Data Engineering & Pipelines
Billions of rows. Sub-second queries. No Spark cluster required.
Real-time ingest pipelines, ETL/ELT transformation layers, columnar data warehouses, and analytics dashboards. We run 747 million rows of market data in ClickHouse with p95 query times under 200ms and 5 to 10x compression over raw storage. Same architecture, applied to your domain.
723M+
Rows in live production data systems
< 200ms
p95 query time on billion-row tables
5 to 10x
Storage compression vs row-oriented databases
TECHNOLOGY
Tech stack
CAPABILITIES
What we build
01
Ingest pipeline architecture
Event-driven connectors in Rust and Python with exactly-once delivery semantics, per-row schema validation, deduplication on natural keys, and dead-letter queues for malformed records. We have built ingest pipelines processing 11 concurrent exchange feeds at sub-50ms tick latency.
02
Columnar data warehouse design
ClickHouse for high-throughput analytics with the right partitioning key and ORDER BY for your dominant query shape. We migrated a production system from QuestDB to ClickHouse and achieved 5x better compression and 2 to 5x faster analytical queries on the same hardware.
03
Real-time streaming
Redis Streams for at-least-once delivery with consumer groups, Kafka for high-throughput durable logs, and custom WebSocket fan-out for client-facing real-time data. Backpressure handling and lag monitoring configured before go-live.
04
Data quality and lineage
Row-count reconciliation between source and sink, schema drift detection, and automated backfill on connector restart. We added a validation gate to a production pipeline after 2,810 corrupt partition events were written by a previous system. Gate has caught zero bad rows since.
FRESHNESS
Data freshness contract
Every table has a freshness SLA. Every SLA has a monitor. When the lag alarm fires, the on-call engineer knows which downstream consumer is affected before opening the dashboard.
| Table type | Update cadence | Alert threshold | Monitor cadence |
|---|---|---|---|
| Tick data | Streaming | Lag > 5s | Every 10s |
| Minute bars | 1 min rollup | Missing 2 consecutive bars | Every 60s |
| Daily bars | Post-close + 30 min | No row by 18:00 ET | Hourly after close |
| Macro series | Source release schedule | 24h past expected release | Every 6h |
| Reference data | Daily 04:00 UTC | Row count drift > 1% | Daily after refresh |
| Aggregates | Materialized on insert | View lag > 30s vs source | Every 60s |
METRICS
By the numbers
723M+
Rows in live pipelines
< 200ms
p95 query at billion-row scale
100%
Schema and pipeline ownership
2 wks
Pipeline to production
APPLICATIONS
Where this applies
- 01Market data warehouse at scale. 723M+ rows across global equity, crypto, and macro markets in ClickHouse. Daily ingest from 11 exchange feeds and 50+ macro series. p95 query time under 200ms for full-table scans used by the signal generation layer.
- 02Replacing manual Excel workflows. A fund administrator ran 14 weekly reports from spreadsheets with manual copy-paste from 4 systems. We built an automated pipeline that publishes the same 14 reports every Monday morning, zero human touches, with a reconciliation check that flags any source data anomaly.
- 03Customer data platform. Consolidated event streams from Segment, Stripe webhooks, and a mobile SDK into a single ClickHouse table. Marketing got accurate cohort retention curves for the first time. Time-to-insight on new segments dropped from 2 days to 10 minutes.
- 04Real-time operations dashboard. An ops team needed live visibility into order status across 3 fulfillment systems. We built a Redis-backed aggregation layer with a WebSocket dashboard that shows current queue depths, SLA breach alerts, and per-warehouse throughput at 1-second resolution.
PROCESS
How we deliver
Every engagement follows the same three phases. No surprises, no scope creep.
Source Audit + Schema Design
We inventory every upstream data source, assess quality and latency, and design a normalized schema with partition strategy optimized for your query patterns.
Pipeline Build + Validation Gates
Ingest connectors and transformation logic built with per-row validation, deduplication, and dead-letter queues so no corrupt data reaches production tables.
Production Deploy + Observability
Pipeline runs under process supervision with lag monitoring, row-count alerts, and automated backfill on connector restart. Full schema and code ownership transferred.
GET STARTED
Ready to build?
Most projects ship in 2 to 4 weeks. Fixed price. Full IP transfer.