Data pipeline and warehouse build

  • Services:
  • Industries: B2B SaaS analytics
  • Engagement: Build
  • Timeline: Q1-Q2 2024

About the project

A European Series-B analytics SaaS came to us with a scaling problem. Raw event data from thousands of customer accounts was piling into a single Postgres instance, and complex dashboard queries were timing out at peak load. This is a representative data engineering case study: we designed and built a multi-stage ingestion pipeline and a columnar warehouse layer on top of PostgreSQL with TimescaleDB. Query times dropped by about 65% and the client shipped two new reporting features inside the same quarter.

Background

The analytics platform had grown faster than its data layer. A monolithic Postgres schema tracked user events, aggregations, and reporting data in the same tables. At roughly 50 million events per day, ad-hoc analytical queries competed with transactional writes. The engineering team had patched the problem with caching, but cache invalidation logic had become a maintenance burden. The platform needed a proper separation between the OLTP write path and the OLAP read path, without a full rewrite of the product.

Need a data engineering team?
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We scope and build production-ready pipelines for analytics products.

What we built

A data engineering pipeline that separated the write path from the analytical read path for a high-volume analytics SaaS.

CDC ingestion layer Icon

CDC ingestion layer

Debezium captured every row-level change from the OLTP Postgres instance via logical replication. Events flowed to a processing queue with guaranteed at-least-once delivery and idempotent consumers.

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Incremental aggregations

TimescaleDB continuous aggregates rebuilt rollup tables on a five-minute refresh cycle. This removed the largest ad-hoc queries from the OLTP path entirely and kept dashboard data fresh without full recomputation.

Tenant-partitioned hypertables Icon

Tenant-partitioned hypertables

We partitioned hypertables by tenant ID and time range, so per-account queries scanned only the relevant chunks. Chunk exclusion kept query plans fast even as total data volume grew.

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Zero-downtime cutover

The migration ran alongside the live system for two weeks. We validated row counts and aggregate totals at each stage, then switched the product API to the new read layer during a low-traffic window with an instant rollback path in place.

Takeaway

This data engineering case study shows how separating an OLTP write path from an analytical read path can unblock a product team without a full rewrite. We bring an incremental approach: design the new layer alongside the live system, validate at every step, and cut over without downtime. If your platform is outgrowing a single Postgres schema, the same pattern applies.

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We pair deep technical expertise with the kind of ownership you'd expect from your own in-house crew.

  • Same team from discovery to launch
  • Daily standups, Slack, and full transparency
  • Senior-only engineering, design, and QA
  • Long-term collaboration, not staffing
10+ years in the field
EU-based UTC+0 → UTC+3
Senior-only no juniors
Alpina Tech Team

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