BBDASAWAmazon Web ServicesAW-BBDAS1.0<p>In this course, you will learn to:</p> <ul> <li>Compare the features and benefits of data warehouses, data lakes, and modern data architectures</li><li>Design and implement a batch data analytics solution</li><li>Identify and apply appropriate techniques, including compression, to optimize data storage</li><li>Select and deploy appropriate options to ingest, transform, and store data</li><li>Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case</li><li>Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights</li><li>Secure data at rest and in transit</li><li>Monitor analytics workloads to identify and remediate problems</li><li>Apply cost management best practices</li></ul><p>Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.</p> <p>We recommend that attendees of this course have: </p> <ul> <li>Completed either <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/amazon-awsa"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Architecting on AWS <span class="fl-prod-pcode">(AWSA)</span></a></span> or <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/amazon-awse"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>AWS Technical Essentials <span class="fl-prod-pcode">(AWSE)</span></a></span></li><li>Completed either <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/amazon-bdla"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Building Data Lakes on AWS <span class="fl-prod-pcode">(BDLA)</span></a></span> or Getting Started with AWS Glue</li></ul><p>This course is intended for:</p> <ul> <li>Data platform engineers</li><li>Architects and operators who build and manage data analytics pipelines</li></ul><h5>Module A: Overview of Data Analytics and the Data Pipeline</h5><ul> <li>Data analytics use cases</li><li>Using the data pipeline for analytics</li></ul><h5>Module 1: Introduction to Amazon EMR</h5><ul> <li>Using Amazon EMR in analytics solutions</li><li>Amazon EMR cluster architecture</li><li>Interactive Demo 1: Launching an Amazon EMR cluster</li><li>Cost management strategies</li></ul><h5>Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage</h5><ul> <li>Storage optimization with Amazon EMR</li><li>Data ingestion techniques</li></ul><h5>Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR</h5><ul> <li>Apache Spark on Amazon EMR use cases</li><li>Why Apache Spark on Amazon EMR</li><li>Spark concepts</li><li>Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell</li><li>Transformation, processing, and analytics</li><li>Using notebooks with Amazon EMR</li><li>Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR</li></ul><h5>Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive</h5><ul> <li>Using Amazon EMR with Hive to process batch data</li><li>Transformation, processing, and analytics</li><li>Practice Lab 2: Batch data processing using Amazon EMR with Hive</li><li>Introduction to Apache HBase on Amazon EMR</li></ul><h5>Module 5: Serverless Data Processing</h5><ul> <li>Serverless data processing, transformation, and analytics</li><li>Using AWS Glue with Amazon EMR workloads</li><li>Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions</li></ul><h5>Module 6: Security and Monitoring of Amazon EMR Clusters</h5><ul> <li>Securing EMR clusters</li><li>Interactive Demo 3: Client-side encryption with EMRFS</li><li>Monitoring and troubleshooting Amazon EMR clusters</li><li>Demo: Reviewing Apache Spark cluster history</li></ul><h5>Module 7: Designing Batch Data Analytics Solutions</h5><ul> <li>Batch data analytics use cases</li><li>Activity: Designing a batch data analytics workflow</li></ul><h5>Module B: Developing Modern Data Architectures on AWS</h5><ul> <li>Modern data architectures</li></ul>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 practicesStudents with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course. We recommend that attendees of this course have: - Completed either Architecting on AWS (AWSA) or AWS Technical Essentials (AWSE) - Completed either Building Data Lakes on AWS (BDLA) or Getting Started with AWS GlueThis course is intended for: - Data platform engineers - Architects and operators who build and manage data analytics pipelinesModule 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 architectures1 jour590.00795.00750.00750.00750.002000.00675.002610.00750.00795.00795.00795.00795.00795.00900.00870.00930.00850.00