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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Athena Federated Queries Explained
Athena Federated Query lets one SQL statement read from sources outside S3, such as DynamoDB, MySQL or Redshift, and join them with data lake tables. This guide explains connectors, predicate pushdown, passthrough queries, the documented limits, how billing works, and when copying the data is the better design.
Amazon Athena: Querying S3 with SQL
An explanation of Amazon Athena for people new to it: what the service is, how a table is only metadata pointing at files in S3, what happens when a query runs, where results go, how billing by bytes scanned works, how workgroups cap spending, and where Athena fits beside Redshift and EMR.
Athena vs Redshift: When to Use Each
Athena and Redshift both run SQL on AWS, but one is a query service over files in S3 and the other is a data warehouse. This comparison covers where the data lives, how each is billed, behaviour under many users, updates and modelling, AWS's own guidance, and the common design that uses both.
Parquet and Partitioning for Cheaper Athena Queries
Athena bills by the bytes each query scans, so file format decides most of the bill. This guide measures a real 2,964,624-row taxi file as CSV, gzipped CSV and Parquet, shows how few bytes a two-column query needs in a columnar file, and gives the CTAS statement that converts a table, with the limits to know.
Partitioning Data in S3 for Athena Performance
A practical guide to partitioning S3 data for Athena: what a partition is, how to choose keys, and how partitions are registered. A real month of taxi trips is partitioned by day and by hour to show pruning, a filter that prunes nothing, stray partitions from bad timestamps, and the small-file cost of going too fine.