Every migration plan your team has drafted has died at the same three fears: lost data, broken reports, and a business that can’t stop while the warehouse relocates. Relevant Software migrates data warehouses with parity as the success criterion — every figure reconciled against the old system, a parallel run before cutover, and a rollback path at every stage.
Assessment maps schemas, pipelines, scheduled jobs, the reports that depend on them, and the undocumented ETL logic.
On-premise warehouses — Teradata, Oracle, Netezza, SQL Server — move to Snowflake, BigQuery, Redshift, Databricks, or Microsoft Fabric with the schema, history, and reporting intact.
A warehouse already in the cloud moves between platforms — Redshift to Snowflake, Synapse to Fabric, BigQuery to Databricks — when pricing, performance, or strategy outgrows the first choice. If the target platform is still an open question, data warehouse implementation services cover the scored selection; migration starts once the destination is named.
Conversion rewrites what the new platform can’t run as-is: SQL dialects, stored procedures, and ETL jobs, translated and tested rather than emulated.
Validation proves the move: row counts, checksums, and report-level regression against the legacy system, documented so finance can sign the numbers.
Both warehouses run live against the same sources until the new one has passed the agreed checks — then cutover happens per consumer group, with the old platform still running as the rollback.
The migration ends when the new platform is tuned and the old one is switched off — not when the data lands.
Schemas, pipelines, jobs, reports, and their dependencies go into a single inventory — including the transformation logic nobody has documented. You see the real size of the move before committing to it.
The plan fixes the approach per workload — rehost or replatform — with effort, order, and a rollback path for each stage. If the honest answer for your warehouse is re-architecture rather than relocation, we will guide you through data warehouse modernization.
Schema and pipeline conversion runs through our data engineering services — every converted object tested against the outputs of the legacy system, every merge passing senior review. Nothing ships on the assumption that translated code behaves like the original.
The new platform runs against production sources alongside the old one, with reconciliation checks comparing them daily. Exit criteria are agreed up front — the parallel run ends by evidence.
Consumer groups move in stages, each with a checkpoint and a way back. The old platform remains fully operational until the last report has moved and held.
The final parity evidence lands in writing, the new platform gets tuned to your workloads, and the legacy estate is retired on a named date — licenses, hardware, and support contracts included.
Every cutover stage ships with a tested way back — the old platform remains in service until the last consumer group has moved and held.
Parity is proven line by line and handed over in writing — row counts, checksums, and report regression against the legacy system.
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.
ISO 27001-certified processes; GDPR and HIPAA controls carried through the move, not rebuilt after it.
99% of projects delivered on time — including the date the old platform gets switched off.
96% employee retention — the engineers who run your inventory are the ones standing by at cutover.
Yes — that’s what the parallel run exists for:
A check worth running now: list the reports your business genuinely can’t lose for a day. That list defines the cutover order — and it’s shorter than most teams assume.
Loss hides in three places, and each gets its own check:
The evidence lands as a written reconciliation report your finance team can review. A preview you can run today: pick one number your board relies on, compute it on the current warehouse, and keep the query — any migration partner should be able to reproduce it on the new platform on demand.
Three counts set the timeline:
The assessment turns those counts into a staged plan with dates — and 99% of projects deliver on the dates the plan commits to. Worth doing before any vendor call: count your scheduled jobs and the reports actively used in the last quarter.
Four drivers set most of the budget:
All four are countable from your current estate before anyone quotes you. Bring the counts to a scoping call and the estimate starts from your numbers — contact us, and what you’ll get back first is an assessment read.
Yes — undocumented logic is the default state of legacy warehouses, not an edge case. The assessment reconstructs what each pipeline actually does from the code, schedules, and outputs, then puts that knowledge into version control, so it survives the move and every departure after it.
The self-check: count the pipelines nobody currently on the team can explain. That number is the strongest argument for migrating deliberately now rather than urgently later — reconstruction is possible while the system still runs; after a shutdown, it becomes far harder and costlier.
Separate the two honestly:
The diagnostic: if your reports are right but slow and expensive — migrate as-is; if the reports themselves are contested — the warehouse needs re-architecture (that’s modernization, not migration), or, when the model is broken at the root, a rebuild through data warehouse development services on the new platform.
Your team’s involvement is limited and specific. The migration needs two roles from your side:
The engineering itself — inventory, conversion, parallel run, cutover — can run entirely on our side, embedded in your process, or through a dedicated team that owns the migration end to end. The assessment names the involvement level per stage, so your team’s calendar is part of the plan. Whichever split you choose, the reconciliation evidence and documentation land with you.
As two workstreams on one calendar. The warehouse move runs on its own discipline — parity validation, parallel run, staged cutover — because “the reports still match” is a stricter bar than “the server came up.” What the workstreams share are dependencies: the landing zone, networking, and identity setup have to exist before the warehouse can land on them. Check the sequencing before committing dates: ask whoever runs the estate program when the landing zone reaches production-ready, and plan the warehouse cutover after that milestone, never on it. The estate-level workstream — applications, infrastructure, and landing zones — is cloud migration consulting scope; the warehouse workstream runs its own parity discipline, and the two share a calendar.
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