Analytics that arrive before the user does.
ClickHouse, Druid, Pinot and the streaming infrastructure underneath them. Sub-second analytics on fresh data — for product surfaces, operational dashboards and the customer-facing experiences they power.
The problem we solve
Once a product needs analytics surfaced inside the product itself — usage dashboards for customers, real-time operational views, alerting on user behaviour — warehouse-grade tools (10-second queries) stop being acceptable. Building a real-time analytics layer is a specialist discipline: streaming ingestion, columnar engines, materialized views and concurrency tuning.
What we ship
- 01Streaming ingestion: Kafka, Redpanda, Kinesis to columnar stores
- 02Real-time columnar databases: ClickHouse, Apache Druid, Apache Pinot
- 03Materialized views and incremental aggregation
- 04Stream processing: Materialize, Bytewax, Flink
- 05Customer-facing embedded analytics
- 06Operational dashboards with sub-second refresh
- 07Concurrency tuning for many simultaneous users
- 08Cost-aware partitioning and TTL strategies
What you receive
- Production real-time analytics stack with documented invariants
- Embedded dashboard or operational view shipped in your product
- Performance and concurrency baseline
- Runbook for the failure modes specific to streaming
Stack we reach for
Ideal for
- → Products shipping customer-facing analytics inside the app
- → Operations teams running on live dashboards (logistics, support, marketplaces)
- → Trading and finance interfaces requiring fresh data
- → Companies whose warehouse can't keep up with operational queries
How an engagement runs
- 01
Workload mapping
Query patterns, freshness requirements, concurrency profile — written down before architecture.
- 02
Architecture
Streaming → columnar → API → UI stack chosen for your specific shape.
- 03
Implementation
End-to-end pipeline built and tuned to meet the targets.
- 04
Operate
Observability, runbooks, on-call handoff.
How to engage
Feasibility Sprint
Prototype on your real data demonstrating sub-second latency at concurrency.
Real-time Analytics Build
Production stack with embedded dashboards and documentation.
Frequently asked.
01ClickHouse vs Druid vs Pinot?
ClickHouse for most modern teams — best ergonomics, broadest community, excellent performance. Druid where deep operational maturity is required. Pinot for specific Linkedin-scale use cases. We'll match the engine to your problem.
02Can we just use our warehouse?
For internal analytics, usually yes. For sub-second user-facing queries at concurrency, no — warehouses aren't designed for that workload. We'll tell you when each is appropriate.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
Start a conversation