A warehouse migration is the riskiest project most data teams will run all year — and the one they've never done before. We've moved data platforms at Facebook, BP, and Comcast scale, and we bring that same discipline to your migration to Snowflake: every table accounted for, every number reconciled, and a cutover your business doesn't feel.
The problem
You're here because the status quo is getting expensive. Maybe your on-prem warehouse is out of headroom, your Teradata or Oracle licensing is bleeding cash, your Hadoop cluster is a maintenance tax nobody wants to own, or your Redshift bill scales faster than your business does. Snowflake is the obvious destination — but the path there is where projects die. Schema and SQL that don't translate cleanly. Pipelines that have to keep running while you move. Stakeholders who will notice the instant a dashboard number shifts. Done wrong, a migration is six months of risk for a platform nobody trusts at the end. Done right, it's a one-time, well-scoped project that lowers your bill and unblocks everything after it.
Who this is for
Teams running a legacy or first-generation cloud warehouse — Teradata, Oracle, Netezza, SQL Server, on-prem Hadoop/Spark, or Redshift — who want to land on Snowflake with confidence rather than crossed fingers. Most of our clients are mid-market and growth-stage companies without a large platform team to spare for a year-long internal project.
What you get
A production-ready Snowflake environment with your data migrated and reconciled row-for-row against the source, your pipelines and transformations rebuilt on the new platform, your reports and dashboards repointed and verified, a documented cost and performance baseline, and a team that's been trained and shadowed until they own it. No dependency on us afterward — that's the point.
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How the migration works
A phased engagement with a decision gate after each step, so you're never committed to a path you can't see the end of.
1. Assess (1–2 weeks)
We inventory every source object, pipeline, and downstream report, map dependencies, flag what's obsolete, and size the effort. You get a written migration plan with scope, cost, risk, and sequencing per wave — yours to keep whether or not we continue.
2. Convert
Schema, data types, and SQL/stored-procedure logic translated to Snowflake — automated where it's safe, hand-reviewed where it matters. We modernize as we go (warehouse sizing, partitioning/clustering, file formats) instead of lifting-and-shifting your old problems.
3. Validate
The step most migrations skip and later regret. We reconcile row counts, aggregates, and business-critical metrics between old and new, automatically, so you can prove the numbers match before anyone trusts a dashboard.
4. Cut over
A rehearsed, low-drama switch with parallel-run and rollback options, scheduled around your business so reporting and pipelines keep working through the transition. No "big bang" weekend prayers.
5. Handover
Documentation, runbooks, cost-optimization tuning, and hands-on training until your team is self-sufficient. We answer questions for 90 days after cutover.
How we de-risk it
Migrations fail in predictable ways, so we plan for them up front. Data that doesn't match gets caught by automated reconciliation, not by an angry stakeholder. Hidden dependencies get found in the assessment, not at cutover. Runaway cloud spend gets controlled with right-sized compute and monitoring from day one. And the project never becomes a black box — you get weekly demos and a fixed, per-phase scope you approved.
Frequently Asked Questions
How long does a migration take?
Most mid-market migrations run 6–14 weeks depending on data volume, pipeline complexity, and how much legacy SQL needs translating. The assessment gives you a real number before you commit.
Will our reporting break during the migration?
No. We keep source systems running and use parallel-run and validation so dashboards stay live until the new platform is proven.
Snowflake or Databricks — which should we pick?
If you're mostly BI, analytics, and SQL workloads, Snowflake is usually the simpler fit. If you're combining analytics with heavy ML/AI and data-science workloads, Databricks' lakehouse often wins. The assessment includes a platform recommendation if you haven't decided.
Do we need a data team to maintain it afterward?
Not a large one. The whole engagement is built around handing you a platform your existing team can run, with the documentation and training to do it.
What does it cost?
Fixed-scope pricing set per phase after the assessment — no open-ended hourly billing. Most full migrations land between $35,000 and $150,000 depending on data volume, pipeline complexity, and the number of source systems; the assessment gives you a firm number before you commit.
Start with a free 30-minute migration assessment.
Bring your current platform and your three biggest worries about moving. You'll leave with a candid plan and a real scope — even if you never hire us.
Book a free migration assessment