Amazon Athena Interview Questions with Answers & Use Cases

1. What is Amazon Athena?

Answer: Athena is a serverless interactive query service that allows you to analyze data stored in Amazon S3 using standard SQL. You pay per query based on data scanned.
Use case: Quickly run ad hoc queries on logs or CSV files stored in S3 without managing infrastructure.

2. How do you connect to Athena from the AWS Management Console?

Answer: Login to AWS Console, open Athena service, set query result location in S3, and start running SQL queries via the web UI.
Use case: Analysts use the console for exploratory data analysis.

3. How can you connect to Athena using JDBC/ODBC drivers?

Answer: Download Athena JDBC/ODBC drivers from AWS, configure DSN with AWS credentials and region, and connect from BI tools like Tableau or SQL clients.
Use case: Business users connect BI tools like Power BI or Tableau to Athena for dashboards.

4. What AWS Glue components does Athena use?

Answer: Athena uses the AWS Glue Data Catalog as its metadata repository for databases, tables, and schemas. Glue Crawlers help populate the catalog.
Use case: Centralized schema management for all datasets queried via Athena.

5. How do you create a database and table in Athena?

Answer: Use SQL DDL commands like CREATE DATABASE and CREATE EXTERNAL TABLE specifying schema and S3 location.
Use case: Organize datasets logically and define schemas for querying structured data.

6. What data formats does Athena support?

Answer: Supports CSV, JSON, Parquet, ORC, Avro, and more. Columnar formats like Parquet and ORC improve query performance.
Use case: Storing logs in Parquet reduces query cost by scanning less data.

7. How do you define partitions in Athena and why?

Answer: Partitions divide data logically (e.g., by date). Define partition columns in table DDL. Partition pruning speeds queries by scanning fewer files.
Use case: Partition logs by year/month/day to reduce query scan size.

8. How does Athena handle schema changes?

Answer: Update Glue catalog manually or run Glue Crawler to detect schema changes. Queries use latest metadata.
Use case: Adding new columns to datasets without downtime.

9. Can Athena update or delete data?

Answer: Native Athena is read-only on S3 data. Updates/deletes require workarounds like CTAS or using Athena's support for transactional tables via AWS Lake Formation governed tables.
Use case: Managing slowly changing data with ACID guarantees using Lake Formation.

10. What is CTAS in Athena?

Answer: Create Table As Select lets you create new tables from query results, often used for data transformation or conversion to efficient formats like Parquet.
Use case: Convert raw CSV logs into optimized Parquet tables for faster querying.

11. How do you optimize query performance in Athena?

Answer: Use partitioning, choose columnar data formats, compress data, limit scanned columns, and use predicate pushdown.
Use case: Partition large sales data by region to speed up regional sales reports.

12. What are Athena Workgroups?

Answer: Workgroups allow grouping queries and users with separate settings, cost controls, and access management.
Use case: Separate dev and prod queries to monitor and limit cost.

13. How do you secure Athena queries?

Answer: Use IAM policies for user access, encrypt query results, and integrate with AWS Lake Formation for fine-grained access control.
Use case: Restrict access to sensitive columns in customer data for compliance.

14. What IAM permissions are required to run Athena queries?

Answer: athena:StartQueryExecution, athena:GetQueryResults, and s3:GetObject/s3:PutObject on query result buckets.
Use case: Grant analysts read-only query execution rights.

15. How do you monitor Athena queries?

Answer: Use AWS CloudTrail for API logging, CloudWatch for query metrics and errors, and Athena's console query history.
Use case: Track failed queries to troubleshoot data issues.

16. How does Athena integrate with AWS Lake Formation?

Answer: Athena enforces Lake Formation permissions to provide fine-grained access control on data catalog tables (column/row-level security).
Use case: Enforce regulatory data access policies centrally.

17. What is Federated Query in Athena?

Answer: Federated Query allows querying data from relational and non-relational databases (e.g., RDS, DynamoDB) alongside S3 data.
Use case: Join CRM data in RDS with web logs in S3 for customer behavior analysis.

18. How do you query nested data like JSON arrays in Athena?

Answer: Use built-in functions like json_extract, UNNEST, and CROSS JOIN to flatten and query nested structures.
Use case: Extract customer info stored as JSON arrays in logs.

19. How do you handle NULL values in Athena queries?

Answer: Use standard SQL functions like COALESCE, IS NULL checks to handle missing data gracefully.
Use case: Replace NULL sales amounts with 0 in reports.

20. How do you filter partitioned data efficiently?

Answer: Use WHERE clause on partition keys (e.g., WHERE year=2023) so Athena scans only relevant partitions.
Use case: Query sales only for Q1 2024.

21. How do you create views in Athena?

Answer: Use CREATE VIEW statement to encapsulate complex queries for reuse. Views are virtual and don’t store data.
Use case: Simplify repeated join logic in customer purchase analysis.

22. How do you perform joins in Athena?

Answer: Use ANSI SQL JOIN syntax (INNER, LEFT, RIGHT). Large table joins may be expensive; optimize data layout.
Use case: Join customer and order tables for detailed sales reports.

23. Can you run updates inside Athena tables?

Answer: Native Athena is read-only, but transactional updates are possible with Lake Formation governed tables or using CTAS with overwrite.
Use case: Implement slowly changing dimension updates with Lake Formation.

24. How does Athena charge for queries?

Answer: Charges based on amount of data scanned per query. Use compression and partitioning to reduce scanned bytes.
Use case: Convert CSV to Parquet to reduce costs by 75%.

25. How do you export Athena results?

Answer: Results are saved to S3 output location; you can download or integrate with other AWS services like QuickSight or Lambda.
Use case: Automate report delivery by triggering Lambda on result files.

26. How do you encrypt Athena query results?

Answer: Enable encryption at rest using SSE-S3 or SSE-KMS for S3 output buckets.
Use case: Comply with data security requirements.

27. What are some common reasons for Athena query failures?

Answer: Incorrect schemas, missing partitions, insufficient permissions, or malformed data.
Use case: Troubleshoot by checking Glue catalog and IAM policies.

28. How do you use Glue Crawlers with Athena?

Answer: Crawlers scan S3 data, infer schemas, and update Glue catalog used by Athena for query metadata.
Use case: Automatically detect new data columns without manual schema updates.

29. What are some limitations of Athena?

Answer: Limited support for complex transactions, no native update/delete (without Lake Formation), max query runtime 30 minutes, and no stored procedures.
Use case: Use Redshift or EMR for heavy OLTP or long-running jobs.

30. How can you improve Athena query speed?

Answer: Partition data, use columnar formats, reduce scanned columns, and avoid SELECT *.
Use case: Optimize a dashboard backend query scanning terabytes of logs.

31. What is predicate pushdown in Athena?

Answer: Ability to apply filters early in the query to reduce data scanned. Supported for Parquet/ORC.
Use case: Filter by event type before reading entire dataset.

32. How do you handle schema evolution in Athena?

Answer: Use Glue crawler to update schemas or manually ALTER tables to add columns.
Use case: Support adding new fields in evolving JSON logs.

33. Can you query compressed data in Athena?

Answer: Yes, supports gzip, snappy, bzip2, and others transparently.
Use case: Reduce S3 storage and query cost with compressed Parquet files.

34. How do you handle data with inconsistent schemas?

Answer: Use schema-on-read flexibility or convert to consistent schema with CTAS.
Use case: Clean messy log data before analysis.

35. What is the maximum query result size in Athena?

Answer: By default, 100 MB uncompressed. Larger results saved to S3 output bucket.
Use case: Manage large exports by splitting queries.

36. How do you integrate Athena with QuickSight?

Answer: Connect QuickSight directly to Athena using JDBC/ODBC for live dashboards.
Use case: Build real-time data visualizations on S3 datasets.

37. How do you automate Athena queries?

Answer: Use AWS Lambda or Step Functions triggered by events or schedules to run queries programmatically.
Use case: Daily ETL workflows with Athena.

38. What logging options exist for Athena?

Answer: CloudTrail logs API calls, CloudWatch logs query status and metrics.
Use case: Audit user activity and detect anomalous queries.

39. How do you control user access in Athena?

Answer: Use IAM policies and Lake Formation permissions for fine-grained control.
Use case: Restrict users to only query certain databases.

40. Can Athena query data across multiple AWS accounts?

Answer: Yes, with cross-account Glue Data Catalog sharing and resource links.
Use case: Centralize data lake queries across subsidiaries.

41. How do you run parameterized queries in Athena?

Answer: Not natively supported; use client-side query generation or AWS SDK to substitute parameters.
Use case: Dynamic report generation in applications.

42. How do you handle large datasets in Athena?

Answer: Use partitioning, compression, and consider splitting datasets into manageable chunks.
Use case: Query petabytes of IoT sensor data efficiently.

43. How can you reduce Athena query costs?

Answer: Convert to columnar formats, partition data, avoid SELECT *, and compress data.
Use case: Save thousands of dollars monthly on data lake queries.

44. How do you query data stored in multiple S3 buckets?

Answer: Create Glue tables referencing each bucket or use external tables with UNION ALL.
Use case: Query logs split by region in different buckets.

45. How do you handle time zone differences in Athena queries?

Answer: Use SQL functions to convert time zones, e.g., from_utc_timestamp.
Use case: Normalize logs from global servers to a common time zone.

46. How do you join Athena with Redshift?

Answer: Use Athena Federated Query or unload Redshift data to S3 and query with Athena.
Use case: Combine warehouse and data lake analytics.

47. Can Athena process streaming data?

Answer: Not directly; use Kinesis Firehose to batch data into S3, then query with Athena.
Use case: Near real-time analytics on streaming logs.

48. How do you handle duplicate data in Athena queries?

Answer: Use DISTINCT or window functions like ROW_NUMBER to filter duplicates.
Use case: Clean log data before aggregation.

49. How do you troubleshoot slow Athena queries?

Answer: Review query plan, check partitions, optimize data formats, and monitor query metrics.
Use case: Identify inefficient joins causing delays.

50. How do you back up Athena metadata?

Answer: Export Glue Data Catalog metadata using Glue APIs or CloudFormation templates.
Use case: Disaster recovery and migration planning.

Additional Interview Questions on Update/Delete in Athena with Glue Catalog & S3

51. Can you update or delete data directly in Athena tables stored on S3 using Glue Catalog?
Answer:
By default, Athena tables over S3 data are read-only. You cannot perform direct UPDATE or DELETE operations on the data files in S3 through Athena SQL. Athena works on immutable data files in S3 and does not support transactional updates or deletes natively.

Use Case: You have log files stored in S3 as Parquet or CSV, and you want to fix some incorrect records. You cannot simply run UPDATE on the Athena table.

52. How can you implement data updates or deletes in Athena?
Answer:

Use CREATE TABLE AS SELECT (CTAS) or INSERT OVERWRITE queries to create a new table or overwrite partitions with updated data.
Use AWS Lake Formation Governed Tables that support ACID transactions and enable INSERT/UPDATE/DELETE operations.
Use Apache Hudi, Apache Iceberg, or Delta Lake on S3, which provide transactional layers and integrate with Athena.

Use Case: You want to maintain a slowly changing dimension table in Athena. Using Apache Hudi on S3 lets you update records while keeping query compatibility.

53. What is a Lake Formation Governed Table and how does it enable update/delete?
Answer:
A Governed Table is a feature of Lake Formation that adds ACID transaction support on top of S3-backed tables. It provides snapshot isolation, allowing safe concurrent UPDATE, DELETE, and INSERT operations. Athena queries on these tables enforce transactionality.

Use Case: A finance team needs to correct daily transaction records with updates and deletes while running queries simultaneously.

54. How does using Apache Hudi or Iceberg help with updates in Athena?
Answer:
These open-source table formats support incremental changes (upserts, deletes) on top of S3 data and maintain metadata for transactional consistency. Athena supports querying these tables natively, enabling update/delete semantics through their transaction layers.

Use Case: Data engineers implement Hudi tables to support GDPR data deletion requests in a data lake.

55. Can you run DELETE queries on regular Glue Catalog tables pointing to S3?
Answer:
No, unless the table is a governed table with transaction support or backed by Hudi/Iceberg. For normal Glue tables, DELETE requires rewriting the data files via CTAS or external ETL jobs.

Use Case: To delete user data for privacy compliance, you must rewrite the affected partitions outside Athena or use Lake Formation governed tables.

56. How do you handle incremental updates to large datasets in Athena?
Answer:
Use partition overwrite strategies, CTAS to create new partitions, or leverage transaction-enabled tables with Lake Formation or Hudi/Iceberg to apply incremental changes efficiently.

Use Case: Daily batch job updates the last 7 days of sales data in the Athena table.

57. What are the limitations of CTAS for update/delete operations?
Answer:
CTAS creates a new table or partition and overwrites data but is not a true update or delete. It can be costly for large datasets and requires managing multiple versions.

Use Case: You need to rebuild partitions after correcting data, which requires downtime or complex job orchestration.

58. Can Athena queries trigger ETL jobs to handle data modification?
Answer:
No. Athena is a query engine and cannot trigger ETL workflows directly. You can automate ETL via AWS Glue Jobs, Lambda, or Step Functions outside Athena based on query results or schedules.

Use Case: Automate daily data correction by triggering Glue ETL jobs after validation queries in Athena.

59. How do transactional features in Lake Formation tables affect Athena query consistency?
Answer:
They provide snapshot isolation ensuring queries see a consistent snapshot of data, even during concurrent writes/updates, preventing dirty reads.

Use Case: Multiple analysts query the sales data while ETL jobs update some records.

60. What is the best practice for managing update/delete workloads in Athena?
Answer:
Use Lake Formation governed tables or open-source transactional formats (Hudi/Iceberg), avoid rewriting entire datasets manually, and architect data lakes to support incremental changes and ACID semantics.

Use Case: Long-term data governance requiring consistent, auditable data mutations.

Can Athena update or delete data if Glue Catalog is pointing to Aurora?

Short Answer:
No, Athena cannot update or delete data in Aurora directly, even if Glue Catalog has metadata for Aurora tables. Athena only queries data stored in Amazon S3 using the Glue Data Catalog as metadata. It does not natively support querying or modifying data inside relational databases like Aurora.

Detailed Explanation:

Glue Data Catalog Role:
Glue Catalog is a centralized metadata repository. It stores table schemas and locations. For Athena, Glue Catalog stores metadata about tables pointing to data files on S3.
Aurora and Glue:
Glue can crawl relational databases such as Aurora (via JDBC) and create metadata tables in the Glue Catalog describing that data schema, but this is just metadata. It does not move or store Aurora data in S3.
Athena Query Scope:
Athena is designed to query data only in S3. Even if Glue Catalog has tables registered from Aurora, Athena cannot execute queries on those database tables.
How to Query Aurora Data via Athena?
- Use Athena Federated Query with a JDBC connector for Aurora. This allows Athena to query Aurora data, but this is a read-only query interface.
- Federated Query does not support DML operations (UPDATE, DELETE) via Athena on Aurora.
Updating or Deleting Aurora Data:
To modify data in Aurora, you must use standard SQL clients or applications connected directly to Aurora, e.g., via JDBC, MySQL Workbench, or other tools.
Glue Catalog Does Not Change Update/Delete Behavior:
Glue Catalog is metadata only. It doesn't enable Athena to update/delete data in Aurora or any external database.

Use Case Example:

You have customer data in Aurora. You want to run analytics combining Aurora data with S3 data.
Use Athena Federated Query with Aurora connector for read queries.
Use your application or SQL client to run UPDATE/DELETE statements on Aurora data.
Use Glue Data Catalog as a metadata layer for both Aurora and S3 tables for a unified view, but query/update mechanisms differ.

Summary Table

Aspect	Athena + Glue Catalog Pointing to Aurora	Athena + Glue Catalog Pointing to S3
Query capability	Read-only via Federated Query	Full SQL query support
Update/Delete capability	No, must use native Aurora clients/tools	No native update/delete; possible with Lake Formation governed tables or transactional formats (Hudi/Iceberg) on S3
Glue Catalog role	Metadata only	Metadata + schema and partition info for S3 files
Use case	Federated queries combining Aurora and S3 data	Serverless analytics on data lake

✅ Clarifying Glue, Athena, Aurora & JDBC

🔹 Glue Works with More Than Just S3

Glue is not limited to S3. It can:

Connect to Aurora, RDS, Redshift, and other JDBC sources using Glue connections.
Crawl those data sources to extract schema and store them in the Glue Data Catalog.
You can use Glue Jobs (ETL) to read from Aurora and write to S3 (and vice versa).

🟢 So Glue can point to Aurora — but it's for ETL/metadata, not for querying Aurora via Athena directly.

🔹 Athena Works Only on S3 (unless Federated Query is used)

By default:

Athena only queries data in S3 using schemas from Glue Catalog.
It does not connect to Aurora directly via JDBC.

If you want Athena to query Aurora, you must use:

🔸 Athena Federated Query + Aurora Connector

Use Athena Federated Query with the Amazon Athena JDBC connector for Aurora MySQL/PostgreSQL.
This allows read-only querying from Aurora through Athena SQL.
Still, no update/delete support through Athena.

🔹 JDBC for Aurora

If your goal is to modify data (UPDATE/DELETE) in Aurora:

Use JDBC clients (e.g., DBeaver, MySQL Workbench, Python scripts, Java apps).
Athena cannot modify Aurora data.

✅ Final Summary

Feature	Purpose
Glue + S3	Data lake with metadata catalog, partitioning, ETL
Glue + Aurora	Crawl schema, ETL from/to Aurora, catalog Aurora tables
Athena + Glue (S3)	Query S3 data using SQL via Glue Catalog
Athena + Aurora (Federated Query)	Read-only queries from Aurora via connector
Aurora JDBC Connection	Direct query + update/delete operations on Aurora

🔧 Typical Scenarios

✅ You want to run SELECT queries on Aurora from Athena:

➡️ Use Athena Federated Query with the Aurora connector.

✅ You want to perform INSERT/UPDATE/DELETE on Aurora:

➡️ Use a JDBC/ODBC SQL client, Lambda with RDS Data API, or application logic.

✅ You want to move data from Aurora to S3 for Athena analytics:

➡️ Use a Glue ETL job to extract data from Aurora and write to S3 in Parquet/CSV format.

❓ Can Athena Use Aurora JDBC Connection?

🔹 Not directly.

You cannot use a raw Aurora JDBC connection inside Athena like you would in an application. However:

✅ You Can Use Athena Federated Query with Aurora — and It Uses a JDBC Connector Behind the Scenes

Here's how it works:

Athena Federated Query allows you to query Aurora (MySQL or PostgreSQL) using Athena SQL, without copying data to S3.
It uses a special JDBC connector, deployed via AWS Lambda.
This connector is managed via AWS Data Source Connector Framework, not through Athena's regular SQL interface.

📌 What You Need to Set It Up:

Create a Lambda function using AWS-provided Athena JDBC Connector for Aurora.
Grant network access from Lambda to your Aurora database (VPC, security groups, subnet).

sql

CREATE EXTERNAL DATA SOURCE my_aurora
USING 'lambda:arn:aws:lambda:...'
WITH CONNECTION 'my-glue-connection';

Query it using:

sql

SELECT * FROM my_aurora.database_name.table_name;

🔐 How Glue Comes In:

You can create a Glue Connection to Aurora and reuse that in your Federated Query setup.
Glue Connection contains JDBC URL, user/password, and VPC settings.

✅ Summary

Method	Can Athena Use It?	Purpose
Glue Connection (JDBC)	✅ Indirectly	Used with Federated Query Lambda
Direct JDBC in Athena	❌ Not supported	Athena does not support native JDBC queries
Athena Federated Query	✅ Yes	Best way to query Aurora from Athena

🛠️ Real Use Case Example

You have customer data in Aurora PostgreSQL and order data in S3. You want to:

Join them in a single Athena query.
Keep Aurora data live (no copy to S3).

➡️ Use Athena Federated Query with Aurora connector.

sql

SELECT
  s.order_id,
  a.customer_name
FROM
  s3_orders s
JOIN
  aurora.customers a
ON
  s.customer_id = a.id;

Wednesday, 25 June 2025

Athena - Interview questions