Apache Cassandra vs Amazon DynamoDB: Why Your NoSQL Strategy Matters More Than Your Database Choice
The Apache Cassandra vs Amazon DynamoDB debate is often framed as a choice between open-source flexibility and managed convenience. But in reality, obsessing over the database itself might be the wrong approach. The key to scalable, high-performance NoSQL applications isn’t just about picking the “right” database—it's about designing a great schema, running realistic load tests, and having detailed tracing metrics in place.
Why DynamoDB Often Wins on Convenience
Amazon DynamoDB is the easy button for NoSQL. It’s fully managed, scales automatically, and eliminates the need to worry about nodes, replication, or operational overhead. It’s a great choice if you want:
- Zero maintenance: No need to tune clusters or manage replication.
- Auto-scaling: Grows with demand, perfect for unpredictable workloads.
- Tight AWS integration: Seamlessly works with Lambda, API Gateway, and Step Functions.
However, DynamoDB isn’t always the cheapest option. Pricing depends on your workload and whether you use on-demand or provisioned capacity. For example, in us-east-2 (Ohio):
- Write request units: $0.625 per million
- Read request units: $0.125 per million
- Storage: $0.25 per GB-month
A high-throughput application with 100M writes per day could easily cost $1,875 per month just for writes, before considering reads and storage.
Why Cassandra Appeals to Engineers Who Want Control
If you need fine-grained control over your infrastructure or multi-cloud deployments, Apache Cassandra is appealing. It’s an open-source, peer-to-peer distributed database that avoids vendor lock-in. But Cassandra isn’t magic—it requires serious operational expertise to:
- Tune compaction and repair processes to avoid performance degradation.
- Manage clusters across multiple data centers with consistent availability.
- Handle undocumented scaling limits under extreme loads.
AWS Keyspaces: Cassandra Without the Headaches?
If you like Cassandra’s data model but don’t want to manage clusters, AWS Keyspaces (for Apache Cassandra) is an alternative. It uses the same CQL (Cassandra Query Language) but is fully managed like DynamoDB. However, pricing can be steep, with:
- Write request units: $0.625 per million
- Read request units: $0.125 per million
- Storage: $0.25 per GB-month
- Point-in-time recovery (PITR): $0.20 per GB-month
So, is Keyspaces just as expensive as DynamoDB? Pretty much. But it lets you reuse existing Cassandra schemas without re-architecting your application.
Forget the Database—Focus on Schema, Load Testing, and Tracing
Choosing between Cassandra, DynamoDB, or Keyspaces matters less than how you design your schema and monitor performance. Your real focus should be:
- Schema Design: Model access patterns first, avoid hot partitions.
- Load Testing: Simulate real traffic patterns before production.
- Tracing & Metrics: Use APM tools like Datadog, AWS X-Ray, or OpenTelemetry to diagnose slow queries.
A well-designed schema on DynamoDB outperforms a bad schema on Cassandra, and vice versa. The best database? The one that gives predictable performance while keeping costs under control.
Conclusion
If you want simplicity, go DynamoDB. If you need open-source control, choose Cassandra. If you love Cassandra’s model but hate managing clusters, AWS Keyspaces is a middle ground. But in the end, your database choice matters less than your architecture, query design, and performance monitoring.
Amazon DynamoDB vs Amazon Keyspaces (aka Apache Cassandra)
