Project Name
How a Global E-Commerce Platform Cut Storage Costs by 40% with Cassandra 5.x and Storage-Attached Indexing (SAI)
A leading global e-commerce company managing millions of customer transactions relied on Apache Cassandra for its high scalability and availability. Over the years, their Cassandra data model became increasingly complex, designed primarily around query patterns instead of business logic. This approach worked initially but resulted in higher operational costs, slower feature delivery, and growing inefficiencies as data volumes increased.
Here are the keg challenges that created a “scalability tax,” where every new business requirement led to additional operational burden.
- Redundant Tables: To support multiple search and filter operations, the team maintained several denormalized tables, each tailored to a specific query pattern.
- Storage Bloat: Data was duplicated across five different tables, inflating cloud storage costs and lengthening backup and restore times.
- High Write Latency: Every write operation was replicated across multiple tables, increasing latency and the risk of data inconsistency.
- Rigid Schema Design: Adding new search capabilities often required a complete schema redesign, extensive data migrations, and downtime planning.
Our engineering team upgraded to Apache Cassandra 5.x and introduced Storage-Attached Indexing (SAI) to simplify data access and reduce redundancy.
- Unified Data Model: Consolidated five denormalized tables into a single Golden Record table that served as the single source of truth.
- Efficient Indexing: Created SAI indexes on key attributes such as region, age, and purchase history to enable flexible filtering and search without needing multiple tables.
- Optimized Query Performance: Leveraged SAI’s native integration with Cassandra’s storage engine to support multi-column AND filters without triggering cluster-wide scans.
- Simplified Schema Evolution: Future query requirements could now be addressed by simply adding new indexes instead of redesigning the entire schema.
- Storage usage dropped from 120 TB to 72 TB, achieving a 40% reduction.
- Write latency improved significantly: multiple table writes are replaced by a single table write, making operations 65% faster.
- Schema updates that previously took weeks can now be completed in minutes, reducing the cycle time by 90%.
- Operational complexity has decreased, moving from managing multiple redundant tables to maintaining a unified, simplified data model.
- Reduced total cost of ownership (TCO) through lower storage and maintenance costs.
- Accelerated development cycles and improved feature delivery speed.
- Simplified operations and improved long-term scalability.
Upgrading from Cassandra 5.x with Storage-Attached Indexing (SAI), we transformed the company’s data strategy. By eliminating redundant tables and leveraging intelligent, storage-integrated indexing, the team achieved both cost efficiency and architectural simplicity without compromising on scalability or performance. Cassandra’s SAI feature empowered the organization to design around its data, not its queries, marking the end of the data duplication era and setting a new standard for operational efficiency in large-scale NoSQL environments.
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