ScyllaDBApache CassandraDynamoDBRedisFoundationDB
NoSQL & Wide-Column Engineering
Production Scylla and Cassandra deployments for time-series, IoT, and high-throughput workloads. We design and operate wide-column stores with data modeling, performance tuning, and migration discipline.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
// Vector index performance
$ pinecone describe-index --name prod-embeddings
✓ Vectors: 12.4M · Dimensions: 1536
✓ Query latency p99: 42ms
✓ Replicas: 3 · Pods: 6
Wide-Column Stores at Production Scale
We engineer Scylla and Cassandra systems that handle time-series ingestion, IoT telemetry, and high-throughput transactional workloads: from data modeling through multi-datacenter operations.
Typical engagement starts when
- write volume has outgrown relational databases and the team needs a storage layer that scales horizontally without query redesign
- a Cassandra cluster exists but performance has degraded: compaction storms, read latency spikes, or tombstone buildup
- the organization is evaluating Scylla as a Cassandra replacement and needs migration planning with production validation
- data modeling decisions made during prototyping are now causing hot partitions, query inefficiency, or operational headaches
What We Build
| Capability | What We Deliver |
|---|---|
| Data modeling | Partition key design, clustering columns, and denormalization patterns for query-first modeling |
| Cluster operations | Multi-DC replication, rack-aware placement, rolling upgrades, and repair scheduling |
| Performance tuning | Compaction strategy selection, cache tuning, and read/write path optimization |
| Migration | Zero-downtime migration from Cassandra to Scylla, or from relational databases to wide-column stores |
Engineering Standards
| Standard | What It Protects |
|---|---|
| Partition sizing review | Prevents hot partitions and oversized access paths |
| Compaction strategy matched to workload | Read-heavy, write-heavy, and time-series patterns get different treatment |
| Repair scheduling | Consistency behavior is planned before repair debt accumulates |
| Multi-DC consistency-level design | Latency and consistency trade-offs are explicit per access pattern |
| Metrics exported to Prometheus and Grafana | Compaction pressure, read latency, and heap behavior stay visible |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| High-throughput time-series data with TTL-based expiration | Scylla with TWCS compaction + CDC for downstream processing |
| Cassandra cluster with degraded performance (compaction, latency, tombstones) | Cluster audit + remediation sprint (2-4 weeks) |
| Evaluating Scylla migration from existing Cassandra deployment | Migration assessment + phased cutover plan |
| IoT or telemetry workload that needs horizontal scaling with no single point of failure | Multi-DC Scylla deployment with rack-aware replication |
| Need key-value caching with persistence and cluster replication | Redis Cluster or DynamoDB depending on cloud constraints |
| Semantic search or vector retrieval, not wide-column storage | Vector & Graph Databases: Pinecone, Weaviate, Neo4j |
Common failure patterns we fix
- partition keys chosen for entity identity rather than query access pattern, causing hot partitions and uneven load
- tombstone accumulation from DELETE operations without understanding gc_grace_seconds and repair cycles
- compaction strategy left on defaults (STCS) for time-series workloads that need TWCS
- repair never scheduled or scheduled beyond gc_grace_seconds, causing data resurrection and consistency drift
- Cassandra-to-Scylla migration attempted without validating driver compatibility, timeout settings, and consistency level behavior
What you leave with
- data model validated against actual query patterns with partition sizing and access path documentation
- cluster operations runbook: repair schedules, compaction monitoring, rolling upgrade procedures
- performance baseline with Prometheus/Grafana dashboards and alerting thresholds
- migration plan (if applicable) with rollback procedures and dual-write validation strategy
Best Fit
- Team has high-throughput write workloads that have outgrown relational databases
- Organization runs Cassandra and needs operational expertise or Scylla migration
- Workload is time-series, IoT, or event-driven with predictable query shapes
- Engineering team is ready to operate distributed systems with monitoring and runbooks
Depth of Practice
Our team has operated Cassandra and Scylla clusters across healthcare anomaly detection, real-time event processing, and IoT telemetry platforms. Production deployments include multi-DC topologies, high-throughput write paths, and migration planning between Cassandra-compatible systems.
Next Step
Discuss your NoSQL & Wide-Column Engineering path
Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.
No SDRs. A Principal Engineer reviews every submission.