Fast track to Databricks.
Compute selection, Unity Catalog, Lakeflow ETL, Asset Bundles. The decisions that determine whether Databricks pays back its bill or just becomes the bill, made once across dozens of rollouts so you don't make them again. Plus a free accelerator that turns eight questions into Terraform, CI/CD, and a compliance doc.
Sound familiar?
The things we hear from teams already running, or considering, Databricks.
The opinions, written down.
Six of the patterns we apply on every Databricks rollout. Written down so you can read them before talking to us.
Job clusters for production. Never all-purpose.
All-purpose clusters create resource contention and unpredictable costs. Job clusters spin up for the run and terminate automatically. Inline-defined in the job spec, never reused across pipelines, so cost and failure isolation are clean.
Photon only where joins and aggregates dominate.
Photon doubles DBU cost. Worth it for heavy multi-table joins and GROUP BY on datasets above ~100 GB. Not worth it for simple ETL, data movement, or most DLT pipelines. Enabling Photon by default is the most common cost mistake we see.
Catalogs by environment. Schemas by source, then domain.
Unity Catalog organised as dev / test / acc / prod at the catalog level. Bronze schemas split per source (landing_sap, landing_salesforce) for traceable lineage. Silver and Gold consolidated by domain (ads, ads_sales) to keep related transformations together.
Asset Bundles deploy code, workflows, and infra as one unit.
Notebooks, jobs, and cluster configs versioned and deployed together. Validated in PR with databricks bundle validate, parameterised with ${var.warehouse_id} and ${env} so promotion is a config change, not a copy. Git SHA on every artifact for clean rollback.
DLT for production transformations. Notebooks for exploration.
Delta Live Tables for production: built-in expectations, automatic lineage, declarative dependencies. expect_or_fail() reserved for genuinely critical rules. Standard notebooks for ad-hoc analysis only. Auto Loader for cloud files, CDC for transactional sources.
Repo folders, layered structure, shared utilities.
/src and /test as top-level folders, mirrored. Notebooks named by source and table in landing layers, with f_ and d_ prefixes for gold-layer facts and dimensions. A /shared utilities folder so the same logic doesn't live in four notebooks.
How we deliver a Databricks setup
Four steps. The same every time. The decisions inside each one come from doing it dozens of times, not from reading a docs page.
Anchor
One real workload, one person who cares about the answer. The thing we ship in the first iteration must be useful on its own, not part of a tenant-wide migration plan.
Right-size
We measure data volume, query patterns, and concurrency before we pick compute. Job clusters for ETL, SQL Warehouses for BI, autoscale ranges set against actual load. Photon only where it earns its 2x.
Build end-to-end
Source to Unity Catalog to Power BI or downstream consumer. DLT pipelines following the medallion pattern, Asset Bundles for deployment, Terraform for Unity Catalog permissions. The patterns we apply on every rollout.
Hand it over
Cluster policies, runbooks, monitoring, CI/CD pipelines. Your team runs it without us, and the next workload reuses what's already built. Docs live next to code, not in a wiki nobody opens.
Get a Databricks rollout plan in 5 minutes.
We turned our rollout patterns into a tool. Answer eight questions, get Terraform, CI/CD pipelines, a compliance justification doc, and an interactive rollout explorer back. Use it as a starting point, or hand it to us to deliver.
- Terraform HCL for your Azure tenant, workspaces, and Unity Catalog
- CI/CD pipelines and Asset Bundles for promoting changes through environments
- Compliance doc mapping every decision to your frameworks
- 20+ frameworks supported: GDPR, DORA, NIS2, ISO 27001, EU AI Act, more
Databricks work sits inside Analytics, part of Build.
Strategy decides if Databricks is the right pick. We build it. Experts keep it running once we hand it over.
Strategy
From business questions to a data, AI and software direction your organisation can follow.
Explore →Build
Data and analytics, AI, and custom software. Built with adoption and ownership from day one.
Explore →Experts
Specialists who think like teammates. Keep your engine running and your team growing.
Explore →Thinking about Databricks? Talk to us first.
Half-hour call. We'll tell you if it's the right pick for your situation, and what it actually takes to get one workload running well.
Book a Databricks chat