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Guided Lab: Retrieving Data using Amazon S3 Select


Amazon S3 Select is a powerful feature that enhances the capabilities of Amazon Simple Storage Service (S3) by offering efficient and selective data retrieval. S3 Select allows you to retrieve data from objects stored in your S3 buckets without downloading and processing the entire file. You can apply SQL-like queries to semi-structured data in JSON, CSV, and Parquet, enabling you to filter, transform, and aggregate data on the fly. This makes S3 Select an ideal tool for extracting valuable insights from massive datasets and improving data analytics.

S3 Select has substantial performance benefits as it minimizes data transfer and processing overhead, which reduces costs and speeds up data retrieval and analysis tasks. Whether working with log files, sensor data, or large datasets, Amazon S3 Select empowers you to access and process only the data you need efficiently, improving your data analytics and reducing the time and resources required for complex data manipulation tasks. It’s also seamlessly integrated with AWS Glue and Amazon Athena, extending its utility for a comprehensive and streamlined data analysis experience.

In this hands-on lab, you’ll explore the capabilities of Amazon S3 Select. This powerful feature efficiently retrieves, filters, and processes data from your S3 objects, making data analysis faster, cost-effective, and more precise. This practical experience will empower you to streamline your data workflows and easily extract valuable insights from your datasets.


To guarantee a successful completion of this lab, you must possess prior experience creating Amazon S3 buckets and have a solid understanding of their core components. If you believe that your knowledge in this regard is lacking, we strongly advise you to consider taking the following to acquire the required proficiency:

  • Creating an Amazon S3 bucket


In this lab, you will:

  • Execute SQL-like queries on your S3 data using S3 Select
  • Understand the benefits of using S3 Select over traditional data retrieval methods