We’re evaluating Athena vs Redshift for our ERP analytics platform and I’m curious about real-world experiences with API integration complexity and performance. Our use case involves querying 2-5TB of structured ERP data (orders, inventory, financials) with both ad-hoc analysis and scheduled reporting.
Athena’s serverless model is attractive from a cost perspective - we only pay for queries executed. But I’m concerned about query performance for complex joins and whether the API integration is mature enough for production dashboards. Redshift offers consistent performance with cluster management, but the pricing model means we’re paying 24/7 even during low-usage periods.
Has anyone done a direct comparison of these services for ERP analytics? Specifically interested in API response times, query optimization complexity, and total cost of ownership when factoring in both compute and API integration development effort.
Consider Redshift Serverless as a middle ground - you get Redshift’s query performance without paying for idle capacity. It auto-scales based on workload and you pay per RPU-hour. We switched to it from provisioned Redshift clusters and saw 40% cost reduction while maintaining performance. The API integration is identical to provisioned Redshift, so no additional dev effort. For ERP analytics with variable workloads, Redshift Serverless often beats both traditional Redshift and Athena on the price/performance curve.
I’d push back on the pure cost comparison. Redshift cluster management gives you much better performance predictability, which matters for user-facing dashboards. With Athena, query times can vary significantly based on S3 data layout and file sizes. We use Redshift for our primary ERP reporting (sub-second queries) and Athena for ad-hoc exploration of historical data. The hybrid approach works well - Redshift handles the hot data (last 13 months), Athena queries the cold archive when needed.
Great insights from everyone! Here’s my synthesis after evaluating all the feedback and running our own benchmarks:
Athena Serverless Pricing: The pay-per-query model is compelling for our spiky workload (month-end heavy). Our cost analysis shows Athena would run ~$1,800/month for 3TB scanned vs ~$5,400/month for a Redshift dc2.large cluster. However, this assumes we optimize data layout - converting to Parquet and implementing partition pruning saved us 70% on scan costs in testing.
Redshift Cluster Management: Performance is definitely more predictable. Our benchmark queries (complex 5-table joins on order data) ran in 2-4 seconds on Redshift vs 8-15 seconds on Athena. For user-facing dashboards with SLA requirements, this matters. Redshift Serverless (mentioned by senior_data_architect) looks promising as a middle ground - we get autoscaling without idle costs.
API Integration Complexity: Both services have mature APIs, but the execution models differ significantly. Athena requires asynchronous query pattern (StartQueryExecution → GetQueryExecution polling → GetQueryResults), which adds complexity to our application code. Redshift Data API supports similar async pattern, but we can also use direct JDBC connections for synchronous queries in our real-time dashboards.
Our Decision: We’re implementing a hybrid architecture:
- Redshift Serverless for operational reporting (last 18 months of hot data, user-facing dashboards, SLA requirements)
- Athena for ad-hoc analytics and historical analysis (3+ years of cold data in S3, data science team exploration)
- API gateway layer that abstracts the difference for our application teams
This gives us performance where we need it and cost efficiency for infrequent queries. The API integration effort is similar for both (2-3 weeks), but maintaining two systems adds operational overhead we’ll need to manage. Total estimated cost: $3,200/month (Redshift Serverless ~$2,400 + Athena ~$800), which is 40% less than provisioned Redshift alone while meeting all our performance requirements.
We run both services for different use cases. Athena excels at exploratory analytics and log analysis where query frequency is low but data volume is high. Redshift is our go-to for operational reporting where users expect consistent sub-3-second query times. Cost-wise, Athena saved us about $4K/month compared to a dc2.large Redshift cluster, but we had to invest in data engineering time to partition our S3 data properly and convert to Parquet format. The API integration effort was similar for both - about 2 weeks to build our reporting service layer.
We migrated from Redshift to Athena last year for our ERP analytics. Athena serverless pricing was the main driver - our analytics workload is very spiky (heavy during month-end, quiet otherwise), so we were wasting money on idle Redshift clusters. Query performance is definitely slower on Athena for complex queries (2-3x in our tests), but the cost savings were 60% overall. The API integration was straightforward using the Athena SDK.