Data Modernization
For teams on legacy systems that can't keep up. You've outgrown the spreadsheets, the legacy warehouse, or the ETL stack a vendor installed five years ago. Reports take days, engineers babysit pipelines, analysts don't trust the numbers. We move you onto a modern cloud-native platform without breaking the reports leadership runs every Monday.
Where we plug in
Audit your current state, design the target architecture, run the migration in parallel & cut over once the new system produces the same or better answers than the old one.
Deliverables
- Architecture diagram & migration plan
- Cloud warehouse stood up (Snowflake / BigQuery / Databricks)
- Pipeline rebuild (Fivetran / Airbyte / custom)
- dbt project with tested, documented models
- Parallel-run validation & cutover
- Runbooks, alerting & on-call handoff
Typical engagement
8–16 weeks. Fixed scope, fixed budget, weekly demos.
Common questions
How long does a data warehouse migration take?
Most of our migrations run 8–16 weeks end to end. The biggest variables are the number of source systems, how much undocumented logic lives in the old pipelines, and how many downstream reports need to keep working on day one. We scope all of that in a one-week discovery before quoting a timeline.
How much does a migration cost?
Most engagements land between $25K and $200K depending on source count and complexity. Every SOW is fixed-price, so the number you sign is the number you pay. We published a breakdown of the cost drivers on our Insights page.
Will our existing reports break during the migration?
No. We run the new platform in parallel with the old one and cut over only after the new system produces the same or better answers. The reports leadership relies on keep running the whole way through.
Which platform do you recommend — Snowflake, BigQuery, or Databricks?
It depends on your team, existing cloud footprint & workload mix. We don't take vendor referral fees, so no reseller margin shapes the answer. As a rough rule: BigQuery if you're already deep in GCP, Databricks if ML workloads dominate, Snowflake for most everything else.
Often paired with
Data Transformation
For teams whose data is messy, stale, or untrusted.
Learn more →Analytics & BI
For teams whose dashboards exist but nobody opens them.
Learn more →Data Strategy
For teams about to make a large data investment.
Learn more →AI Consulting
For teams that have run pilots and want production systems.
Learn more →Working on something like this?
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