Building Data Lakes on AWS

Building Data Lakes on AWSBDLAAWAmazon Web ServicesAW-BDLA1.0In this course, you will learn to: <ul> <li>Apply data lake methodologies in planning and designing a data lake</li><li>Articulate the components and services required for building an AWS data lake</li><li>Secure a data lake with appropriate permission</li><li>Ingest, store, and transform data in a data lake</li><li>Query, analyze, and visualize data within a data lake</li></ul>We recommend that attendees of this course have: <ul> <li>Completed the <a class="fl-href-prod" href="/swisscom/en/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 (AWSE)</a> classroom course</li><li>One year of experience building data analytics pipelines or have completed the <a class="cms-href-ext" href="https://fastlane.live/ww_en/e-learning/data-analytics-fundamentals-awsii-8421.html" data-cms-evt-click="Outbound Links;click;https://fastlane.live/ww_en/e-learning/data-analytics-fundamentals-awsii-8421.html">Data Analytics Fundamentals</a> digital course</li></ul>This course is intended for: <ul> <li>Data platform engineers</li><li>Solutions architects</li><li>IT professionals</li></ul>Module 1: Introduction to data lakes <ul> <li>Describe the value of data lakes</li><li>Compare data lakes and data warehouses</li><li>Describe the components of a data lake</li><li>Recognize common architectures built on data lakes</li></ul>Module 2: Data ingestion, cataloging, and preparation <ul> <li>Describe the relationship between data lake storage and data ingestion</li><li>Describe AWS Glue crawlers and how they are used to create a data catalog</li><li>Identify data formatting, partitioning, and compression for efficient storage and query</li><li>Lab 1: Set up a simple data lake</li></ul>Module 3: Data processing and analytics <ul> <li>Recognize how data processing applies to a data lake</li><li>Use AWS Glue to process data within a data lake</li><li>Describe how to use Amazon Athena to analyze data in a data lake</li></ul>Module 4: Building a data lake with AWS Lake Formation <ul> <li>Describe the features and benefits of AWS Lake Formation</li><li>Use AWS Lake Formation to create a data lake</li><li>Understand the AWS Lake Formation security model</li><li>Lab 2: Build a data lake using AWS Lake Formation</li></ul> Module 5: Additional Lake Formation configurations <ul> <li>Automate AWS Lake Formation using blueprints and workflows</li><li>Apply security and access controls to AWS Lake Formation</li><li>Match records with AWS Lake Formation FindMatches</li><li>Visualize data with Amazon QuickSight</li><li>Lab 3: Automate data lake creation using AWS Lake Formation blueprints</li><li>Lab 4: Data visualization using Amazon QuickSight</li></ul> <h5>Module 6: Architecture and course review</h5><ul> <li>Post course knowledge check</li><li>Architecture review</li><li>Course review</li></ul>In this course, you will learn to: - Apply data lake methodologies in planning and designing a data lake - Articulate the components and services required for building an AWS data lake - Secure a data lake with appropriate permission - Ingest, store, and transform data in a data lake - Query, analyze, and visualize data within a data lakeWe recommend that attendees of this course have: - Completed the AWS Technical Essentials (AWSE) classroom course - One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals (https://fastlane.live/ww_en/e-learning/data-analytics-fundamentals-awsii-8421.html) digital courseThis course is intended for: - Data platform engineers - Solutions architects - IT professionalsModule 1: Introduction to data lakes - Describe the value of data lakes - Compare data lakes and data warehouses - Describe the components of a data lake - Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation - Describe the relationship between data lake storage and data ingestion - Describe AWS Glue crawlers and how they are used to create a data catalog - Identify data formatting, partitioning, and compression for efficient storage and query - Lab 1: Set up a simple data lake Module 3: Data processing and analytics - Recognize how data processing applies to a data lake - Use AWS Glue to process data within a data lake - Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation - Describe the features and benefits of AWS Lake Formation - Use AWS Lake Formation to create a data lake - Understand the AWS Lake Formation security model - Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations - Automate AWS Lake Formation using blueprints and workflows - Apply security and access controls to AWS Lake Formation - Match records with AWS Lake Formation FindMatches - Visualize data with Amazon QuickSight - Lab 3: Automate data lake creation using AWS Lake Formation blueprints - Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review - Post course knowledge check - Architecture review - Course review1 day750.00750.00750.00750.00750.00695.00695.00590.002000.002610.00750.00750.00750.00900.00870.00675.00930.00