Amazon Web Services (AWS)
Building Data Lakes on AWS
Create a modern data lake on AWS, from ingestion to dashboards.
1 dayIntermediate6 modules
About this course
Create a modern data lake on AWS, from ingestion to dashboards. You will ingest, catalogue and prepare data with AWS Glue, secure it with Lake Formation, query it with Athena and visualise results in QuickSight.
What you'll learn
- Identify the core components of a modern data lake architecture
- Use AWS Lake Formation to build a data lake and manage access controls
- Catalog and transform data with AWS Glue
- Query datasets using Amazon Athena and visualize results with QuickSight
Curriculum
6 modules
- Define what a data lake is and why it matters
- Compare data lakes to traditional data warehouses
- Break down the key components of a data lake architecture
- Review several common data lake architectures used in AWS environments
- Ingest structured and unstructured data into your data lake
- Use AWS Glue crawlers to create a searchable data catalog
- Apply data formatting, partitioning, and compression for efficient storage
- Lab: Set up and populate a simple data lake
- Process data within your AWS data lake using AWS Glue
- Perform ad hoc queries using Amazon Athena
- Explore real-time analytics use cases
- Lab: Transform and query data sets
- Use AWS Lake Formation to build a data lake from start to finish
- Implement fine-grained access control and secure permissions
- Understand the functionality of the services involved in creating a data lake
- Lab: Build and secure your own AWS data lake
- Automate data lake setup using Lake Formation blueprints
- Create visual dashboards using Amazon QuickSight
- Lab: Automate workflows and visualize data analytics results
- Review key concepts and best practices
- Take a final knowledge check to reinforce learning
- Participate in a group discussion of data lake architecture strategies
Who it's for
Data engineers and architects designing analytics platforms.
Before you start
- Working knowledge of core AWS services and public cloud implementation
- One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course
- Attended one of Architecting on AWS, Developing on AWS, or Systems Operations on AWS
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