Non-relational Database (NoSQL)</p><p style='margin: 0'>This video explains about the non-relational database.</p></td></tr><tr><td style='padding: 15px;'><p>4. Distributed SQL Database (NewSQL)</p><p style='margin: 0'>This video helps to understand about the distributed SQL database.</p></td></tr><tr><td style='padding: 15px;'><p>5. In-memory Database (IMDB)</p><p style='margin: 0'>This video talks about in-memory database.</p></td></tr><tr><td style='padding: 15px;'><p>6. Time Series Database (TSDB)</p><p style='margin: 0'>This video helps you understand the time series database.</p></td></tr></tbody></table><br><h4>4. Types of NoSQL Databases</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. #1 - Key-value Database</p><p style='margin: 0'>This video demonstrates the key-value database.</p></td></tr><tr><td style='padding: 15px;'><p>3. #2 - Document Database</p><p style='margin: 0'>This video talks about the document database.</p></td></tr><tr><td style='padding: 15px;'><p>4. #3 - Wide Column Database</p><p style='margin: 0'>This video talks about the wide column database.</p></td></tr><tr><td style='padding: 15px;'><p>5. #4 - Graph Database</p><p style='margin: 0'>This video talks about the graph database.</p></td></tr><tr><td style='padding: 15px;'><p>6. Databases Ranking Review</p><p style='margin: 0'>This video explains the databases ranking review.</p></td></tr></tbody></table><br><h4>5. Database as a Service (DBaaS)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. The Challenges of Traditional Databases</p><p style='margin: 0'>This video demonstrates the challenges of traditional databases.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Concept of DBaaS</p><p style='margin: 0'>This video explains the concept of DBaaS.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Advantages of DBaaS</p><p style='margin: 0'>This video shows the advantages of DBaaS.</p></td></tr><tr><td style='padding: 15px;'><p>5. Azure - DBaaS Portfolio Review</p><p style='margin: 0'>This video shows the DBaaS portfolio review of Azure platform.</p></td></tr><tr><td style='padding: 15px;'><p>6. AWS - DBaaS Portfolio Review</p><p style='margin: 0'>This video shows the DBaaS portfolio review of AWS platform.</p></td></tr><tr><td style='padding: 15px;'><p>7. GCP - DBaaS Portfolio Review</p><p style='margin: 0'>This video shows the DBaaS portfolio review of GCP platform.</p></td></tr></tbody></table><br><h4>6. Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Quick Recap and Thank You!</p><p style='margin: 0'>This video helps with the recap of the concepts learned so far.</p></td></tr></tbody></table><br><h4>7. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video provides an introduction to this part.</p></td></tr><tr><td style='padding: 15px;'><p>2. Our Learning Objectives</p><p style='margin: 0'>This video provides an extension and focus areas about this course in detail.</p></td></tr></tbody></table><br><h4>8. Cloud Computing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to the World of Cloud Computing</p><p style='margin: 0'>This video provides an introduction to the cloud computing concepts.</p></td></tr><tr><td style='padding: 15px;'><p>2. Private vs Public</p><p style='margin: 0'>This video explains the difference between private and public cloud computing.</p></td></tr><tr><td style='padding: 15px;'><p>3. Hybrid-cloud and Multi-cloud</p><p style='margin: 0'>This video explains the hybrid-cloud and multi-cloud.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hyperscale Cloud Service Providers</p><p style='margin: 0'>This video explains the hyperscale cloud service providers.</p></td></tr><tr><td style='padding: 15px;'><p>5. Cloud Service Models</p><p style='margin: 0'>This video explains the cloud service models.</p></td></tr><tr><td style='padding: 15px;'><p>6. SaaS - Software as a Service</p><p style='margin: 0'>This video talks about SaaS - Software as a Service.</p></td></tr><tr><td style='padding: 15px;'><p>7. IaaS - Infrastructure as a Service</p><p style='margin: 0'>This video talks about IaaS - Infrastructure as a Service.</p></td></tr><tr><td style='padding: 15px;'><p>8. PaaS - Platform as a Service</p><p style='margin: 0'>This video talks about PaaS - Platform as a Service.</p></td></tr><tr><td style='padding: 15px;'><p>9. FaaS - Function as a Service</p><p style='margin: 0'>This video talks about FaaS - Function as a Service.</p></td></tr><tr><td style='padding: 15px;'><p>10. Demo - AWS, Azure, GCP</p><p style='margin: 0'>This video provides demo on the various cloud service provides - AWS, Azure, and GCP.</p></td></tr></tbody></table><br><h4>9. Virtualization Technologies</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Underutilized Physical Servers</p><p style='margin: 0'>This video talks about underutilized physical servers.</p></td></tr><tr><td style='padding: 15px;'><p>3. Virtualization with Virtual Machines</p><p style='margin: 0'>This video demonstrates how to do virtualization with virtual machines.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vertical and Horizontal Scaling</p><p style='margin: 0'>This video explains the vertical and horizontal scaling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Microservices and Cloud-Native Apps</p><p style='margin: 0'>This video focuses on microservices and cloud-native apps.</p></td></tr><tr><td style='padding: 15px;'><p>6. Virtualization with Containers</p><p style='margin: 0'>This video demonstrates how to do virtualization with containers.</p></td></tr><tr><td style='padding: 15px;'><p>7. The Benefits of Containers</p><p style='margin: 0'>This video demonstrates the benefits of containers.</p></td></tr></tbody></table><br><h4>10. Introduction to Infrastructure as a Service (IaaS)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Overview</p><p style='margin: 0'>This video provides an overview and introduction to the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. IaaS - Transform IT to Utility</p><p style='margin: 0'>This video explains how to transform IT to utility in IaaS.</p></td></tr><tr><td style='padding: 15px;'><p>3. Compute, Storage and Networking</p><p style='margin: 0'>This video focuses on compute, storage and networking.</p></td></tr><tr><td style='padding: 15px;'><p>4. Demo - IaaS Solution with Microsoft Azure</p><p style='margin: 0'>This video provides a demo on IaaS solution with Microsoft Azure.</p></td></tr><tr><td style='padding: 15px;'><p>5. Pricing Models</p><p style='margin: 0'>This video talks about the pricing models in Microsoft Azure.</p></td></tr><tr><td style='padding: 15px;'><p>6. Main Advantages</p><p style='margin: 0'>This video focuses on the advantages of IaaS.</p></td></tr><tr><td style='padding: 15px;'><p>7. Disadvantages</p><p style='margin: 0'>This video tells the disadvantages of IaaS.</p></td></tr><tr><td style='padding: 15px;'><p>8. Typical Market Use Cases</p><p style='margin: 0'>This video explains the typical market use cases.</p></td></tr></tbody></table><br><h4>11. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Let's Recap and Thank You!</p><p style='margin: 0'>This video helps with a recap of the concepts learned so far.</p></td></tr></tbody></table>

This course focuses on the beginner-level concepts of cloud computing in two different arenas. The first part is to explore the world of database technologies or DBaaS (Database as a Service) and the second part revolves around IaaS (Infrastructure as a Service) model.
Over the last couple of years, many business companies decided to use more and more cloud services as part of their digital transformation, and you already know data is the new gold. Therefore, this course is predominantly divided into two important parts: database technologies and infrastructure as a service. The first part is about databases technologies and, even more specifically, how such technologies are offered as cloud database services (DBaaS) by leading public cloud providers-Azure, AWS, and GCP. We will learn the technical concept, market use cases, advantages, and disadvantages of each database technology while following its evolution path. The next part is about the IaaS model. The IaaS model provided by large cloud providers is helping companies transform their private IT infrastructure into a utility service. As a first step, you will review the key terms in cloud computing to establish a clear understanding of the big picture, and then we will zoom in on the IaaS model. The look at building blocks, what are the typical use cases, advantages as well as disadvantages, pricing models, and more. By the end of this course, you will be able to select the right database technology for a specific use case to dramatically affect any aspect of an application and learn about the IaaS model of cloud computing. The resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Cloud-Computing-for-Beginners---Database-Technologies-and-Infrastructure-as-a-Service
What You Will Learn
Look at batch and stream processing, operational and analytics data
Understand SQL, NoSQL, NewSQL technologies
DBaaS portfolio overview - Azure, AWS, GCP
Learn the four service models - SaaS, PaaS, IaaS, and FaaS
Microservice and cloud-native apps, and different types of clouds
Virtualization technologies - virtual machines and containers
Audience
This course is designed for the software developers, IT professionals, product/project/marketing managers who wish to reduce the footprint of enterprise applications sitting in private data centers and leverage the capabilities in a public cloud environment. Solution architects, technical advisors, telecom experts, and security professionals can also get benefit from this course.

You don't need to have prior knowledge of cloud computing concepts.
Approach
This is a hands-on course designed for beginner-level individuals in the field of cloud computing. There are two parts in the course; the first focuses on database technologies and the other on the infrastructure model.
Key Features
Learn the technical concept, market use cases, advantages, and disadvantages of the DBaaS model * Learn the technical concept, market use cases, advantages, and disadvantages of the IaaS model * Review key terms in cloud computing to establish a clear understanding
Github Repo
https://github.com/PacktPublishing/Cloud-Computing-for-Beginners---Database-Technologies-and-Infrastructure-as-a-Service
About the Author
Idan Gabrieli
Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies. Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers. Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.
Course Outline
1. Introduction to the Course

1. Data is Everywhere
This video provides an overview of the course.
2. Our Learning Objectives
This video provides an extension and focus areas about this course in detail.
2. Basic Database Terminology

1. Overview
This video provides an overview of the section.
2. DBMS, Database Instance and Schema
This video explains the DBMS (Database management System), database instance and schema.
3. Operational Data and OLTP
This video explains operational data and OLTP (Online Transaction Processing).
4. Analytics Data and OLAP
This video explains analytics and OLAP (Online Analytical Processing).
5. Data Warehouse and Data Lake
This video explains about the data warehouse and data lake.
6. ETL and ELT
This video explains ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
7. Batch and Stream Processing
This video explains the batch and stream processing.
8. Scaling Up and Out
This video explains about scaling up and out.
3. Database Technologies - What, Where, and Why?

1. Overview
This video provides an overview of the section.
2. Traditional Relational Database (SQL)
This video focuses on a traditional relational database.
3. Non-relational Database (NoSQL)
This video explains about the non-relational database.
4. Distributed SQL Database (NewSQL)
This video helps to understand about the distributed SQL database.
5. In-memory Database (IMDB)
This video talks about in-memory database.
6. Time Series Database (TSDB)
This video helps you understand the time series database.
4. Types of NoSQL Databases

1. Overview
This video provides an overview of the section.
2. #1 - Key-value Database
This video demonstrates the key-value database.
3. #2 - Document Database
This video talks about the document database.
4. #3 - Wide Column Database
This video talks about the wide column database.
5. #4 - Graph Database
This video talks about the graph database.
6. Databases Ranking Review
This video explains the databases ranking review.
5. Database as a Service (DBaaS)

1. Overview
This video provides an overview of the section.
2. The Challenges of Traditional Databases
This video demonstrates the challenges of traditional databases.
3. The Concept of DBaaS
This video explains the concept of DBaaS.
4. The Advantages of DBaaS
This video shows the advantages of DBaaS.
5. Azure - DBaaS Portfolio Review
This video shows the DBaaS portfolio review of Azure platform.
6. AWS - DBaaS Portfolio Review
This video shows the DBaaS portfolio review of AWS platform.
7. GCP - DBaaS Portfolio Review
This video shows the DBaaS portfolio review of GCP platform.
6. Summary

1. Quick Recap and Thank You!
This video helps with the recap of the concepts learned so far.
7. Getting Started

1. Welcome
This video provides an introduction to this part.
2. Our Learning Objectives
This video provides an extension and focus areas about this course in detail.
8. Cloud Computing

1. Welcome to the World of Cloud Computing
This video provides an introduction to the cloud computing concepts.
2. Private vs Public
This video explains the difference between private and public cloud computing.
3. Hybrid-cloud and Multi-cloud
This video explains the hybrid-cloud and multi-cloud.
4. Hyperscale Cloud Service Providers
This video explains the hyperscale cloud service providers.
5. Cloud Service Models
This video explains the cloud service models.
6. SaaS - Software as a Service
This video talks about SaaS - Software as a Service.
7. IaaS - Infrastructure as a Service
This video talks about IaaS - Infrastructure as a Service.
8. PaaS - Platform as a Service
This video talks about PaaS - Platform as a Service.
9. FaaS - Function as a Service
This video talks about FaaS - Function as a Service.
10. Demo - AWS, Azure, GCP
This video provides demo on the various cloud service provides - AWS, Azure, and GCP.
9. Virtualization Technologies

1. Introduction
This video provides an introduction to the section.
2. Underutilized Physical Servers
This video talks about underutilized physical servers.
3. Virtualization with Virtual Machines
This video demonstrates how to do virtualization with virtual machines.
4. Vertical and Horizontal Scaling
This video explains the vertical and horizontal scaling.
5. Microservices and Cloud-Native Apps
This video focuses on microservices and cloud-native apps.
6. Virtualization with Containers
This video demonstrates how to do virtualization with containers.
7. The Benefits of Containers
This video demonstrates the benefits of containers.
10. Introduction to Infrastructure as a Service (IaaS)

1. Introduction and Overview
This video provides an overview and introduction to the section.
2. IaaS - Transform IT to Utility
This video explains how to transform IT to utility in IaaS.
3. Compute, Storage and Networking
This video focuses on compute, storage and networking.
4. Demo - IaaS Solution with Microsoft Azure
This video provides a demo on IaaS solution with Microsoft Azure.
5. Pricing Models
This video talks about the pricing models in Microsoft Azure.
6. Main Advantages
This video focuses on the advantages of IaaS.
7. Disadvantages
This video tells the disadvantages of IaaS.
8. Typical Market Use Cases
This video explains the typical market use cases.
11. Course Summary

1. Let's Recap and Thank You!
This video helps with a recap of the concepts learned so far.

Cloud Computing

This course focuses on the beginner-level concepts of cloud computing in two different arenas.

The first part is to explore the world of database technologies or DBaaS (Database as a Service) and the second part revolves around IaaS (Infrastructure as a Service) model.

Cloud Computing for Beginners - Database Technologies and Infrastructure as a Service

<strong>Delve into big data streaming with Azure using Event Hubs, Data Lake, and Azure Stream Analytics</strong>
        <p>A modern digital company captures a large amount of data every day. This data gets locked in data islands such as caches, queues, and logs, and extracting this data and making it useful poses a major challenge. Real-time data processing is the most effective alternative to traditional extract, transform, and load (ETL) processes. This course will help you to think of data as an ever-flowing stream of events instead of data as islands locked away in databases.

This course employs live labs to get you up to speed with Azure Event Hubs and teach you how to create C# console applications for sending and receiving data from Event Hubs. As you advance, you'll learn how to capture and archive data from Azure Event Hubs to Azure Data Lake. The subsequent videos will show you how to provision Event Hubs, Data Lake, and SQL Server databases in Azure. Toward the end of the course, you'll write an analytics job to stream live data from an event hub to a SQL Server database.

By the end of this course, you'll have developed a solid understanding of how to stream big data using Azure Stream Analytics.

All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Real-Time-Data-Stream-Processing-in-Azure</p>
        <h2>What You Will Learn</h2><p>Gain a thorough understanding of Azure Stream Analytics<br>Discover how to ingest data using Azure Event Hubs<br>Find out how to archive data from Event Hubs to Azure Data Lake<br>Provision Event Hubs, Data Lake, and SQL Server in Azure<br>Listen to an event hub and stream the data to a SQL Server table<br>Communicate with event hubs for effortless data transfer<br>Discover how event hub groups work</p>
        <h2>Audience</h2><p>This course is for Azure developers and big data engineers looking to change their approach to data processing. To take this course, you should have an Azure portal account. A free Azure trial account will also work.</p>
        <h2>Approach</h2><p>This course makes use of live labs and engaging videos to offer an interesting learning experience.</p>
        <h2>Key Features</h2><p>Learn to think of data as an ever-flowing stream of events * Discover how to harness the power of data events using Azure Stream Analytics * Get up to speed with Azure Event Hubs</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/real-time-data-stream-processing-in-azure</p>
        <h2>About the Author</h2><h3>Hersh Bhasin</h3><p>Hersh Bhasin is a published author and work as a Cloud Architect with J.P. Morgan Chase where he is part of the Enterprise Cloud Solutions Architecture team responsible for creating cloud architectural blueprints, guidance, Infrastructure as Code and best practices for fostering cloud adoption across the three public cloud providers (AWS, Azure and Google).</p>
        <h2>Course Outline</h2><h4>1. course-intro</h4><p></p><br><h4>2. understanding-eventhubs</h4><p></p><br><h4>3. Lab- Provisioning An Event Hub</h4><p></p><br><h4>4. 	Lab -Sending Events to an Event Hub</h4><p></p><br><h4>5. Lab-Receiving Events from an Event Hub</h4><p></p><br><h4>6. 	Lab-Event Hub Consumer Groups</h4><p></p><br><h4>7. 	Lab-Capturing Event Data to a Data Lake</h4><p></p><br><h4>8. 	Understanding Azure Stream Analytics</h4><p></p><br><h4>9. Lab-streaming</h4><p></p>

Delve into big data streaming with Azure using Event Hubs, Data Lake, and Azure Stream Analytics
A modern digital company captures a large amount of data every day. This data gets locked in data islands such as caches, queues, and logs, and extracting this data and making it useful poses a major challenge. Real-time data processing is the most effective alternative to traditional extract, transform, and load (ETL) processes. This course will help you to think of data as an ever-flowing stream of events instead of data as islands locked away in databases. This course employs live labs to get you up to speed with Azure Event Hubs and teach you how to create C# console applications for sending and receiving data from Event Hubs. As you advance, you'll learn how to capture and archive data from Azure Event Hubs to Azure Data Lake. The subsequent videos will show you how to provision Event Hubs, Data Lake, and SQL Server databases in Azure. Toward the end of the course, you'll write an analytics job to stream live data from an event hub to a SQL Server database. By the end of this course, you'll have developed a solid understanding of how to stream big data using Azure Stream Analytics. All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Real-Time-Data-Stream-Processing-in-Azure
What You Will Learn
Gain a thorough understanding of Azure Stream Analytics
Discover how to ingest data using Azure Event Hubs
Find out how to archive data from Event Hubs to Azure Data Lake
Provision Event Hubs, Data Lake, and SQL Server in Azure
Listen to an event hub and stream the data to a SQL Server table
Communicate with event hubs for effortless data transfer
Discover how event hub groups work
Audience
This course is for Azure developers and big data engineers looking to change their approach to data processing. To take this course, you should have an Azure portal account. A free Azure trial account will also work.
Approach
This course makes use of live labs and engaging videos to offer an interesting learning experience.
Key Features
Learn to think of data as an ever-flowing stream of events * Discover how to harness the power of data events using Azure Stream Analytics * Get up to speed with Azure Event Hubs
Github Repo
https://github.com/packtpublishing/real-time-data-stream-processing-in-azure
About the Author
Hersh Bhasin
Hersh Bhasin is a published author and work as a Cloud Architect with J.P. Morgan Chase where he is part of the Enterprise Cloud Solutions Architecture team responsible for creating cloud architectural blueprints, guidance, Infrastructure as Code and best practices for fostering cloud adoption across the three public cloud providers (AWS, Azure and Google).
Course Outline
1. course-intro

2. understanding-eventhubs

3. Lab- Provisioning An Event Hub

4. Lab -Sending Events to an Event Hub

5. Lab-Receiving Events from an Event Hub

6. Lab-Event Hub Consumer Groups

7. Lab-Capturing Event Data to a Data Lake

8. Understanding Azure Stream Analytics

9. Lab-streaming


Delve into big data streaming with Azure using Event Hubs, Data Lake, and Azure Stream Analytics

Real-Time Data Stream Processing in Azure

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				<div>
								<div>
					<p><span>Our <strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></span><span> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Career Path</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</span></p><ul><li><span>Open doors of opportunities</span></li><li><span>Increase your adaptability</span></li><li><span>Keep you relevant</span></li><li><span>Boost confidence</span></li></ul><p><span>And much more!</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
						</div>
						</div>
					</div> <h3>Course Curriculum</h3><div><p>7 sections • 25 lectures • 05:04:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python Installation: 00:03:00</p></div><div><div><p>•Installing Third Party Libraries: 00:03:00</p></div><div><div><p>•Installing Apache Spark: 00:12:00</p></div><div><div><p>•Installing Java (Optional): 00:05:00</p></div><div><div><p>•Testing Apache Spark Installation: 00:06:00</p></div><div><div><p>•Installing MongoDB: 00:04:00</p></div><div><div><p>•Installing NoSQL Booster for MongoDB: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Integrating PySpark with Jupyter Notebook: 00:05:00</p></div><div><div><p>•Data Extraction: 00:19:00</p></div><div><div><p>•Data Transformation: 00:15:00</p></div><div><div><p>•Loading Data into MongoDB: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Pre-processing: 00:19:00</p></div><div><div><p>•Building the Predictive Model: 00:12:00</p></div><div><div><p>•Creating the Prediction Dataset: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Loading the Data Sources from MongoDB: 00:17:00</p></div><div><div><p>•Creating a Map Plot: 00:33:00</p></div><div><div><p>•Creating a Bar Chart: 00:09:00</p></div><div><div><p>•Creating a Magnitude Plot: 00:15:00</p></div><div><div><p>•Creating a Grid Plot: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing Visual Studio Code: 00:05:00</p></div><div><div><p>•Creating the PySpark ETL Script: 00:24:00</p></div><div><div><p>•Creating the Machine Learning Script: 00:30:00</p></div><div><div><p>•Creating the Dashboard Server: 00:21:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Source Code and Notebook: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Overview

This comprehensive course on Building Big Data Pipelines with PySpark MongoDB and Bokeh will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This Building Big Data Pipelines with PySpark MongoDB and Bokeh comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. 
So enrol in this course today to fast-track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
There is no experience or previous qualifications required for enrolment on this Building Big Data Pipelines with PySpark MongoDB and Bokeh. It is available to all students, of all academic backgrounds.
Requirements
Our Building Big Data Pipelines with PySpark MongoDB and Bokeh is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career Path
Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-
Open doors of opportunities
Increase your adaptability
Keep you relevant
Boost confidence
And much more!
Course Curriculum
7 sections • 25 lectures • 05:04:00 total length
•Introduction: 00:10:00
•Python Installation: 00:03:00
•Installing Third Party Libraries: 00:03:00
•Installing Apache Spark: 00:12:00
•Installing Java (Optional): 00:05:00
•Testing Apache Spark Installation: 00:06:00
•Installing MongoDB: 00:04:00
•Installing NoSQL Booster for MongoDB: 00:07:00
•Integrating PySpark with Jupyter Notebook: 00:05:00
•Data Extraction: 00:19:00
•Data Transformation: 00:15:00
•Loading Data into MongoDB: 00:13:00
•Data Pre-processing: 00:19:00
•Building the Predictive Model: 00:12:00
•Creating the Prediction Dataset: 00:08:00
•Loading the Data Sources from MongoDB: 00:17:00
•Creating a Map Plot: 00:33:00
•Creating a Bar Chart: 00:09:00
•Creating a Magnitude Plot: 00:15:00
•Creating a Grid Plot: 00:09:00
•Installing Visual Studio Code: 00:05:00
•Creating the PySpark ETL Script: 00:24:00
•Creating the Machine Learning Script: 00:30:00
•Creating the Dashboard Server: 00:21:00
•Source Code and Notebook: 00:00:00

Apex Learning

Online Course Marketing

Online Course Creation

Data Engineering

Professional Skills Course (PSC)

Data Skills

Building Big Data Pipelines with PySpark MongoDB and Bokeh

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						</div>
					</div>
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		</section>
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				</div>
			<p><span>Business and Data Analysis with SQL is a comprehensive training in the theoretical and practical study of Data analysis and SQ language. This module is open to the people who want to elevate their business growth. Secure your position as an asset to the organization by learning SQL. Make your professional skill more productive by learning data analysis and SQL communication. The course exposes to you the details on computational and data investigational methods. It also highlights the basics of extracting data from the database storage. On completion of this online course, you will have an extensive understanding of how the database works and ways to function them properly, and thus be able to make critical decisions like a professional.</span></p>
<h3><strong>What you'll learn</strong></h3>
<ul>
<li><span>At the end of this course, you will have the ability to run your own data projects, work with databases and manage large datasets and provide meaningful business intelligence reports to your team.</span></li>
</ul>
<h3><strong>Requirements</strong></h3>
<div>
<ul>
<li>Be familiar with reports and data-based projects.</li>
</ul>
<h3><strong>Who this course is for:</strong></h3>
<ul>
<li>This course is aimed at business professionals who work with data of all sizes. You will learn the fundamentals of business intelligence and basic data warehouse development.</li>
</ul>
</div><table>				<tbody><tr>
					<td colspan="4">Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Business and Data Analysis with SQL</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Database and Datawarehouse Design</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Databases- A History</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>SQL Statement Categories</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Capture data requirements</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Physical Vs Logical Data Representations</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Normalise data</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Fact Tables</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Aggregate data</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Entity Relationship Diagrams (ERD's)</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Extract, Transform and Load (ETL)</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Structured Query Language (SQL) - Fundamentals</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>MySQL Server Setup</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>MySQL Server Start and stop Service</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Connect to database</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Comments in SQL</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Select data from a table</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Table Indexes</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Table and Column Aliases</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Inner Join Tables</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Left and Right Outer Table Joins</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Union (all) Results</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Project Management</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Project Management (Agile Methodology)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Kanban boards</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Prioritise Requirements using MoSCoW</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Sales Project</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sales Project - Introduction</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create and Drop Database</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Query Workflow</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sales Project - Part 1 - Data Capture</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sales Project - Part 2 - Data Analysis</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sales Project - Part 3 - Data Summary</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sales Project - Part 4 - Send to production</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>MySQL Query Explain Plan</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Common Table Expressions (WITH)</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Control Language (DCL)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>User Administration</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">MySQL Programs</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>MySQL Set Windows Environment Variable</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>MySQL Command Line</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Visualising Data</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Export results to Excel</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup MySQL ODBC (Open Database Connector)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating a report in Microsoft Excel</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Course Certification </td>
				</tr>
				</tbody></table>

Business and Data Analysis with SQL is a comprehensive training in the theoretical and practical study of Data analysis and SQ language. This module is open to the people who want to elevate their business growth. Secure your position as an asset to the organization by learning SQL. Make your professional skill more productive by learning data analysis and SQL communication. The course exposes to you the details on computational and data investigational methods. It also highlights the basics of extracting data from the database storage. On completion of this online course, you will have an extensive understanding of how the database works and ways to function them properly, and thus be able to make critical decisions like a professional.
What you'll learn
At the end of this course, you will have the ability to run your own data projects, work with databases and manage large datasets and provide meaningful business intelligence reports to your team.
Requirements
Be familiar with reports and data-based projects.
Who this course is for:
This course is aimed at business professionals who work with data of all sizes. You will learn the fundamentals of business intelligence and basic data warehouse development.
Introduction			
	Business and Data Analysis with SQL	FREE	00:06:00
Database and Datawarehouse Design			
	Introduction to Databases- A History		00:04:00
	SQL Statement Categories		00:02:00
	Capture data requirements		00:09:00
	Physical Vs Logical Data Representations		00:03:00
	Normalise data		00:07:00
	Fact Tables		00:04:00
	Aggregate data		00:07:00
	Entity Relationship Diagrams (ERD's)		00:07:00
	Extract, Transform and Load (ETL)		00:13:00
Structured Query Language (SQL) - Fundamentals			
	MySQL Server Setup		00:05:00
	MySQL Server Start and stop Service		00:02:00
	Connect to database		00:07:00
	Comments in SQL		00:05:00
	Select data from a table		00:16:00
	Table Indexes		00:08:00
	Table and Column Aliases		00:05:00
	Inner Join Tables		00:02:00
	Left and Right Outer Table Joins		00:04:00
	Union (all) Results		00:04:00
Project Management			
	Project Management (Agile Methodology)		00:11:00
	Kanban boards		00:09:00
	Prioritise Requirements using MoSCoW		00:04:00
Sales Project			
	Sales Project - Introduction		00:05:00
	Create and Drop Database		00:05:00
	Query Workflow		00:09:00
	Sales Project - Part 1 - Data Capture		00:15:00
	Sales Project - Part 2 - Data Analysis		00:08:00
	Sales Project - Part 3 - Data Summary		00:12:00
	Sales Project - Part 4 - Send to production		00:09:00
	MySQL Query Explain Plan		00:07:00
	Common Table Expressions (WITH)		00:14:00
Data Control Language (DCL)			
	User Administration		00:13:00
MySQL Programs			
	MySQL Set Windows Environment Variable		00:05:00
	MySQL Command Line		00:17:00
Visualising Data			
	Export results to Excel		00:06:00
	Setup MySQL ODBC (Open Database Connector)		00:11:00
	Creating a report in Microsoft Excel		00:21:00
Course Certification			

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