10761 Mac courses

Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for This course is introductory-level, but technical in nature. In order to participate in the hands-pon labs you should have a basic understanding of database principles, basic scripting skills (in relation to Oracle) and basic analytics skills. Overview This course is approximately 50% hands-on lab to lecture ratio, combining engaging expert lessons, demos and group discussions with real-world, skills-focused machine-based labs and exercises. Working in a hands-on learning environment led by our expert facilitator, you'll explore: Core concepts of regular PDBs Creating a CDB, and then using different methods to create PDBs. How to start and shut down a CDB and how to open and close a PDB. Security aspects in CDBs and PDBs in various areas like privileges and roles, lockdown profiles, auditing, Database Vault, and encryption. Availability through backup, duplicate, recovery, and flashback topics and then performance, monitoring, and resources allocation management in CDBs and PDBs. How you can move data from a non-CDB environment to a PDB. How to move data between PDBs by using utilities such as the export and import features of Oracle Data Pump, SQL*Loader, external tables, and Oracle Recovery Manager. The multitenant architecture enables you to have many pluggable databases inside a single Oracle Database instance. Oracle Database 19c Multitenant Architecture is a three-day hands-on course that explores the multitenant architecture and the different types of pluggable databases (PDBs) in multitenant container databases (CDBs). Multitenant Architecture CDB Basics CDB and Regular PDBs Application PDBs and Application Installation PDB Creation PDB Creation CDB and PDB Management Storage Security Security Backup and Duplicate Recovery and Flashback Performance Resources Allocation Data Movement Data Movement Upgrade Methods Miscellaneous Additional course details: Nexus Humans Oracle 19c Database Multitenant Architecture (TTOR20719) 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 Oracle 19c Database Multitenant Architecture (TTOR20719) 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.

Duration 3 Days 18 CPD hours This course is intended for This course is introductory-level, but technical in nature. In order to participate in the hands-pon labs you should have a basic understanding of database principles, basic scripting skills (in relation to Oracle) and basic analytics skills. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: Core concepts of regular PDBs Creating a CDB, and then using different methods to create PDBs. How to start and shut down a CDB and how to open and close a PDB. Security aspects in CDBs and PDBs in various areas like privileges and roles, lockdown profiles, auditing, Database Vault, and encryption. Availability through backup, duplicate, recovery, and flashback topics and then performance, monitoring, and resources allocation management in CDBs and PDBs. How you can move data from a non-CDB environment to a PDB. How to move data between PDBs by using utilities such as the export and import features of Oracle Data Pump, SQL*Loader, external tables, and Oracle Recovery Manager. The multitenant architecture enables you to have many pluggable databases inside a single Oracle Database instance. Oracle Database 19c Multitenant Architecture is a three-day hands on course that explores the multitenant architecture and the different types of pluggable databases (PDBs) in multitenant container databases (CDBs). Multitenant Architecture CDB Basics CDB and Regular PDBs Application PDBs and Application Installation PDB Creation PDB Creation CDB and PDB Management Storage Security Security Backup and Duplicate Recovery and Flashback Performance Resources Allocation Data Movement Data Movement Upgrade Methods Miscellaneous Additional course details: Nexus Humans Oracle Database 19c Multitenant Architecture (TTOR20719) 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 Oracle Database 19c Multitenant Architecture (TTOR20719) 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.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it's often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for students wishing to gain intermediate-level skills or individuals whose job responsibilities include constructing relational databases and developing tables, queries, forms, and reports in Microsoft Access for Microsoft 365. Overview In this course, you will optimize an Access database. You will: Provide input validation features to promote the entry of quality data into a database. Organize a database for efficiency and performance, and to maintain data integrity. Improve the usability of Access tables. Create advanced queries to join and summarize data. Use advanced formatting and controls to improve form presentation. Use advanced formatting and calculated fields to improve reports. Your training and experience using Microsoft© Access© has given you basic database management skills, such as creating tables, designing forms and reports, and building queries. In this course, you will expand your knowledge of relational database design; promote quality input from users; improve database efficiency and promote data integrity; and implement advanced features in tables, queries, forms, and reports. Extending your knowledge of Access will result in a robust, functional database for your users.This course is the second part of a three-course series that covers the skills needed to perform database design and development in Access.Microsoft© Access© for Office 365?: Part 1 : Focuses on the design and construction of an Access database?viewing, navigating, searching, and entering data in a database, as well as basic relational database design and creating simple tables, queries, forms, and reports.Microsoft© Access© for Office 365?: Part 2 (this course): Focuses on optimization of an Access database, including optimizing performance and normalizing data; data validation; usability; and advanced queries, forms, and reports.Microsoft© Access© for Office 365?: Part 3 : Focuses on managing the database and supporting complex database designs, including import and export of data; using action queries to manage data; creating complex forms and reports; macros and Visual Basic for Applications (VBA); and tools and strategies to manage, distribute, and secure a database.This course may be a useful component in your preparation for the Microsoft Access Expert (Microsoft 365 Apps and Office 2019): Exam MO-500 certification exam. Lesson 1: Promoting Quality Data Input Topic A: Restrict Data Input Through Field Validation Topic B: Restrict Data Input Through Forms and Record Validation Lesson 2: Improving Efficiency and Data Integrity Topic A: Data Normalization Topic B: Associate Unrelated Tables Topic C: Enforce Referential Integrity Lesson 3: Improving Table Usability Topic A: Create Lookups Within a Table Topic B: Work with Subdatasheets Lesson 4: Creating Advanced Queries Topic A: Create Query Joins Topic B: Create Subqueries Topic C: Summarize Data Lesson 5: Improving Form Presentation Topic A: Apply Conditional Formatting Topic B: Create Tab Pages with Subforms and Other Controls Lesson 6: Creating Advanced Reports Topic A: Apply Advanced Formatting to a Report Topic B: Add a Calculated Field to a Report Topic C: Control Pagination and Print Quality Topic D: Add a Chart to a Report

Duration 2 Days 12 CPD hours This course is intended for This course is designed for network and software engineers who hold the following job roles: Network administrators Network operators Overview After taking this course, you should be able to: Explain the benefits of using Cisco DNA Center in a traditional, enterprise network Explain at a detailed level the Cisco DNA Center Assurance system architecture, functional components, features, and data-processing concepts Explain the health scores, metrics, and strategies that you use for monitoring network devices, clients, and applications with Cisco DNA Assurance Describe how Cisco DNA Center Assurance analyzes the streaming telemetry and collected data, correlates the data, performs root cause analysis, and displays detected issues, insights, and trends Describe the Cisco DNA Center Assurance troubleshooting tools, mechanisms, strategies, and scenarios to proactively detect and resolve wireless network, client, and application issues and pinpoint the root cause Deploy and configure Cisco DNA Center to use Assurance features for monitoring and troubleshooting network devices, clients, and applications The Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 course provides you with the skills to monitor and troubleshoot a traditional brownfield network infrastructure by using Cisco© Digital Network Architecture (Cisco DNA?) Assurance. The course focuses on highlighting issues rather than on monitoring data. The advanced artificial intelligence and machine learning features within Cisco DNA Assurance enable you to isolate the root cause of a problem and to take appropriate actions to quickly resolve issues. Cisco DNA Assurance can be used to perform the work of a Level 3 support engineer. Course Outline Introducing Cisco DNA Center Assurance Monitoring Health and Performance with Cisco DNA Center Assurance Troubleshooting Issues, Observing Insights and Trends Troubleshooting Wireless Issues with Cisco DNA Center Assurance Tools Additional course details: Nexus Humans Cisco Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 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 Cisco Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 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.

Duration 62.5 Days 375 CPD hours Cisco Learning Library: Data Center offers a subscription to Cisco online data center training, including certifications, products, and technologies.This comprehensive technical training library offers full-length, interactive certification courses, additional product and technology training with labs, and thousands of reference materials Data Center Library Certification Courses CCNP Data Center Implementing and Operating Cisco Data Center Core Technologies (DCCOR) v1.0 Designing Cisco Data Center Infrastructure (DCID) v7.0 Troubleshooting Cisco Data Center Infrastructure (DCIT) v7.0 Implementing Cisco Application Centric Infrastructure (DCACI) v1.0 Configuring Cisco MDS 9000 Series Switches (DCMDS) Implementing Automation for Cisco Data Center Solutions (DCAUI) CCIE Data Center Implementing and Operating Cisco Data Center Core Technologies (DCCOR) v1.0 Product and Technology Training Implementing and Operating Cisco Data Center Core Technologies (DCCOR) v1.0 Understanding Cisco Data Center Foundations (DCFNDU) v1.0 Implementing Cisco Application Centric Infrastructure (DCACI) v1.0 Introducing Cisco NX-OS Switches and Fabrics in the Data Center (DCINX) v1.0 Configuring Cisco NX-OS Switches and Fabrics in the Data Center (DCCNX) v1.0 Designing Cisco Data Center Infrastructure (DCID) v7.0 Troubleshooting Cisco Data Center Infrastructure (DCIT) v7.0 Implementing Cisco Application Centric Infrastructure?Advanced (DCACIA) v1.0 Introducing Cisco Unified Computing System (DCIUCS) v1.0 Configuring Cisco Unified Computing System (DCCUCS) v1.0 Implementing Cisco Tetration Analytics (DCITET) v1.0 Cisco MDS 9000 Series Switches Overview (DCMDSO) v1.0 Configuring Cisco Nexus 9000 in NX-OS Mode (C9KNX) v1.2 Configuring Cisco Nexus 9000 Series Switches in ACI Mode (DCAC9K) v3.0 Introducing Cisco Nexus 9000 Switches in NX-OS Mode (DCINX9K) v2.1 Configuring VXLANs on Cisco Nexus 9000 Series Switches (DCVX9K) v1.0 Cisco Application Centric Infrastructure Operations and Troubleshooting (DCACIO) v4.1 Configuring Cisco MDS 9000 Series Switches (DCMDS) v3.1 Introducing Cisco MDS 9000 Series Switches (DCIMDS) v1.0 Implementing Automation for Cisco Data Center Solutions (DCAUI) v1.0 Introducing Automation for Cisco Solutions (CSAU) v1.0 Implementing Cisco HyperFlex (DCIHX) v1.2 Configuring the Cisco Nexus Data Center Transport (CCNDC-T) v2.0 Configuring the Cisco Nexus Data Center Virtual Machines (CCNDC-V) v2.0 Additional course details: Nexus Humans Cisco Digital Learning Data Center 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 Cisco Digital Learning Data Center 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.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers System Administrators Solutions Architects Overview This course is designed to teach you how to: Design a microservices-based architecture that uses containers Use Amazon ECS to run and scale a microservices-based application Integrate Amazon ECS with other AWS services Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes. Module 1a: Overview of Microservices on AWS Welcome to Simple Mustache Service! The monolith What are microservices? How to implement a microservices infrastructure The six principles of microservices Module 1b: Containers and Docker Introduction to containers Comparing virtual machines with containers Docker Running containers Storing container images Hands-on lab: Building and running your first container Module 2: Continuous delivery for container-based microservices Compare and contrast different software development cycles Use AWS CodePipeline to code, build, and deploy a microservice Use AWS CodeCommit as a source control service Use Jenkins to perform a Docker build Use Postman to run and test microservices Use AWS CloudFormation to provision and deploy microservices Hands-on lab: Using the Amazon ECS Service Scheduler Module 3: High availability and scaling with Amazon Elastic Container Service High availability Cluster management and scheduling Monitoring Scaling a cluster Scaling services Hands-on lab: Continuous delivery pipelines for container-based microservices Module 4: Security for container-based microservices Implement security Apply best practices Automate security Evaluate compliance requirements Embed security into the CI/CD Hands-on lab: Extending Amazon ECS with Service Discovery and Config Management Additional course details: Nexus Humans Running Container Enabled Microservices on AWS 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 Running Container Enabled Microservices on AWS 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.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures