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    Building Data Lakes on AWS (BDLA)

    Cloud Computing - AWS
    In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures.

    Building Data Lakes on AWS (BDLA) Objectives

    • 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 lake

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    Key Point of Training Programs

    • Building Data Lakes on AWS (BDLA) Prerequisites

      Who should attend
      This course is intended for:

      Data platform engineers
      Solutions architects
      IT professionals
      Certifications
      This course is part of the following Certifications:

      AWS Certified Data Engineer - Associate
      Prerequisites
      We 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 digital course

    • Building Data Lakes on AWS (BDLA) Delivery Format

      In-Person

      Online

    • Building Data Lakes on AWS (BDLA) Outline

      Module 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 review