Services
Four service lines, and what each one actually delivers.
Every engagement produces working software, written documentation and a stated end state. Below is what each service line contains, what you receive, the stack we use, and when it makes sense to buy it.
Platform engineering
We design and build the Databricks lakehouse itself: workspaces, catalogs, storage layout, compute policies, orchestration and deployment. The medallion architecture is the default, not because it is fashionable, but because separating raw, conformed and serving layers is what makes lineage and reprocessing tractable.
Governance is expressed in Terraform and Databricks Asset Bundles from the first commit. Catalogs, schemas, grants, service principals, cluster policies and jobs are code, reviewed in pull requests and deployed by pipeline. There is no manual click-path we cannot reproduce in another environment.
What you get
- Unity Catalog model: catalogs, schemas, grants, service principals and groups, defined in Terraform
- Medallion architecture with documented layer contracts and reprocessing strategy
- Databricks Asset Bundles for jobs, pipelines and cluster configuration
- Reusable CI/CD template components for dev, test and production promotion
- Cluster and compute policies, with tagging enforced at creation
- Onboarding documentation so a new team can ship a pipeline without asking us
Stack
- Azure Databricks
- Unity Catalog
- Delta Lake
- PySpark
- Databricks Asset Bundles
- Terraform
- Azure DevOps
- GitHub Actions
- Apache Airflow
- Azure Data Factory
When it makes sense
- You are starting a lakehouse and want the governance and deployment model right before the data volume makes it expensive to change.
- You have a working Databricks estate that grew organically and nobody can reproduce it in a second environment.
- Onboarding a new data team takes a project rather than a pull request.
Data quality engineering
Quality is a property of the pipeline, not a feature of the dashboard. We put validation at the boundary where data enters a layer: schema and contract checks, business rules, referential and volume checks, freshness thresholds. Failures are visible where they occur, with the offending records quarantined rather than silently dropped.
Rules are versioned alongside the pipeline code and executed on your own compute. We prefer declarative expectations in Lakeflow / Delta Live Tables and DQX rule sets over bespoke validation scattered through transformation code, because a rule nobody can find is a rule nobody maintains.
What you get
- A data quality framework wired into ingestion and transformation, with quarantine and replay
- Rule sets per dataset: schema, nullability, ranges, referential integrity, volume and distribution
- Data contracts between producing and consuming teams, in version control
- Freshness and completeness SLAs with alerting routed to an owner, not a shared inbox
- A quality report per domain, and a review cadence that someone actually attends
Stack
- Lakeflow / Delta Live Tables
- DQX
- PySpark
- Delta Lake
- Spark Structured Streaming
- dbt
- Unity Catalog
- Power BI
When it makes sense
- Business users find bad numbers before your engineers do.
- You cannot state, per dataset, when it was last valid and who owns it.
- Every incident ends in a manual reload, and the same class of error returns a quarter later.
SAP & enterprise source integration
SAP is usually the hardest source in the estate and the one the business cares about most. We extract master and transactional data, model it into something analysts can reason about, and load it incrementally so that a full reload is a recovery path rather than a nightly habit.
The output is a documented semantic layer: named entities, agreed grain, agreed keys, and a written mapping from the source tables. CRM, web analytics, in-house ERPs and event streams integrate into the same model rather than beside it.
What you get
- Extraction and change-data-capture patterns for SAP ERP master and transactional data
- Incremental load pipelines with reconciliation against the source
- A modelled, documented semantic layer with agreed grain and keys
- Integration of adjacent sources: CRM, web analytics, in-house ERP, event streams
- Test coverage across the transformation code and the orchestration
Stack
- SAP ERP
- Azure Databricks
- Azure Data Factory
- PySpark
- Delta Lake
- Kafka
- Apache Airflow
- dbt
- Unity Catalog
When it makes sense
- Analysts are exporting SAP data by hand, or waiting on a report queue.
- Finance, supply chain and product each maintain a different version of the same entity.
- A previous integration works but nobody can explain the mapping or trust the deltas.
Cost and performance optimisation
Most Databricks overspend is structural: all-purpose compute running scheduled jobs, clusters sized for the worst case and never revisited, small-file layouts forcing full scans, and no tags to attribute any of it. We measure first, change second, and report the difference.
Performance work follows the same discipline. We profile the jobs and queries that actually dominate the bill, fix the layout and the plan, and leave behind the measurement so the next regression is visible.
What you get
- A measured baseline of compute spend by workload, team and job
- Compute right-sizing: job clusters, policies, autoscaling and photon decisions with the reasoning written down
- Job and query optimisation: partitioning, file layout, liquid clustering, join and skew fixes
- Tagging and chargeback model so spend has an owner
- A before-and-after report with the measurement method included
Stack
- Azure Databricks
- Delta Lake
- PySpark
- Unity Catalog system tables
- Terraform
- Power BI
- BigQuery
- Dataproc
When it makes sense
- The Databricks invoice grows faster than the workload does.
- Nobody can say which team or product caused a given line of spend.
- Jobs that used to finish in minutes now run for hours and nobody knows when that changed.
Not sure which one you need?
The five-day health check exists for exactly that. It ends with a scored report and a prioritised roadmap, whether or not we build any of it.
Book a health check