How do you move twenty years of history onto Snowflake with every balance intact? Relevant Software scores every object for conversion complexity, loads history stage by stage, and reconciles each table against the legacy platform before the next one starts.
Assessment counts what actually has to move: schemas, stored procedures, pipelines, reports, and the dependencies nobody documented. The output is a sequenced plan with each stage priced and dated.
Teradata, Oracle Exadata, Netezza, and SQL Server estates carry decades of logic, so each object moves under a plan that keeps the original output as the reference. When the target platform is still open or points somewhere other than Snowflake, our data warehouse migration work scores the destinations first.
A warehouse already running in the cloud moves for pricing, performance, or consolidation reasons, and Redshift, Synapse, and BigQuery each bring their own conversion quirks.
Tooling converts the bulk of standard SQL quickly, and the value sits in what it leaves behind: stored procedures, vendor-specific functions, and BI logic that our engineers re-engineer by hand.
Feeds pointed at the old warehouse need a new destination, so our team rebuilds the ingestion and transformation on Snowflake and runs them alongside the originals until the outputs agree.
Reconciliation is what turns a completed migration into a signed one, so row counts, balances, and report totals get compared between the two platforms until they agree.
Both warehouses stay live while your analysts compare outputs, then consumer groups move onto Snowflake in stages with a rollback available at each one.
Every schema, object, pipeline, and report gets counted and scored for conversion complexity, so the plan rests on the estate you actually have.
Stages arrive priced and dated, each carrying its own rollback, and your sign-off opens the next one.
Schemas, SQL, and stored procedures land in Snowflake, then every object runs against the output it produced on the old platform.
Rebuilt ingestion and transformation, delivered by data engineering team, feed Snowflake on schedule while the legacy warehouse keeps serving the business.
Both platforms answer the same questions for an agreed window, and the teams who own the reports confirm the figures match.
Consumer groups move across in stages, the reconciliation report goes to your finance and analytics leads, and the legacy platform retires on your word.
Every table and every report gets compared across both platforms, and the reconciliation report carries your team’s signature before the legacy warehouse retires.
Each stage ships with a defined way back, so the legacy warehouse keeps serving the business until your analysts confirm the Snowflake figures.
14 days from the first call to a phased migration plan your finance team can read.
The Relevant AI Delivery Framework™ cuts delivery time by up to 50% with up to 20% fewer bugs through AI-assisted development, with every release still passing senior code review.
92% senior engineers and 96% employee retention keep the people who inventory your estate on it through cutover.
ISO 27001-certified processes, with GDPR and HIPAA controls carried across to the Snowflake access model during the move.
Standard DDL and analytic SQL convert well with tooling, and that covers a large share of a typical estate by object count. The remainder is where migrations run long: stored procedures, vendor-specific functions, macros, and BI-layer logic that no converter reads. Our engineers rebuild those and test each one against its legacy output.
No, your reporting stays available throughout. Both warehouses run in parallel for an agreed window: the legacy platform keeps serving the business while Snowflake loads the same data through rebuilt pipelines, and your analysts compare the two on their own reports. Consumer groups then move across in stages, each with a checkpoint, so exposure stays small and reversible.
A useful drill before you commit to any plan: pick your three most business-critical reports and name who signs off that each one is correct. Those names become the parallel-run reviewers. If ownership of a report is unclear right now, our team helps you establish it as part of the planning stage.
Yes, we can, and partly finished migrations are a common starting point. Our architects audit what already moved, what still runs on the legacy platform, and whether the converted objects reconcile, then produce a completion plan with the remaining work priced and dated. Plenty of stalled migrations keep most of their converted schemas and pipelines, and the honest answer is sometimes that finishing costs far less than restarting.
The two answer different questions, and picking the wrong one costs a year. Migration suits an estate where the platform itself is the constraint, through licence cost, scaling limits, or a data centre you are leaving. Re-architecting in place suits an estate where the platform still fits and the gaps sit in modelling, freshness, or governance, and our data warehouse modernization team scores each workload for that decision.
A quick self-check: list your three biggest complaints about the current warehouse. If they read as cost per query, capacity ceilings, or an end-of-life contract, migration addresses them. If they read as stale data, disputed metrics, or reports nobody trusts, those follow you onto Snowflake unless the modelling changes too. When your list mixes both, our architects split it and price each part.
For most estates moving off a fixed licence the run rate drops, and the two variables that decide your case are measurable before you commit.
Snowflake charges by the second and prices storage separately, so an estate with concentrated reporting windows tends to land below a fixed licence, while an estate querying around the clock earns its saving through sizing and tuning instead.
Our engineers model both sides during the assessment: your current annual platform cost including licences, support, and hardware refresh, against a projected Snowflake bill built from your real query profile. The projection assumes the guardrails are in place, warehouse sizing, auto-suspend, and resource monitors, so the number reflects a governed platform.
Relevant Software turns your sources, reports, and dependencies into a staged plan with reconciliation built into each step.
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