Design & Build

Cassandra rewards teams that design tables around access patterns and punishes everyone else. Design-and-build engagements put that discipline in place from the start, whether the target is self-managed Cassandra, DataStax Enterprise, or Astra DB.

Query-first data modeling

We start from your query workload — every read and write the application needs, with expected rates and growth — and derive the tables from there. Partition keys chosen so partitions stay bounded; clustering columns ordered for the reads that matter; denormalization applied deliberately, with the write paths to maintain it. The relational instinct to normalize first and query later is the single biggest source of Cassandra rework, and this is where we remove it.

Driver integration

Correct driver setup is half of perceived cluster performance: token-aware routing, prepared statements, sensible consistency levels per query (not one global default), retry and speculative-execution policies that fit your latency budget, and paging that doesn't quietly materialize a million rows.

Multi-datacenter topology

Replication strategy and datacenter layout designed for your actual failure domains and read locality — including LOCAL_QUORUM discipline, DC-aware load balancing, and the operational runbook for losing a datacenter and rejoining it.

Capacity planning

Node counts, storage sizing, and growth headroom computed from write rate, replication factor, compaction overhead, and retention — with the math shown, so finance and engineering are looking at the same numbers.

Vector and time-series workloads

Cassandra 5 added native vector types, and time-series remains one of the store's strongest fits. We design both — including TWCS-backed time-series tables with TTL-driven expiry, and vector search schemas on Cassandra 5 or Astra DB.

Engagements are hourly, project-scoped or staff augmentation, and typically pair one of our engineers with your team so the modeling discipline stays after we leave.