ETL (Extract, Transform, Load) and ELT pipelines are the automated workflows that pull data from your source systems, clean and structure it, and deliver it to your data warehouse or downstream applications. We build pipelines that run on schedule, recover from failure, and alert before problems reach your reports.
Anyone can move a CSV once. Production pipelines are defined by what happens when the API times out at 03:00, when a source adds a column, or when yesterday's run needs to be replayed without duplicating a single row.
Connect to the source, pull the data, reshape it, land it in the warehouse. This is the part every tutorial covers and the part that takes the least engineering.
We handle authentication, pagination, and rate limits per source, and we extract incrementally: only what changed since the last run, so a growing business does not mean a growing bill.
This is the standard shape of a pipeline we ship. Click any stage to pin its description, or let it cycle.
These are the source families we connect most often. Custom and legacy systems are welcome: an odd API is an engineering problem, not a blocker.
Shopify, WooCommerce, Magento, BigCommerce. Orders, products, inventory, customers, and fulfillment data.
Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads. Spend, impressions, clicks, conversions, and ROAS.
GA4, server-side analytics, and custom event tracking. Sessions, funnels, attribution, and user behavior.
Salesforce, HubSpot, Kustomer, Zendesk. Leads, tickets, customer interactions, and lifecycle data.
SAP, Business Central, Dynamics 365, custom ERP systems. Transactions, inventory, logistics, and financial data.
REST and GraphQL APIs, SFTP file drops, webhooks, CSV and Excel imports, database replication.
Pipelines run on Airflow, Cloud Composer, Cloud Scheduler, or event triggers, with dependencies expressed explicitly. No cron scripts held together with hope.
Run history, row counts, durations, and error context in one place. When something breaks, the log tells you what, where, and why.
Failures retry with backoff. Persistent failures alert a human with context. Any historical window can be replayed with one command.
Pipeline code lives in git with review and rollback. Transformations are tested before they touch production data.
Source-to-target mapping, scheduling, ownership, and runbooks written down. The pipeline survives personnel changes.
Incremental extraction, partition pruning, and right-sized compute. Data movement should not be a mystery line on the invoice.
The difference is where transformation happens. Neither is universally better; we routinely mix both in one platform.
| Dimension | ETL (transform before load) | ELT (transform after load) |
|---|---|---|
| Where logic runs | In the pipeline layer, before the warehouse | Inside the warehouse, in SQL (dbt, Dataform) |
| Best for | Sensitive data that must be masked in flight, odd formats, operational destinations | Cloud warehouses with cheap compute and analysts who live in SQL |
| Transparency | Logic in code, reviewed like software | Logic in versioned SQL models, visible to the data team |
| Reprocessing | Requires re-running extraction or replaying staged data | Rebuild from raw tables at any time, no re-extraction |
| Our default | Ingestion edges and compliance-sensitive fields | Core business modeling on BigQuery and Snowflake |
Validation catches schema drift and alerts before bad data lands. We then patch the extractor, replay the affected window, and document the change. Sources evolving is normal; silent corruption is not.
Whatever the business case justifies: nightly is standard for reporting, hourly for operations, and streaming for events that cannot wait. Freshness is a design input, not an afterthought.
Yes, pipeline rescue is a common engagement: we audit, stabilize, add monitoring, and document what exists before improving it.
Both, on merit. A managed connector that works is not something to rebuild out of pride; a flaky or expensive one gets replaced with lean custom extraction.
List your sources and the reports they should feed. We will design the pipeline architecture and give you a clear build plan.