— Databricks consulting services · Lakehouse engineering
Databricks consulting services: build the lakehouse, then make it hold.
Our Databricks consulting services take mid-market teams from scattered sources to one governed lakehouse: medallion layers over the systems you actually run, Unity Catalog governance, orchestration your team can maintain, and compute you can account for. We fix the pipeline and the process together, because most Databricks programmes fail on the second.
30+ years inside IBM · EY · J&J · McKesson
Lean Six Sigma Master Black Belt
AIMContext from $3,500
60-Day Ship Guarantee
— The pattern · Three failure modes
The platform rarely fails. The process around it does.
Sources land, nothing reconciles.
Data arrives in bronze from every system, but nobody conformed the keys. Finance, sales and operations each build their own silver, and the agent built on top answers confidently and wrongly.
Symptom: three dashboards, one metric, three numbers.
One corner each, nobody sees it all.
A year in, you have engineers who each know one part of the platform and nobody who can see the whole pipeline. Jobs depend on jobs nobody documented.
Symptom: outages nobody can trace to a cause.
Compute nobody scheduled.
Clusters left running, jobs nobody retired, a warehouse on all night for a dashboard three people open. Cost is a process problem in a platform costume.
Symptom: the Databricks bill grows faster than usage.
— The work · What we build
What our Databricks consulting services actually cover.
Lakehouse build
Bronze, silver and gold over your real source systems, such as SAP, NetSuite and SharePoint, with Auto Loader and AUTO CDC so changes land once and in order.
Pipelines and orchestration
Lakeflow Declarative Pipelines (formerly Delta Live Tables) and Lakeflow Jobs, with retries, dependencies and failure recovery, deployed through Declarative Automation Bundles so every change is versioned.
Unity Catalog governance
Grants by group, lineage on every table and one catalog for data, models and dashboards, so an auditor can follow any figure back to the row it came from.
Platform cost control
System tables, tags and budgets that show which team, job and warehouse spends what, then the schedule and sizing changes that bring the bill back in line.
Migration and consolidation
Snowflake, Teradata and Hive metastore workloads moved onto the lakehouse with validation at every step, and duplicate tools retired rather than left running beside it.
Gold views, Genie and Power BI
Gold views and metric views that Power BI and AI/BI Genie read directly, so business logic lives in one governed place instead of every report.
— The discipline · Platform cost
Cost is a process problem in a platform costume.
Most Databricks bills are not too high because the platform is expensive. They are high because nobody owns the schedule, nobody retires a job, and nobody can say which team spent what. We read the system tables, tag every workload to an owner, and set budgets that alert before the month closes, not after.
Then we change the process: which jobs run on serverless, which warehouses stop overnight, and who signs off a new cluster. The saving holds because the control plan outlives the engagement.
— 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
Platform and process map
Every source, job, warehouse and owner mapped, with the spend and the failures attached to each.
02 · INNOVATE
Innovate
Target lakehouse design
One ingestion pattern, one set of conformed keys and one governance model, designed before any build.
03 · MODEL
Model
Two sources, reconciled
Two real sources landed end to end and reconciled against the numbers finance already trusts.
04 · IMPLEMENT
Implement
Build in your workspace
The rest built in your Databricks workspace, with your engineers pairing on every pipeline.
05 · TRACK
Track
Control plan and scorecard
Freshness, quality and cost measured on a schedule, each with a named owner.
— The deliverable · What you get
Databricks consulting services with a fixed scope and a guarantee.
AIMContext
Governed lakehouse foundation, fixed scope.
Most engagements start with AIMContext, the fixed-scope build for a governed lakehouse foundation. What the first ninety days look like:
- By day 30: the platform and process map, the target design, and your first sources landing in bronze.
- By day 60: two sources reconciled in gold, pipelines on a schedule with failure recovery, and Unity Catalog grants in place.
- By day 90: cost tagged to owners, a written control plan, and your team running it without us.
If the problem is the process rather than the platform, AIMSolve starts at $2,500 and redesigns the workflow before anything is automated.
$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 approach · Design before tools
Map the sources first. Then build the lakehouse.
Every Databricks programme we are asked to rescue skipped the same step: nobody drew the sources, the hand-offs and the keys before building. So we start on the whiteboard, with the people who run the systems in the room, and we leave with one ingestion pattern and one set of conformed keys.
That is thirty years of enterprise systems work inside IBM, Ernst & Young, Johnson & Johnson and McKesson, applied to the lakehouse, with Lean Six Sigma discipline on every hand-off.
— Related reading · From the blog
Go deeper on Databricks and the lakehouse.
Cost
What you actually pay for, and where bills surprise teams.
Budget
Realistic ranges by scope, and what moves them.
FinOps
Tagging, budgets and chargeback from the system tables.
Governance
Grants, lineage and one catalog for everything.
Architecture
What the lakehouse changes, and what it does not.
Platform choice
An honest comparison for teams already on Azure.
Azure
A reference layout sized for a mid-market team.
Migration
The cost lines a migration plan usually leaves out.
— 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.
How small is too small?
If you have two or three real source systems and somebody who cares whether the numbers reconcile, there is a lakehouse engagement here. If you are a local business wanting a website and some ads, we are the wrong firm and will say so on the first call.
We already run Databricks. Can you fix what we have?
That is most of our work. The common pattern is a platform live for a year or more, several engineers who each know one corner of it, and nobody who can see the whole pipeline. We map it, fix the silver layer, bring the cost under control and leave a control plan behind.
Should we choose Databricks or Snowflake?
It depends on your workload mix, your team and what your BI layer already reads. We work on both and will tell you when Snowflake is the better answer. A consultant who only ever recommends one platform is telling you about their bench, not your problem.
What do Databricks consulting services cost?
AIMContext starts at $3,500 for a fixed-scope governed lakehouse foundation, and a typical first build lands in 14 days. Larger migrations are quoted after the assessment, because an honest number needs to see your sources first. Every Sprint engagement carries the 60-Day Ship Guarantee.
Start with the assessment, not the migration.
One call, your real sources on the table, and an honest read on whether the lakehouse is the right next move at all. If we are not the right team, we will say so.