BDASARAWAmazon Web ServicesAW-BDASAR1.0<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 data warehouse 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 data warehouses 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-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> or <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></li><li>Completed <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></li></ul><p>This course is intended for:</p> <ul> <li>Data warehouse engineers</li><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: Using Amazon Redshift in the Data Analytics Pipeline</h5><ul> <li>Why Amazon Redshift for data warehousing?</li><li>Overview of Amazon Redshift</li></ul><h5>Module 2: Introduction to Amazon Redshift</h5><ul> <li>Amazon Redshift architecture</li><li>Interactive Demo 1: Touring the Amazon Redshift console</li><li>Amazon Redshift features</li><li>Practice Lab 1: Setting up your data warehouse using Amazon Redshift</li></ul><h5>Module 3: Ingestion and Storage</h5><ul> <li>Ingestion</li><li>Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API</li><li>Data distribution and storage</li><li>Interactive Demo 3: Analyzing semi-structured data using the SUPER data type</li><li>Querying data in Amazon Redshift</li><li>Practice Lab 2: Data analytics using Amazon Redshift Spectrum</li></ul><h5>Module 4: Processing and Optimizing Data</h5><ul> <li>Data transformation</li><li>Advanced querying</li><li>Practice Lab 3: Data transformation and querying in Amazon Redshift</li><li>Resource management</li><li>Interactive Demo 4: Applying mixed workload management on Amazon Redshift</li><li>Automation and optimization</li><li>Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster</li></ul><h5>Module 5: Security and Monitoring of Amazon Redshift Clusters</h5><ul> <li>Securing the Amazon Redshift cluster</li><li>Monitoring and troubleshooting Amazon Redshift clusters</li></ul><h5>Module 6: Designing Data Warehouse Analytics Solutions</h5><ul> <li>Data warehouse use case review</li><li>Activity: Designing a data warehouse 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 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 practicesStudents with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have: - Completed either AWS Technical Essentials (AWSE) or Architecting on AWS (AWSA) - Completed Building Data Lakes on AWS (BDLA)This course is intended for: - Data warehouse engineers - 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: 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: Setting up your data warehouse using Amazon Redshift 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 architectures1 jour795.00795.00750.00795.00725.002000.00675.00650.00795.00795.00795.00795.00795.00900.00870.00930.00