— Data engineering · Analytics on the lakehouse
Data engineering consulting services for one governed lakehouse.
Our data engineering consulting services land ERP, CRM and SaaS data in one governed Databricks lakehouse, conform it, and serve it to the people who decide. We build the pipelines, the gold views your BI tool reads, and the scheduling that keeps them honest after we leave.
30+ years inside IBM · EY · J&J · McKesson
Lean Six Sigma Master Black Belt
AIMContext from $3,500
60-Day Ship Guarantee
— The pattern · What breaks
More tools did not fix it. The pipeline was never designed.
Sources that never agree.
One team pulls the ERP nightly, another scrapes the CRM, a third exports spreadsheets from SharePoint. Nothing shares a key, so every report starts with a reconciliation argument.
Symptom: finance and sales bring different revenue to the same meeting.
Tool sprawl with no owner.
A connector product here, a script there, a scheduler nobody owns. Buying more data pipeline software adds seams. The fix is one design for how data moves, not another licence.
Symptom: a failed job is found by the person reading the report.
Logic trapped in the report.
Business rules live inside Power BI measures and nowhere else. Change a report and the number moves, and nobody can say which figure is right.
Symptom: two dashboards, one metric name, two answers.
— The work · What we build
What our data engineering consulting services actually build.
Managed ingestion
Lakeflow Connect managed connectors where one exists for your source, such as Salesforce, Workday and SQL Server, and custom ingestion where it does not. Everything lands in bronze with lineage intact.
SAP and ERP sources
SAP through the SAP Business Data Cloud connector for Databricks, which uses OpenSharing rather than Lakeflow Connect. NetSuite and other ERPs scoped against your real landscape before we promise anything.
Medallion layers
Bronze, silver and gold built with Lakeflow Declarative Pipelines (formerly Delta Live Tables) or dbt, so every figure can be traced back to the row it came from.
Data quality expectations
Expectations that stop bad rows before they reach a report, and fail loudly when a source changes shape instead of passing the damage downstream.
Orchestration
Lakeflow Jobs (formerly Databricks Workflows) for scheduling, retries and failure recovery, alongside Azure Data Factory or Airflow where they already earn their place.
Gold views for BI
Gold views and metric views that Power BI and AI/BI Genie read directly, governed in Unity Catalog, so the business logic lives in one place instead of every report.
— The approach · Design before tools
One ingestion pattern. One set of conformed keys.
Most lakehouse projects fail slowly: a pipeline per request, a key per team, and a backlog that never shrinks. We design one ingestion pattern and one set of conformed keys first, then land two real sources end to end and reconcile them against the numbers finance already trusts.
The sources we have worked with are the ones mid-market teams actually run: SAP, NetSuite, SharePoint, FreshService and Google Analytics. Only when two of them agree in gold do we build the rest.
— The method · AIM-IT
Five phases. Every build, every time.
Assess, Innovate, Model, Implement, Track. Each phase ends with something you can inspect and sign off, not a slide.
01 · ASSESS
Assess
Source and hand-off map
Every source, every hand-off and every spreadsheet that quietly holds the real logic, mapped.
02 · INNOVATE
Innovate
Ingestion and key design
One ingestion pattern and one set of conformed keys, not a pipeline per request.
03 · MODEL
Model
Two sources, reconciled
Two sources landed end to end on real data and reconciled against the numbers finance trusts.
04 · IMPLEMENT
Implement
Pipelines in your workspace
The rest built in your workspace, with your engineers pairing on every pipeline.
05 · TRACK
Track
Freshness and cost scorecard
Freshness, quality and cost measured on a schedule, with a named owner for each.
— The deliverable · What you get
A governed lakehouse your team can run without us.
AIMContext
Governed data foundation, fixed scope.
AIMContext is the fixed-scope build for a governed data foundation. You get:
- Your priority sources landed in a governed bronze, silver and gold lakehouse.
- Pipelines on a schedule, with retries, alerts and failure recovery.
- Data quality expectations that fail loudly instead of passing bad rows.
- Gold views and metric views Power BI and Genie can read without a translation layer.
- Your team able to run it. We are not trying to become permanent.
$3,500+
14 days
5 phases
60 days
60-Day
Ship Guarantee
On every Sprint engagement: if we do not deliver the agreed working artifact in 60 days, you do not pay the final invoice. That is what AIM-IT is for.
— The discipline · Lean Six Sigma
Databricks and data engineering, run like a production line.
A medallion lakehouse is process control in disguise. Bronze is the receiving dock, silver is inspection, gold is the finished good. We measure defects at each stage the way a Master Black Belt would on a factory floor, and we put a named owner on every one.
That is the difference between a pipeline that works on demo day and a data engineering practice that still reconciles a year later.
— Related reading · From the blog
Go deeper on lakehouse data engineering.
Tools
Where managed connectors, dbt and custom code each fit.
Ingestion
What the managed connectors cover and where they stop.
Pipelines
The rename, what moved and what did not.
Orchestration
How to pick an orchestrator you will still like in a year.
Azure
When ADF earns its place and when it is a second seam.
Transformation
Where dbt helps silver and gold, and where it adds work.
ERP
Getting NetSuite data into governed, reconciled layers.
SAP
The Business Data Cloud route against classic extraction.
Architecture
A reference layout sized for a mid-market team.
— Straight answers · FAQ
Questions we get asked first.
Are you a Databricks partner or certified?
No, and we will not imply otherwise. Certification is in preparation, and we are not in the Databricks partner programme. What we bring is thirty years of delivering the enterprise systems this data comes out of, inside IBM, Ernst & Young, Johnson & Johnson and McKesson, plus Lean Six Sigma at Master Black Belt level. Ask us for the work, not the badge.
Which data pipeline tools will you use?
Whatever your lakehouse already runs, where it is sound. On Databricks that usually means Lakeflow Connect for managed ingestion, Lakeflow Declarative Pipelines or dbt for transformation, and Lakeflow Jobs for orchestration. We will not sell you another pipeline licence to solve a design problem.
Can you bring SAP data into Databricks?
Yes. The documented path is the SAP Business Data Cloud connector for Databricks, which uses OpenSharing and is separate from the Lakeflow Connect managed connectors. We scope it against your SAP landscape before we commit to a date.
We already have one Databricks engineer. Is that enough?
Often it is not, and that is not a criticism of the engineer. One specialist ends up owning every source, every job and every late-night failure. We build the pattern with your engineer pairing, so the knowledge stays in-house when we leave.
What does data engineering consulting cost?
AIMContext starts at $3,500 for a fixed-scope governed data foundation, and a typical first build lands in 14 days. Larger programmes are quoted after the assessment, because an honest number needs to see your sources first.
Start with your sources, not a tool list.
One call, your real systems on the table, and a straight read on what a governed lakehouse pipeline would take. If we are not the right team, we will say so.