This 1-day course provides hands-on skills in ingesting, analyzing, transforming and visualizing data using AWS Athena and getting the best performance when using it at scale.
Audience:
This class is intended for data engineers, analysts and data scientists responsible for: analyzing and visualizing big data, implementing cloud-based big data solutions, deploying or migrating big data applications to the public cloud, implementing and maintaining large-scale data storage environments, and transforming/processing big data.
Prerequisites:
Before attending this course, participants should have: General understanding of columnar formats such as Parquet and ORC Experience using a SQL-like query language to analyze data
Objectives:
At the end of this course, participants will be able to:
- Understand the purpose of and use cases for AWS Athena
- Comparing AWS Athena to Google BigQuery
- Understand the architecture of Athena and how queries are processed
- Interact with Athena using the web UI and JDBC driver
- Identify the purpose and structure of Athena schemas and data types
- Transform and load data into Athena
- Store and re-access query results
- Integrating Athena with other AWS services such as ELB, CloudFront etc.
- Understand Athena pricing structure and controlling the costs
- Identify best practices for optimizing query performance
- Troubleshoot common errors in Athena
- Use various Athena functions
- Use external tools to interact with Athena via JDBC
- Visualize Athena data
- Understanding AWS Athena service limits
AWS Athena Workshop Labs
- Lab 2 - Interacting with AWS Athena
- Lab 3 - Supported Formats and SerDes
- Lab 4 - Partitioning Data
- Lab 5 - Converting to Columnar Formats
Evaluation Form