The kind of work we take on.
Four patterns that come up again and again. If one of them sounds like your situation,
the conversation will be short and useful.
These are engagement patterns rather than named client projects. Bytewave publishes
client work only with written permission, and does not quote numbers it cannot evidence.
Reporting migration
An SSRS estate nobody could safely turn off
Hundreds of reports accumulated over a decade, an unknown number still in use, and a server well past its support date. The blocker is never the conversion. It is proving which reports still matter and that the replacements agree with the originals.
- Approach
- Usage telemetry first, so retirement candidates are identified before any rebuild starts
- Build
- Interactive Power BI for the reports people explore, paginated for the ones that get printed or posted
- Proof
- Automated row and total comparison between old and new for a full reporting cycle
Cloud migration
On premises SQL Server, moved without a freeze weekend
A database estate that had grown past the hardware, with a business that could not tolerate a long outage. Serverless is attractive on spiky workloads and expensive on steady ones, so the sizing decision comes before the migration plan.
- Approach
- Workload profiled first, then split between serverless and provisioned on measured usage
- Build
- Staged cutover with replication running, so rollback stayed available at every step
- Proof
- Performance baselines captured before and after, per workload, not per server
Platform rebuild
Overnight batch that no longer finished overnight
A nightly load stretching further into the working day each quarter, built as a chain of SSIS packages where one failure meant a manual restart from the beginning. The fix is rarely faster hardware.
- Approach
- Dependency mapped, then rebuilt as metadata driven pipelines with per source restartability
- Build
- Incremental loads into a lakehouse, with the warehouse layer kept for the reporting model
- Proof
- Failure alerting and a rerun path that any team member can follow at 2am
Machine learning
A forecast that lived in one analyst laptop
An accurate model that only ran when one person ran it, with no versioning, no monitoring, and no path into the reports the business actually reads. Getting it into production is mostly engineering, not data science.
- Approach
- Retraining and inference moved next to the warehouse, with tracked experiments and versioned models
- Build
- Predictions written back to the platform so they appear in Power BI beside the actuals
- Proof
- Drift monitoring with a defined threshold for when a human gets involved
A good outcome is one you stop thinking about.
The measure that matters is whether the platform stays quiet after we leave.
Refreshes that land on time without anyone watching, a model the finance team
stops questioning, a pipeline your own engineers extend without calling us.
Better to be missed than needed.
Recognise one of these?
Tell us which one and what makes yours different. That difference is usually the
interesting part.
