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Machine Learning Engineer
You will learn about the components and functions 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 exercises will deepen your knowledge by exploring various common data lake architectures.
Module 1: Introduction to Data Lakes
Describing the value of data lakes
- Comparison of data lakes and data warehouses
- Describing the components of a data lake
- Recognize common architectures that are based on data lakes
Module 2: Data acquisition, cataloging and preparation
- Describing the relationship between data lake memory and data input
- Describe the AWS glue crawlers and how they are used to create a data catalog
- Identify data formatting, partitioning and compression for efficient storage and retrieval
- Exercise 1: Setting up a simple data lake
Module 3: Data processing and analytics
- Recognize how data processing is applied to a data lake
- Using AWS Glue to process data in 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
- Using AWS Lake Formation to create a data lake
- Understanding the AWS Lake Formation security model
- Exercise 2: Create a data lake with AWS Lake Formation
Module 5: Additional lake formation configurations
- Automate AWS Lake Formation using blueprints and workflows
- Applying security and access controls to AWS Lake Formation
- Matching data records with AWS Lake Formation FindMatches
- Visualize data with Amazon QuickSight
- Exercise 3: Automated creation of data lakes using AWS lake formation blueprints
- Exercise 4: Data visualization with Amazon QuickSight
Module 6: Architecture and course review
- Knowledge check after the course
- Review of the architecture
- Course review
- Application of data lake methods in the planning and design of a data lake
- Formulation of the components and services required to build an AWS Data Lake
- Securing a data lake with corresponding authorizations
- Importing, storing and converting data in a data lake
- Querying, analyzing and visualizing data in a data lake
This course consists of training training and is led by a trainer who supervises the participants live. Theory and practice are taught with live demonstrations and practical exercises. The video conferencing software Zoom is used.
This course is aimed at the following job roles:
- Data analytics
- Machine Learning & AI
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