36 ETL courses

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

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Duration 4 Days 24 CPD hours This course is intended for This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Hadoop Fundamentals The Motivation for Hadoop Hadoop Overview Data Storage: HDFS Distributed Data Processing: YARN, MapReduce, and Spark Data Processing and Analysis: Pig, Hive, and Impala Data Integration: Sqoop Other Hadoop Data Tools Exercise Scenarios Explanation Introduction to Pig What Is Pig? Pig?s Features Pig Use Cases Interacting with Pig Basic Data Analysis with Pig Pig Latin Syntax Loading Data Simple Data Types Field Definitions Data Output Viewing the Schema Filtering and Sorting Data Commonly-Used Functions Processing Complex Data with Pig Storage Formats Complex/Nested Data Types Grouping Built-In Functions for Complex Data Iterating Grouped Data Multi-Dataset Operations with Pig Techniques for Combining Data Sets Joining Data Sets in Pig Set Operations Splitting Data Sets Pig Troubleshoot & Optimization Troubleshooting Pig Logging Using Hadoop?s Web UI Data Sampling and Debugging Performance Overview Understanding the Execution Plan Tips for Improving the Performance of Your Pig Jobs Introduction to Hive & Impala What Is Hive? What Is Impala? Schema and Data Storage Comparing Hive to Traditional Databases Hive Use Cases Querying with Hive & Impala Databases and Tables Basic Hive and Impala Query Language Syntax Data Types Differences Between Hive and Impala Query Syntax Using Hue to Execute Queries Using the Impala Shell Data Management Data Storage Creating Databases and Tables Loading Data Altering Databases and Tables Simplifying Queries with Views Storing Query Results Data Storage & Performance Partitioning Tables Choosing a File Format Managing Metadata Controlling Access to Data Relational Data Analysis with Hive & Impala Joining Datasets Common Built-In Functions Aggregation and Windowing Working with Impala How Impala Executes Queries Extending Impala with User-Defined Functions Improving Impala Performance Analyzing Text and Complex Data with Hive Complex Values in Hive Using Regular Expressions in Hive Sentiment Analysis and N-Grams Conclusion Hive Optimization Understanding Query Performance Controlling Job Execution Plan Bucketing Indexing Data Extending Hive SerDes Data Transformation with Custom Scripts User-Defined Functions Parameterized Queries Choosing the Best Tool for the Job Comparing MapReduce, Pig, Hive, Impala, and Relational Databases Which to Choose?

This is a complete crash course about KNIME for beginners. Here, we will learn how to do data cleaning and data preparation without any code, using KNIME. We will also cover data visualization using Tableau and Power BI Desktop. Then we will understand the predictive analytics capabilities of KNIME and finally, cover machine learning in KNIME.

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents, and interests with our special Big Data Analytics with PySpark Power BI and MongoDB Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides the professional training that employers are looking for in today's workplaces. The Big Data Analytics with PySpark Power BI and MongoDB Course is one of the most prestigious training offered at Skillwise and is highly valued by employers for good reason. This Big Data Analytics with PySpark Power BI and MongoDB Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This Big Data Analytics with PySpark Power BI and MongoDB Course, like every one of Skillwise's courses, is meticulously developed and well-researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At Skillwise, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from Skillwise, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this Big Data Analytics with PySpark Power BI and MongoDB? Unlimited access to the course forever Digital Certificate, Transcript, and student ID are all included in the price Absolutely no hidden fees Directly receive CPD Quality Standard-accredited qualifications after course completion Receive one-to-one assistance every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Big Data Analytics with PySpark Power BI and MongoDB there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for free. Original Hard Copy certificates need to be ordered at an additional cost of £8. Who is this course for? This Big Data Analytics with PySpark Power BI and MongoDB course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skills. Prerequisites This Big Data Analytics with PySpark Power BI and MongoDB does not require you to have any prior qualifications or experience. You can just enroll and start learning. This Big Data Analytics with PySpark Power BI and MongoDB was made by professionals and it is compatible with all PCs, Macs, tablets, and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as a bonus, you will be able to pursue multiple occupations. This Big Data Analytics with PySpark Power BI and MongoDB is a great way for you to gain multiple skills from the comfort of your home.   Section 01: Introduction Introduction 00:10:00 Section 02: Setup and Installations Python Installation 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 Section 03: Data Processing with PySpark and MongoDB 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 Section 04: Machine Learning with PySpark and MLlib Data Pre-processing 00:19:00 Building the Predictive Model 00:12:00 Creating the Prediction Dataset 00:08:00 Section 05: Creating the Data Pipeline Scripts Installing Visual Studio Code 00:03:00 Creating the PySpark ETL Script 00:22:00 Creating the Machine Learning Script 00:24:00 Section 06: Tableau Data Visualization Installing Tableau 00:03:00 Installing MongoDB ODBC Drivers 00:03:00 Creating a System DSN for MongoDB 00:04:00 Loading the Data Sources 00:04:00 Creating a Geo Map 00:11:00 Creating a Bar Chart 00:03:00 Creating a Magnitude Chart 00:07:00 Creating a Table Plot 00:06:00 Creating a Dashboard 00:07:00 Source Code Source Code and Notebook

Duration 3 Days 18 CPD hours This course is intended for This class is designed for experienced administrators and platform developers as well as solution, enterprise, and technical architects responsible for designing or building integration solutions on the Salesforce Platform. It's also designed for project or program managers looking to understand the big picture of integration in order to better manage their projects and programs. Ideally, you hold the Salesforce Administrator, Salesforce App Builder, or Salesforce Platform Developer I credential and/or are knowledgeable about standard and custom objects; how to work with flows and Process Builder; how to navigate Salesforce, Lightning Components, and Schema Builder; and Apex basics such as creating a Class and Anonymous Blocks. This class is recommended for anyone looking to earn their Salesforce Certified Integration Designer credential. Overview When you complete this course, you will be able to: Identify integration requirements by asking relevant integration questions to business stakeholders. Recommend appropriate integration patterns (Remote Process Invocation - Request and Reply, Remote Process Invocation - Fire and Forget, Batch Data Synchronization, Remote Call-In, and UI Update Based on Data Changes, Data Virtualization) to design integration solutions. Recommend appropriate integration capabilities (SOAP, REST, bulk, streaming, Outbound Messaging, Callouts, and Salesforce Connect) to build integration solutions. Explore architectural concepts and the big picture thinking of how to integrate on the Salesforce Platform. In this 3-day class, our Salesforce integration experts will demonstrate how to build solutions that provide a single view of your customers. You?ll learn about integration capabilities and the considerations that should be made when architecting an integration solution. Through a combination of demos, discussions, and hands-on exercises, you?ll design and build secure, scalable, and high-performing integration solutions that seamlessly surface the information that matters most Integration Overview Understand the Importance of Integration Explore Integration Architecture Explore the Meaning of Single View of the Customer Explore the Meaning of System Landscape Understand the Role of Enterprise Service Bus (ESB) and Extract Transform Load (ETL) in System Landscape Integration Capabilities Introduction to Salesforce APIs Create External Applications Using SOAP API Build Mobile Applications Using REST API Work with Large Data Volumes Using BULK API Build Event-Driven Applications Using Streaming API Build Event-Driven Applications Using Platform Events Build Event-Driven Applications Using Change Data Capture Send Data from Salesforce to External Applications Using Outbound Message Call External APIs Programmatically Using Callouts Integrate Data from an External Data Source Using Salesforce Connect Synchronize Data with Heroku Connect Call External APIs Declaratively Using External Services Integration Patterns Understand Use Cases for a Request and Reply Pattern Understand Use Cases for a Fire and Forget Pattern Understand Use Cases for a Batch Data Synchronization Pattern Understand Use Cases for a Remote Call-In Pattern Understand Use Cases for a UI Update Based on Data Changes Pattern Understand Use Cases for a Data Virtualization Pattern Integration Security Explore Two-Way Transport Layer Security (TLS) Understand When to Use Remote Site Settings Understand the Benefits of Using Named Credentials Understand Certificates Encrypt Your Data at Rest Using Salesforce Shield Platform Encryption Advanced Integration Discussions Design Solutions for Performance Design Solutions for Scale Review Integration Regulatory Requirements Review Integration Best Practices Case Study Draw a System Landscape Diagram Evaluate Business Needs Translate Needs to Integration Requirements Design an Integration Solution Build an Integration Solution Maintain an Integration Solution Additional course details: Nexus Humans Salesforce Design and Build Integration Solutions (ARC101) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Salesforce Design and Build Integration Solutions (ARC101) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Scala is doubtless one of the most in-demand skills for data scientists and data engineers. This competitive course will teach you the essential concepts and methodologies of Scala with a lot of practical implementations.

Unlock the power of data with our comprehensive course on Business and Data Analysis with SQL. Learn essential SQL skills to extract meaningful insights, make data-driven decisions, and drive business success. Whether you're a beginner or looking to enhance your expertise, our course empowers you with the tools and knowledge needed to excel in the dynamic world of data analysis. Enroll now to master SQL for business intelligence and elevate your analytical skills to new heights.

A beginner's level course that will help you learn data engineering techniques for building metadata-driven frameworks with Azure data engineering tools such as Data Factory, Azure SQL, and others. You need not have any prior experience in Azure Data Factory to take up this course.

The course helps you learn Snowflake from scratch and explore a few of its important features. You will build automated pipelines with Snowflake and use the AWS cloud with Snowflake as a data warehouse. You will also explore Snowpark to be worked on the data pipelines.