Project Name
Apache Cassandra 5.x Storage Optimization and SAI Migration for a Global E-Commerce Platform
The client is a leading global e-commerce company operating across North America, Europe, and the Asia-Pacific region, managing millions of customer transactions daily across web and mobile channels. Their Apache Cassandra cluster had grown to 120 TB of stored data, supporting a product catalog, customer profiles, order history, and regional inventory data that together demanded fast, flexible querying at scale.
As the platform expanded into new markets and product categories, the engineering team followed the standard Cassandra design pattern of the time: build a new denormalized table for each new query pattern. Over time, this approach created a data model built around queries rather than data, resulting in five separate tables storing largely the same customer and transaction information in slightly different shapes. Every new business requirement added operational burden rather than delivering capability, creating a scalability tax that the team could no longer afford.
Ksolves, an AI-First Company, was engaged to modernize the Cassandra data infrastructure, eliminate redundant storage, and deliver a schema architecture that could evolve without a complete redesign every time the business introduced a new feature.
Here are the key challenges that created a scalability tax on the client's data infrastructure:
- Redundant Tables Multiplying Storage and Write Overhead: Customer and transaction data were duplicated across five different denormalized tables, each designed to serve a specific query pattern. Every write operation required five simultaneous table updates, compounding latency with each new data point ingested.
- Storage Bloat Driving Cloud Costs: The five-table model inflated the total stored data from the actual unique dataset to 120 TB, a significant storage amplification factor that generated unnecessary cloud storage and backup costs each month, with no additional data value.
- High Write Latency Introducing Inconsistency Risk: The multi-table write pattern produced measurable write latency degradation during peak transaction windows. With five tables requiring synchronized updates per write, any delay in one table creates window-level inconsistency across the data model, increasing the risk of serving stale or conflicting records to customers.
- Rigid Schema Design Blocking Feature Delivery: Adding new search capabilities, such as filtering by region, purchase history, or customer age segment, required a complete schema redesign affecting all five denormalized tables. Each change cycle consumed multiple weeks of engineering time, required planned downtime for data migrations, and delayed product feature releases. With new product categories and regional expansions requiring frequent schema changes annually, this rigidity had become a strategic bottleneck.
Ksolves began with a full diagnostic review of the existing data model, query execution patterns, table utilization, and write amplification ratios before making any changes to the production cluster. This evidence-based baseline confirmed that the root cause was architectural, not operational, and that consolidation, rather than tuning, was the appropriate intervention.
- Unified Data Model with a Golden Record Architecture: Ksolves consolidated all five denormalized tables into a single Golden Record table, establishing one authoritative source of truth for customer and transaction data. This eliminated cross-table write amplification entirely and reduced the total stored dataset from 120 TB to 72 TB, a 50% reduction that eliminated 48 TB of storage.
- Storage-Attached Indexing for Flexible, Low-Overhead Querying: SAI indexes were created on key attributes including region, age, and purchase history, enabling multi-column AND filters without cluster-wide scans. The query capabilities that previously required five separate tables were delivered through a single, indexed schema, with no additional write overhead.
- Optimized Write Performance via Single-Table Writes: With one Golden Record table replacing five, every write operation became a single-table update. This eliminated the multi-table synchronization overhead, delivering a 65% reduction in write latency and removing the inconsistency risk introduced by the multi-table pattern.
- Simplified Schema Evolution Without Table Redesigns: SAI's native integration with Cassandra's storage engine means new indexes can be added to the existing schema without a full table redesign. Schema changes that previously consumed weeks of migration planning and engineering hours are now completed in a fraction of the time, delivering a 90% reduction in schema update time.
Technology Stack
| Layer | Legacy (Before) | Modern (After) |
|---|---|---|
| Database | Apache Cassandra (pre-5.x) | Apache Cassandra 5.x |
| Indexing | Secondary indexes / query-driven tables | Storage-Attached Indexing (SAI) |
| Schema Model | Five denormalized tables | Single Golden Record table |
| Total Storage | 120 TB (5x amplification) | 72 TB (50% reduction) |
| Write Pattern | Five simultaneous table updates per transaction | Single-table writes |
| Schema Evolution | Full table redesign required per change | New SAI indexes added without redesign |
| Query Capability | One table per query pattern | Multi-column AND filters on a single table |
- 50% Reduction in Cloud Storage Costs: Total stored data reduced from 120 TB to 72 TB, eliminating 48 TB of redundant storage and its associated cloud backup and replication costs.
- 65% Improvement in Write Latency: Single-table writes replaced five-table synchronized updates, directly cutting write response times and eliminating the window-level inconsistency risk.
- 90% Reduction in Schema Update Time: New data access patterns and filter capabilities are now delivered by adding SAI indexes to the Golden Record schema, replacing multi-week migration cycles with changes that complete in minutes.
- Operational Complexity Reduced from 5 Tables to 1: Schema management overhead dropped from maintaining five CQL schemas, five sets of ETL dependencies, and five backup targets to a single Golden Record schema, significantly reducing engineering hours spent on data model maintenance each month.
- Reduced Total Cost of Ownership: The 48 TB storage reduction, combined with lower write operation costs and reduced backup window overhead, delivered measurable annual TCO savings across cloud infrastructure.
- AccelaAccelerated Feature Delivery Cycles: New e-commerce features requiring new data access patterns, previously blocked on schema redesigns consuming weeks of engineering time, are now delivered by adding SAI indexes, increasing release velocity for data-dependent product features.erated development cycles and improved feature delivery speed.
- Long-Term Scalability Without Schema Debt: The Golden Record architecture with SAI indexing supports future data growth and new query requirements without accumulating the schema debt that had created the original scalability tax.
“Our Cassandra data model had grown into something we had to work around rather than with. Every new business requirement meant adding another denormalized table, more storage, more writes, more complexity. Upgrading to Cassandra 5.x with SAI was the architectural reset we needed. Ksolves consolidated five tables into one Golden Record in a single engagement. We cut 48 TB of storage, our writes are 65% faster, and schema changes that used to take weeks now take minutes. The 50% storage cost reduction was significant, but the operational simplicity we gained is what changed how the team works every day.”
VP of Engineering, Global E-Commerce Platform (name withheld by request)
By upgrading to Apache Cassandra 5.x with Storage-Attached Indexing (SAI), we transformed a query-driven, five-table data model into a single Golden Record architecture that is faster to write, cheaper to store, and simpler to evolve. The 50% storage reduction (from 120 TB to 72 TB), 65% write latency improvement, and 90% schema update time reduction are measurable outcomes of a single architectural principle: design around data, not queries.
For global e-commerce platforms managing large-scale Cassandra deployments, this engagement sets a new standard for operational efficiency in large-scale NoSQL environments. Explore Ksolves Apache Cassandra Development Services to see how we can deliver the same transformation for your infrastructure.
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