DevOps Solutions
DevOps for Analytics Platforms
Analytics platforms ingest millions of events per day, store terabytes of time-series data, and serve complex queries with sub-second response times. We implement the infrastructure stack for analytics products — event ingestion pipelines, columnar storage, query engines, and auto-scaling that handles both real-time dashboards and heavy batch queries.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Analytics Infrastructure Challenges
Analytics platforms face a unique combination of high-write (event ingestion), high-read (dashboard queries), and high-storage (historical data) demands. Events must be ingested without loss. Queries must be fast despite scanning millions of rows. Storage costs must be managed as data accumulates. Real-time and batch workloads compete for resources.
Event Ingestion Pipeline
We deploy high-throughput event ingestion via Kafka, Kinesis, or custom HTTP collectors. Events are validated, enriched, and routed to appropriate storage. Ingestion handles burst traffic with buffering — no events are dropped during spikes. Schema registry enforces event structure. Dead-letter topics capture malformed events for investigation.
Storage Architecture
Hot data (recent events, frequently queried) lives in ClickHouse, TimescaleDB, or Apache Druid for fast analytical queries. Warm data moves to columnar formats (Parquet) on object storage. Cold data is archived with lifecycle policies. Materialized views and pre-aggregated rollups accelerate common query patterns. Total storage cost is optimized across tiers.
Query Performance
We optimize query engines for your access patterns: partitioning by time for time-series queries, indexing by tenant for multi-tenant analytics, materialized views for dashboard queries, and query caching for repeated patterns. Resource isolation prevents heavy batch queries from impacting real-time dashboard performance. Query governors prevent runaway queries from consuming all resources.
Multi-Tenant Isolation
If your analytics platform serves multiple customers, each tenant's data is isolated via row-level security, separate tables, or schema-per-tenant depending on your scale. Query concurrency limits prevent one tenant's heavy queries from affecting others. Storage quotas and usage metering track per-tenant resource consumption.
How It Works
Purchase the engagement, submit your async brief with your event volume, query patterns, and retention requirements, and receive a complete analytics infrastructure implementation within 10–14 business days.
Why Anubiz Engineering
100% async — no calls, no meetings
Delivered in days, not weeks
Full documentation included
Production-grade from day one
Security-first approach
Post-delivery support included
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.