Updated February 28, 2025
· ~4 min read
Amazon Athena and Amazon Redshift are both SQL query engines on AWS, but they represent opposite ends of the spectrum: Athena is serverless, pay-per-query, and queries data directly on S3 without loading anything. Redshift is a columnar data warehouse: data is loaded (or accessed via Spectrum), stored in columnar format, and queried with consistently high concurrency performance.
Athena emerged from the Presto project and is optimized for ad-hoc exploration, operational queries, and infrequent large scans. It scales to zero when not in use and charges $5/TB of data scanned. Redshift was AWS's answer to on-premises data warehouses — purpose-built for concurrent analytical queries, BI dashboard workloads, and complex multi-table joins on multi-terabyte fact tables.
The architecturally interesting development: Apache Iceberg support in both Athena and Redshift Spectrum has blurred the boundary considerably. Athena can now write Iceberg tables, run MERGE statements, and serve as a lightweight data transformation engine. Redshift Spectrum can query S3 Iceberg tables without loading data. Many production architectures use both.
Athena is the right choice when query frequency is low, data is already on S3, and you want to avoid maintaining a warehouse. Three specific scenarios favor Athena: data exploration and ad-hoc analysis by a small team of analysts (fewer than 5 simultaneous queries at any time), operational analytics where queries run hourly or daily rather than continuously, and cost-sensitive environments where the $5/TB scan model is cheaper than maintaining a Redshift cluster for infrequent use. Optimize aggressively: Parquet or ORC format reduces scan costs 10–20× vs CSV; partition pruning with well-designed partition keys (year/month/day for time-series) eliminates irrelevant files before scanning; query result reuse returns cached results for identical queries within a configurable window. A well-optimized Athena setup is hard to beat on cost for low-to-moderate frequency analytical workloads.
Redshift is the right choice when concurrency, consistent latency, and complex analytical query performance matter more than per-query cost. The canonical Redshift use case: a BI dashboard (Tableau, Power BI, QuickSight) refreshed continuously by 10+ business users expecting sub-second query response times. Athena's Presto architecture is not designed for this — queries queue, cold starts add latency, and the serverless cost model becomes expensive at high query rates. Redshift's MPP architecture distributes query execution across multiple compute nodes, handles high concurrency via the Workload Management (WLM) system, and caches result sets for repeated queries. Choose provisioned Redshift when utilization is consistently high; choose Redshift Serverless when utilization is variable and cost efficiency at low utilization matters.
The most common production pattern combines both: keep all historical data in S3 as Apache Iceberg tables (the data lake), use Athena for exploration and ETL queries, and serve BI dashboards from Redshift with Redshift Spectrum querying the S3 Iceberg tables for historical data that does not fit in Redshift's storage tier. This avoids the data duplication cost of loading everything into Redshift while still providing warehouse-quality performance for reporting. A practical split: load the most recent 90 days of fact data into Redshift native storage for fast dashboard queries; access anything older via Redshift Spectrum pointing to the S3 Iceberg table. Spectrum queries are charged per byte scanned at Redshift's standard rate — optimize the same way as Athena (Parquet, partitioning).
Question 1 of 4
How frequently will this query engine be used?
Use Athena for ad-hoc analysis, data exploration, operational queries that run a few times per day, and any workload where serverless pay-per-query economics make sense. Use Redshift when you have concurrent BI dashboards (5+ simultaneous users), need consistent sub-second query latency for reporting, or have heavy aggregations on multi-terabyte fact tables where Athena's per-scan cost would be prohibitive. Use both together via Redshift Spectrum for heavy analytics on S3 data: Athena for exploration and ETL, Redshift for production dashboard serving. Do not maintain two separate copies of data — Iceberg on S3 with Redshift Spectrum querying it is the efficient pattern.
AWS Data Lakehouse Platform
A production data lakehouse on AWS (S3-backed data lake with Glue, Iceberg, Athena, and Redshift) structured for analytics, ML feature stores, and governed data sharing.
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As of February 28, 2025. Provided as is, for general information, and accurate to the best of our knowledge on the date shown. Not legal, compliance, tax, financial or other professional advice. Prices, limits and vendor services change; check current vendor documentation before relying on anything here. Terms of use.