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This document discusses the creation of a serverless data pipeline using AWS services like Athena and Step Functions. It covers data storage options based on query purposes including S3, Redshift, and RDS, and the serverless query service offered by Athena, which enables quick SQL queries without the need for ETL. The document also highlights the batch processing approach using Airflow and emphasizes the advantages of using AWS Step Functions for managing workflows.
Introduction to serverless data pipelines using Athena and Step Functions. Topics include data stores like S3, Redshift, and RDS.
Exploration of data storage options tailored for query purposes, emphasizing S3's durability and Redshift's capabilities.Discussion on Amazon Athena as a serverless ETL solution, its compatibility with various data formats, and fast query execution.
Exploration of batch processing using Airflow, including management challenges and the need for system resources.
AWS Step Functions functionality for creating workflows with enhanced error handling and serverless infrastructure.
Technical aspects of defining a serverless pipeline using code, including key components and interactions within services.
References for further reading on S3, Athena, and Step Functions, along with contact information and gratitude for the audience.


























