ETL & Data Pipelines
Analytics-ready data, landing on schedule, every day.
- From
- ₹1,10,000
- Timeline
- 4–6 weeks
Once reporting matters, querying production databases directly stops being viable — it's slow, it's risky, and the schema isn't shaped for analysis.
We build pipelines that land your operational data in a warehouse, modelled for analysis, with the monitoring that tells you when a load silently fails.
What we build
- Extraction from APIs, databases, files and SaaS platforms
- Incremental loading with watermarks and change detection
- Transformation and modelling for analytical queries
- Schema evolution handling when sources change
- Warehouse setup on Postgres, BigQuery, ClickHouse or Snowflake
- Pipeline monitoring, data freshness checks and alerting
- Historical backfill and reprocessing
Examples of this work
Concrete builds rather than capability statements.
Unified revenue warehouse
Consolidated Shopify, Amazon, Razorpay and the CRM into BigQuery, giving one revenue number instead of four that never matched.
What you get
- Deployed pipelines with scheduling
- Warehouse schema and documentation
- Freshness and quality monitoring
- Backfill of historical data
Tools we use here
- Python
- n8n
- PostgreSQL
- BigQuery
- ClickHouse
- dbt
ETL pipelines — questions we get asked
Do we need a warehouse at all?
Not if a few dashboards over a replica cover your needs. Warehouses earn their keep once you're joining data across several systems or your history is too large to query live. We'll say when it's overkill.
Often paired with
Want ETL pipelines working in your business?
Book a free 30-minute audit. We'll tell you what it would take, what it would cost, and whether it's worth doing at all.
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