7 questions found
What is Amazon Athena and what problem does it solve?
Beginner
Amazon Athena is a serverless interactive query service that lets you analyze data stored directly in Amazon S3 using standard SQL, without needing to set up or manage any database servers, making it easy to quickly explore large datasets and only pay for the queries you actually run.
SELECT * FROM sales_data WHERE region = 'US' LIMIT 10;
Real-world example
A marketing team analyzes millions of website log files stored in S3 using simple SQL queries in Athena, getting insights within minutes instead of setting up a full data warehouse.
Common follow-ups: What file formats does Athena support?;How is Athena priced?
S3 & Storage;Amazon QuickSight & Business Intelligence
How does Athena pricing work, and how can you reduce query costs?
Beginner
Athena charges based on the amount of data scanned by each query, so you can reduce costs significantly by converting your data into a compressed columnar format like Parquet, partitioning your data by fields commonly used in filters such as date, and only selecting the specific columns you need instead of using select star.
CREATE TABLE sales_parquet WITH (format = 'PARQUET') AS SELECT * FROM sales_data;
Real-world example
A company reduces its monthly Athena bill by over seventy percent after converting its raw CSV log files into partitioned Parquet format, since each query then scans far less data.
Common follow-ups: What is the difference between row based and columnar storage formats?;How do you set up data partitioning in Athena?
S3 & Storage;AWS Cost Management & Billing
What is the AWS Glue Data Catalog and how does it relate to Athena?
Intermediate
The AWS Glue Data Catalog acts as a central metadata repository that stores information about the structure and location of your data, such as table schemas and partition details, and Athena relies directly on this catalog to know how to interpret the raw files sitting in S3 as structured tables that can be queried with SQL.
aws glue create-table --database-name mydb --table-input file://table-definition.json
Real-world example
A data engineering team uses a Glue crawler to automatically scan a folder of JSON files in S3 and populate the Glue Data Catalog, allowing analysts to immediately query that same data through Athena.
Common follow-ups: What is a Glue crawler and how does it discover schemas automatically?;Can Athena work without the Glue Data Catalog?
AWS Glue & ETL;S3 & Storage
How does partitioning improve query performance and reduce cost in Athena?
Intermediate
Partitioning organizes your data into separate folders in S3 based on one or more key values, such as year, month, and day, so when a query filters on those partition keys, Athena can skip scanning irrelevant folders entirely, which both speeds up the query and reduces the amount of data scanned, directly lowering your cost.
SELECT * FROM logs WHERE year = '2026' AND month = '09';
Real-world example
A logging platform partitions its S3 data by date, so a query asking for just one day of logs only scans that single day's folder instead of scanning years of accumulated log history.
Common follow-ups: What is the difference between Hive style partitioning and manual partitioning?;How do you add new partitions automatically as new data arrives?
AWS Glue & ETL;AWS Cost Management & Billing
Can Athena query data from sources other than Amazon S3?
Intermediate
Yes, Athena Federated Query lets you run SQL queries against data sources beyond S3, including relational databases, DynamoDB, and other systems, by using data source connectors, which means you can join data sitting in S3 with data living in an operational database without needing to first copy everything into one place.
SELECT s.customer_id, d.order_total FROM s3_customers s JOIN dynamo_orders d ON s.customer_id = d.customer_id;
Real-world example
A retail company joins historical order data stored in S3 with live inventory data stored in DynamoDB using Athena Federated Query, producing a combined report without building a separate data pipeline.
Common follow-ups: What connectors are available for Athena Federated Query?;Is there a performance cost when querying across federated sources?
Amazon DynamoDB;AWS Glue & ETL
How do Athena workgroups help manage query costs and access control across teams?
Advanced
Athena workgroups let you separate queries run by different teams or applications, each with its own query result location, data scan limits, and cost tracking, so an organization can enforce spending limits per team and monitor usage separately, preventing one team's expensive queries from affecting the budget visibility of another.
aws athena create-work-group --name 'MarketingTeam' --configuration BytesScannedCutoffPerQuery=1000000000
Real-world example
A large enterprise creates separate Athena workgroups for its finance and marketing teams, setting a data scan limit on the marketing workgroup to prevent runaway costs from accidental large queries.
Common follow-ups: How do you set a per query data scan limit in a workgroup?;Can workgroups enforce which S3 bucket stores query results?
AWS Cost Management & Billing;IAM
What are the performance considerations when running very large analytical queries in Athena?
Advanced
Since Athena is built on Presto and processes queries in a distributed manner, performance for very large queries benefits greatly from using compressed columnar formats like Parquet or ORC, partitioning data appropriately, avoiding small numbers of very large files by compacting them, and limiting the columns selected, since Athena has to scan and process every byte of data included in a query regardless of how it will ultimately be filtered.
SELECT customer_id, order_total FROM sales_parquet WHERE year = '2026' AND month = '09';
Real-world example
A data team notices queries against thousands of tiny JSON files are extremely slow, so they run a compaction job to merge them into larger Parquet files, dramatically improving Athena query performance.
Common follow-ups: What is file compaction and why does it help Athena performance?;How does Athena's underlying Presto engine handle distributed query execution?
AWS Glue & ETL;S3 & Storage