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Building Data Lakes on AWS – BDLAWS001

  • Course Code : BDLAWS001
  • Duration : 1 Day
  • Price : 708 GBP
  • Level: Intermediate
  • Language: English

Course Content

  • he Building Data Lakes on AWS course equips you with the skills to design and build an operational data lake capable of analyzing structured and unstructured data. You will explore the components and functionality of various AWS services integral to data lake creation, including AWS Lake Formation for data lake management, AWS Glue for data cataloging, and Amazon Athena for data analysis. Through a combination of lectures and hands-on labs, you will gain practical experience with common data lake architectures and best practices.

Delivery Method

  • Online

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Goals

    • By the end of this course, you will be able to:

      • Apply data lake methodologies to the planning and design process.
      • Identify the components and services required to build a data lake on AWS.
      • Secure your data lake using appropriate permissions and access controls.
      • Ingest, store, and transform data effectively within a data lake.
      • Query, analyze, and visualize data stored in the data lake.

Pre Requisites

    • Participants should have:

      • Completed the AWS Technical Essentials course.
      • One year of experience building data analytics pipelines or completed the Data Analytics Fundamentals digital course.

Course Outline

Module 1: Introduction to Data Lakes
  • Understanding the value of data lakes.
    • Comparing data lakes and data warehouses.
    • Exploring the components of a data lake.
    • Recognizing common architectures built on data lakes.
  • Understanding the value of data lakes.
    • Comparing data lakes and data warehouses.
    • Exploring the components of a data lake.
    • Recognizing common architectures built on data lakes.
  • Understanding the value of data lakes.
    • Comparing data lakes and data warehouses.
    • Exploring the components of a data lake.
    • Recognizing common architectures built on data lakes.
  • Understanding the value of data lakes.
    • Comparing data lakes and data warehouses.
    • Exploring the components of a data lake.
    • Recognizing common architectures built on data lakes.
  • Understanding the value of data lakes.
    • Comparing data lakes and data warehouses.
    • Exploring the components of a data lake.
    • Recognizing common architectures built on data lakes.
    • Conducting a post-course knowledge check.
    • Reviewing data lake architecture.
    • Final course review.

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