Typical enterprise migrations take 3-6 months depending on complexity. Our 12-week programme delivers pilot workloads by week 6 and production cutover by week 12. Very large migrations (petabyte-scale, hundreds of dependencies) may take 6-12 months. We scope realistically no 18-month discovery phases.

It depends on your use cases. Snowflake excels at analytics, BI, and data sharing — it’s easier to manage and has excellent governance. Databricks excels at unified analytics plus machine learning — it’s more flexible but requires more engineering. If you’re pure analytics/BI, Snowflake. If you’re doing ML and want lakehouse architecture, Databricks. We help you decide based on your actual needs.

We convert SSIS packages to modern equivalents typically dbt for transformation logic and Airflow or ADF for orchestration. We don’t do a “lift and shift” of SSIS to the cloud (that just moves the problem). We convert incrementally, validating that outputs match before decommissioning old packages.

Yes. We always run parallel systems during transition. Old warehouse keeps running while we build and validate the new one. We only cutover when the new system is proven and stakeholders are confident. Regulatory deadlines don’t move — we don’t take risks with your production data.

Data quality validation is built into every migration. We create automated comparison checks between source and target, validate row counts, checksums, and business logic. If something doesn’t match, we investigate before proceeding. Migration is the perfect time to clean up data quality issues but we do it intentionally, not accidentally.

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