Project Name
Kafka Tiered Storage Decoupled Broker Scaling From Compliance Retention for a Financial Services Firm
A mid-to-large financial services firm under multi-jurisdictional compliance frameworks mandating 12 to 24 months of event-level retention was scaling its Kafka broker fleet for retention rather than throughput. Every new regulatory obligation extended the cluster. All retained data, whether accessed every second or untouched for 18 months, lived on the same broker tier. Applying its AI-First approach, Ksolves redesigned the retention architecture using Apache Kafka Tiered Storage, decoupling broker scale from retention, enabling 12- to 24-month compliance retention on object storage, and delivering full audit replay without duplicating infrastructure.
- Broker Fleet Scaling Driven by Retention Not Throughput: Compliance obligations required 12 to 24 months of retention across payment flows, trade records, and audit logs. Brokers were scaled to accommodate data volume rather than processing demand, making the cluster unnecessarily large.
- No Separation Between Active and Archive Data: All retained data lived on the same high-performance broker tier with no mechanism to demote aged partitions to a long-term storage layer while keeping them accessible to consumers.
- Audit and Compliance Replay Was Fragile: Retrieving historical event data for regulatory audits required either retaining everything on brokers indefinitely or maintaining a separate secondary archive, duplicating infrastructure and introducing consistency risk.
- DR Gaps for Long-Retention Topics: Cross-region replication had not been extended to long-retention topics, leaving regulatory datasets unprotected against a primary region outage.
- Schema Evolution Risk on Long-Lived Data: Without a governed schema management layer, Kafka data retained for 18+ months had no guarantee of remaining readable as producer schemas evolved.
- No Retention Policy Observability: Infrastructure teams had no visibility into per-topic tiering rates, object storage consumption, or offload health, making it impossible to validate retention policies before a compliance deadline.
Ksolves designed a Tiered Storage architecture for Apache Kafka that separates real-time throughput from long-term compliance retention. Recent partitions stay on the broker tier for sub-second access. Aged data is automatically offloaded to object storage, remaining fully readable to all consumers with no application changes. The governing principle: broker cluster size reflects throughput requirements, not retention obligations.
- Kafka Tiered Storage Configuration: Partition segments older than a defined hot-tier window (6 to 48 hours, tunable per topic) are automatically offloaded to AWS S3, Azure Blob, or MinIO for the full compliance period (12 to 24 months). Consumers fetch from either tier via the standard Kafka consumer API with no code changes.
- Object Storage Lifecycle and Retention Policies: Per-topic lifecycle policies governing retention duration, cold storage archival, and purge on expiry. Regulatory schedules enforced as infrastructure policy. AES-256 encryption at rest, TLS in transit.
- Schema Registry Integration: Avro schema contracts enforced at the producer level. Events are schema-versioned and backward-compatible. Cold-tier data retained for 18+ months remains readable regardless of schema evolution.
- Kafka MirrorMaker 2 Cross-Region Replication: Replication topology extended to include tiered-storage topics. Hot broker data and object storage offload configurations mirrored to a secondary region. Regulatory datasets recoverable in a full primary region outage.
- Prometheus and Grafana Observability: Dashboards covering broker health, tiered offload rates, hot vs cold fetch latency, object storage consumption by topic, and retention policy compliance from a single pane.
Technology Stack
| Category | Technology |
|---|---|
| Messaging | Apache Kafka (Tiered Storage enabled) |
| Object Storage | AWS S3 / Azure Blob / MinIO |
| Observability | Prometheus + Grafana |
- Broker Scaling Decoupled From Retention: The broker cluster is now sized for throughput only. Retention windows of 12 to 24 months served from object storage with no broker capacity impact.
- Compliance Retention as Policy Not Infrastructure: Full retention window enforced via per-topic lifecycle policies. Extending retention requires a policy update, not a cluster change.
- Zero Consumer Disruption: Tiered Storage is transparent to all consumers. Historical reads served from object storage via standard Kafka API with no code changes and zero downtime.
- Audit Replay From Days to Hours (Target): All retained data accessible via standard Kafka consumer APIs. Audit replay requests that previously required days of assembly now complete within hours.
- Full DR for Compliance Datasets: MirrorMaker 2 replication extended to all tiered topics. Compliance event data recoverable from the secondary region within RTO/RPO targets.
“Our compliance team used to treat Kafka retention as an infrastructure negotiation. With Tiered Storage, it is a policy setting. We configure the retention window we need and the architecture handles the rest.”
-Head of Compliance Technology.
A financial services firm scaling its Kafka broker fleet for compliance retention rather than throughput was given a clean, scalable retention architecture through Ksolves Big Data services. Kafka Tiered Storage now offloads aged partitions to object storage automatically. The broker tier is sized for real-time throughput. Compliance retention of 12 to 24 months is served transparently with no consumer code changes. Retention obligations are met as policy configuration, not infrastructure investment.
Is Your Kafka Cluster Scaling for Compliance Retention When It Should Be Scaling for Throughput?
