Your transformation logic lives inside a GUI that only your ETL tool’s license lets you open. Relevant Software’s engineers build ETL and logic for teams ready to migrate off licensed tools and own it outright.
Our engineers design the extraction logic for every source your pipeline pulls from, choosing incremental or full extraction based on what each source can actually support.
Business rules that decide how raw data becomes usable, currency conversion, field-level calculations, conditional logic, get written as code your team can read line by line.
Our architects decide how transformed data lands in your warehouse: full overwrite, incremental append, or upsert, matched to what each table actually needs. Where the warehouse it lands in still needs to be designed and built, our data warehouse development team handles that side.
For pipelines where transformation logic has outgrown what your ETL tool can efficiently process, we move that logic into the warehouse itself, closer to where the compute already lives.
When Informatica, SSIS, or Talend comes up for a license renewal, our engineers rebuild the same logic as code your team owns outright, with nothing left running on a per-core license.
Every transformation currently running inside your ETL tool gets documented, including logic nobody has looked at since it was built. You get a full map of what exists before anything gets rebuilt.
Extraction connectors and business rules get rebuilt as code, checked against the original mappings for parity. The logic transfers to your team as a build artifact they maintain independently.
Load logic gets tested against your actual data volumes and edge cases, the same conditions it runs under in production. Discrepancies against the legacy tool’s output surface before cutover.
The new pipeline runs alongside the legacy tool for a defined period, with outputs compared row by row. Cutover happens once the two outputs match, row for row.
Cutover happens once the parallel run confirms a match, and the legacy tool’s license becomes optional rather than required. Documentation transfers with it.
ISO 27001:2022 certified, GDPR covered, HIPAA-ready. Transformation logic carries a documented audit trail, so a compliance review has a clear answer instead of a black box GUI.
96% employee retention. The team that rebuilt your transformation logic is still there when the next source needs the same treatment.
98% client satisfaction. Most ETL migrations lead to the next pipeline engagement, not a one-off replatform.
We use AI-assisted development to cut delivery time by up to 50% reduce errors by up to 20%, with senior code review still covering every transformation rule before launch.
We’ve rebuilt fragile, manual data flows into pipelines that run unattended and stay owned by the client’s team, including high-volume environments.
Both, and migration off an existing tool is the more common starting point. We start with a full audit of your current mappings, including logic nobody has documented since it was built, so you know exactly what exists before anything gets rebuilt. From there, the mappings get rebuilt as code and run in parallel with your existing tool until the outputs match row for row.
Three different things live under this umbrella, and each solves a separate piece of the problem.
If the gap is the transformation logic itself, or a licensed tool is up for renewal, this page is the right starting point. If working pipelines already exist and need coordinating and monitoring at scale, data pipeline development covers that. If different systems still disagree on the same fact before any transformation happens, data integration is where that gets resolved.
Your team does, from day one. The extraction connectors, transformation rules, and load logic all transfer as code your engineers can read, modify, and run without a license tied to any vendor. If the engagement ends after cutover, the pipeline keeps running on infrastructure your team controls.
No, not if it’s sequenced correctly. The new pipeline runs in parallel with the legacy tool until outputs match row for row, so cutover happens once we’ve verified correctness, not on a deadline that risks a production gap. Timeline depends mostly on how much undocumented logic the audit turns up: a tool with clean, documented mappings migrates faster than one with years of unreviewed custom rules. Contact us with which tool you’re on and roughly how many mappings it runs, and we’ll turn that into a scoped timeline within 14 days.
The deciding factor is where the transformation load belongs. ELT fits when your warehouse has compute to spare and most of your logic can run as SQL your analysts own. A separate ETL step still earns its place when transformations are too heavy to sit in query paths, have to run before data lands for compliance or deduplication, or pull from sources the warehouse can’t read directly. We assess which of your tables fall on each side instead of moving everything one way, so the split matches your actual workload.
Scattered across a GUI, half-documented in a wiki nobody updates, or correct but impossible to verify without opening the tool itself: our team turns what’s there into code your engineers can read.
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