Athena

 Amazon Athena: Feature Comparison and Use Cases 

Feature	Description & Use Case	When to Use
Serverless Query Engine	Athena lets you run SQL queries directly on data stored in S3 without managing servers.	Ideal for ad-hoc queries, analytics on large datasets without infrastructure overhead.
Standard SQL Support	Supports ANSI SQL with extensions for querying complex data formats like JSON, Parquet, ORC.	When you want SQL familiarity with semi-structured data analysis in S3.
Multiple Data Format Support	Works with CSV, JSON, Parquet, ORC, Avro, etc.	Use when your data is stored in optimized columnar formats like Parquet for faster queries.
Glue Data Catalog Integration	Uses AWS Glue Catalog for metadata management, schema definitions, and partition indexing.	Essential when working with a well-organized data lake with schemas for faster querying.
Partition Pruning	Athena can skip reading irrelevant partitions to improve query performance.	When your dataset is partitioned (e.g., by date or region) and you want to minimize scan cost.
User-Defined Functions (UDFs)	Supports custom scalar functions written in Java or Python for complex transformations.	When built-in SQL functions aren't enough, and you need custom logic during querying.
Workgroups & Query Limits	Workgroups allow managing query execution, cost controls, and permissions for different teams.	For cost management and team-level query governance in shared environments.
Encryption Support	Supports encrypting query results with SSE-S3, SSE-KMS, or client-side encryption.	When you need to secure query output data per compliance or organizational policies.
Query Result Location Control	You can specify the S3 bucket where query results are stored.	Useful for organizing results, managing costs, or separating environments.
Federated Query	Ability to query data across relational databases, NoSQL, and other sources via Athena Federated Query.	When combining S3 data with data from databases or third-party systems in a single query.
Materialized Views	Supports materialized views to cache query results for faster repeated queries.	When queries are repeated frequently and you want to reduce compute costs and latency.
Automatic Table Updates (CTAS)	Supports Create Table As Select for transforming and storing query results back to S3.	When you want to build transformed datasets or optimized tables from raw data.
Integration with AWS Lake Formation	Athena enforces Lake Formation permissions for fine-grained access control on data in Glue Catalog.	Critical for secure, governed data lakes requiring row/column level security.
Performance Optimizations (Caching)	Uses result caching and query plan optimizations for faster response times.	When optimizing for speed in interactive or BI dashboard scenarios.
SQL Workbench & SDK Access	Query Athena via AWS Console, JDBC/ODBC drivers, CLI, SDKs, and integrated BI tools like QuickSight.	For seamless integration with data analytics tools and developer workflows.
Cost Model: Pay Per Query	Charges based on amount of data scanned during queries.	Useful for cost control by optimizing data formats and partitioning.

 

🛠️ Example Use Cases 

1. Ad Hoc Analytics on Raw Logs 

• Scenario: You collect web server logs in S3 as JSON files daily. 

• Use Athena Features: Query JSON files using standard SQL without ETL. 

• Benefit: Fast insights with zero infrastructure setup. 

 

2. Data Lake with Governed Access 

• Scenario: Multiple teams access sensitive financial data stored in Parquet format. 

• Use Athena Features: Glue Catalog integration + Lake Formation enforcement. 

• Benefit: Fine-grained, secure access control to columns and rows per user. 

 

3. Combining Data from Multiple Sources 

• Scenario: You want to join customer data in RDS with purchase data in S3. 

• Use Athena Features: Federated queries to combine relational DB and S3 data. 

• Benefit: Unified analytics without data movement. 

 

4. Repeated Reporting Queries 

• Scenario: Daily dashboards query the same summary of sales data. 

• Use Athena Features: Materialized views for precomputed results. 

• Benefit: Reduced query latency and cost savings. 

 

5. Transforming Raw Data into Optimized Tables 

• Scenario: Convert raw CSV logs into partitioned Parquet tables for analytics. 

• Use Athena Features: CTAS (Create Table As Select). 

• Benefit: Optimized queries and cost reduction.