ETL (Extract, Transform, Load) transforms data before loading into the target. ELT (Extract, Load, Transform) loads raw data first, then transforms inside the data warehouse. ELT is generally preferred for modern cloud data warehouses like Snowflake and Databricks because it’s more flexible and leverages cheap cloud compute. HST builds both we recommend based on your specific requirements.

Simple pipelines (single source, basic transformation) can be production-ready in 1-2 weeks. Complex pipelines (multiple sources, business logic, data quality checks) typically take 4-8 weeks. Full pipeline modernisation projects (replacing legacy SSIS, migrating to cloud) take 3-6 months depending on scope.

Yes. We mirror your stack if you’re on Azure with Data Factory, we work in Azure with Data Factory. We don’t force technology changes that add risk. We modernise incrementally, proving value before expanding scope.

Data quality is built into every pipeline we build. We implement automated checks using tools like Great Expectations or dbt tests, set up alerting for failures, and create runbooks for common issues. Poor data quality is usually a pipeline design problem, not a tooling problem.

Absolutely. About 40% of our engagements start with stabilising existing pipelines before building new ones. We audit what you have, identify failure points, and fix them while documenting everything so your team can maintain it.

7–10 business days from signed agreement to engineer embedded in your team.