Data Engineering & Warehousing
Data engineering services for SQL Server and Azure
We design and build the pipelines that move data from ERP, CRM, files and APIs into a platform your reporting can rely on — with logging, restartability and lineage designed in from the first load, not bolted on later.
When teams bring us in
Signs you need Data Engineering support
- Data arrives from several source systems with no consistent integration pattern
- Loads are full reloads every night because incremental logic was never built
- A failed load means someone re-runs steps by hand and hopes nothing was missed
- Nobody can say with confidence where a number in a report came from
What's included
Our Data Engineering work
- 01
Source integration design
Connection, extraction and scheduling patterns for each source system, agreed before build starts.
- 02
Incremental loading
Change detection, watermarks and late-arriving data handled explicitly so loads stay fast as volumes grow.
- 03
Orchestration & restartability
Dependencies, retries and restart points so a failure costs one step, not the whole night.
- 04
Logging & lineage
Run history, row counts and source-to-target lineage captured as part of every pipeline.
FAQ
Data Engineering — common questions
- Do you work with on-premises SQL Server, or only the cloud?
- Both. Many of the environments we work in are on-premises SQL Server with SSIS, some are on Azure, and many are hybrid. We design for the platform you have and the one you're moving to.
- Can you extend our existing pipelines rather than rebuild them?
- Yes. We usually start by assessing what exists, keep what works, and refactor or replace only the parts that are causing failures, slowness or maintenance cost.
- How does an engagement start?
- With a short assessment of your sources, pipelines and reporting. You get a current-state map and a prioritised plan before any build work is committed to.
Related Dataventra solutions
Guides
- Guide · 7 min readHow to find everything that depends on a SQL Server table before you change itFind views, procedures, SSIS packages and Power BI datasets that depend on a SQL Server table — with sys.dm_sql_referencing_entities, recursive dependency queries and what they miss.
- Guide · 8 min readMonitoring SQL Agent job and SSIS package failures: a practical guideQuery msdb and SSISDB to find failed SQL Agent jobs and SSIS executions, slow runs, missing rows and stale data — and turn the queries into monitoring.
- Guide · 6 min readAutomatically creating Jira tickets for failed SQL Server Agent jobsDetect SQL Agent and SSIS failures, capture diagnostics and create Jira issues through the REST API — with deduplication, routing and secure credentials.
More in Data Engineering & Warehousing
- Data Engineering & WarehousingData WarehousingDimensional models and warehouse architecture built for reporting performance and long-term change.
- Data Engineering & WarehousingSQL Server & SSISDeep, hands-on work in SQL Server, SSMS and SSIS — the platforms many enterprises still run on.
- Data Engineering & WarehousingAzure Data PlatformMove and extend on-premises SQL workloads onto Azure without losing control of cost or reliability.
Talk to us about Data Engineering.
Tell us what you're running and what's getting in the way. We'll suggest a sensible first step.