ETL & Data Pipeline Development
Data pipelines are the plumbing that moves information between your systems — from operational databases and marketplaces into your warehouse, and between the tools your business runs on. When they are reliable, everything downstream just works. When they are not, reports lie and syncs drift.
We build pipelines engineered like software: idempotent, retried, monitored, and tested — so data lands completely and on time, and failures raise alerts instead of silently corrupting a report.
Problems we solve
Silent failures
A job dies at 2am, no one is paged, and the data is stale or partial by morning. Pipelines without monitoring turn into invisible landmines.
Duplicates and drift
Re-running a job double-loads rows; a missed run leaves gaps. Without idempotency and reconciliation, the warehouse slowly diverges from reality.
Brittle, one-off scripts
Scattered cron scripts with no retries or logging are impossible to trust or hand off. Every change is a gamble.
How we approach it
Idempotent, retried, ordered
Every pipeline can safely re-run without duplicating data, retries transient failures, and processes in a defined order — so a hiccup does not corrupt downstream tables.
Monitored and alerted
Freshness checks, run status, and row-count reconciliation feed alerting, so you know within minutes if something did not land.
Batch or streaming, as the case needs
Scheduled batch for reporting, near-real-time streams for operational sync — chosen to fit the latency the use case actually requires.
What you get
- Source-to-target mapping and pipeline design
- Idempotent, retried, ordered pipelines (batch and/or streaming)
- Freshness, run-status, and reconciliation monitoring with alerts
- Backfill and replay tooling for corrections
- Logging, runbooks, and on-call-ready documentation
- Handover and ongoing support options
Technologies & integrations
Our delivery process
- 01Map
Document sources, targets, volumes, and the latency each use case needs.
- 02Build
Implement idempotent, retried pipelines with clear ordering.
- 03Instrument
Add freshness checks, reconciliation, and alerting.
- 04Backfill
Load history and add replay tooling for corrections.
- 05Operate
Document runbooks and hand over a monitored system.
Frequently asked questions
ETL or ELT?
Both have their place. We often load raw data first and transform inside the warehouse (ELT) for flexibility and auditability, but use in-flight transformation (ETL) when volume or privacy demands it. We pick per pipeline, not by dogma.
Do you do real-time pipelines?
When the use case needs it — operational sync between systems, for example. For reporting, scheduled batch is usually simpler and cheaper, so we match latency to need rather than defaulting to streaming.
What happens when a pipeline fails?
It retries transient errors, alerts on hard failures, and — because pipelines are idempotent — can be safely re-run or backfilled without creating duplicates.