Coming soonUnity Catalog Best Practices: 12 Proven Rules for Governance
Twelve opinionated Unity Catalog rules for 2026, from metastore and catalog layout to managed tables, ABAC policies, system-table audits and hive_metastore migration.
Databricks Consulting Services
Databricks setup, Unity Catalog, Lakeflow pipelines, migrations, cost control and AI on governed data, on AWS.
Databricks consulting services
Get the Databricks platform you were promised
Our Databricks consulting services help teams set up, migrate to and optimize the Databricks Data Intelligence Platform on AWS: Unity Catalog governance, Lakeflow pipelines, Databricks SQL, cost control and AI on governed data.
Whether you are starting fresh, moving off EMR, Hadoop or a legacy warehouse, or untangling a workspace that grew without a plan, we bring senior engineers who have seen the pitfalls before.
Typical outcomes
- Workspaces and Unity Catalog set up right the first time
- Reliable pipelines with data quality checks built in
- Visible, controlled Databricks spend
- AI features built on data you can trust
What we deliver
Databricks consulting, from first workspace to production AI
Platform setup on AWS
Account and workspace topology, networking with PrivateLink, storage credentials, cluster policies and serverless compute, all in Terraform.
Unity Catalog governance
Catalog design, group-based permissions, row filters and column masks, lineage and audit with system tables, and migration from the Hive metastore.
Lakeflow pipelines
Ingestion with Lakeflow Connect and Auto Loader, transformations with Lakeflow pipelines (formerly Delta Live Tables) and orchestration with Lakeflow Jobs.
Migration to Databricks
Planned moves from Amazon EMR, Hadoop, AWS Glue or warehouses, with code conversion, parallel runs and reconciliation before cutover.
Cost and performance
Liquid clustering, Photon, right-sized SQL warehouses and job compute, plus budgets and chargeback from system tables.
AI on governed data
Mosaic AI, AI Search (formerly Vector Search), Agent Bricks and Lakebase used where they add value, with evaluation and guardrails from day one.
Our approach
Opinionated where it matters, flexible everywhere else
- Unity Catalog first. Governance, lineage and access control are designed before the first production table.
- Everything as code. Terraform for the platform and bundles for jobs and pipelines, deployed through CI/CD.
- Serverless where it pays. Serverless compute and SQL warehouses for spiky work, job compute and policies for steady pipelines.
- Quality gates. Expectations and tests stop bad data at the door instead of in a dashboard.
- Your team owns it. Pairing, reviews and runbooks so the platform does not depend on us.
Deliverables
- Workspace and catalog architecture
- Terraform and bundle repositories
- Production pipelines with tests
- Cost dashboards and guardrails
- Handover and training sessions
Insights
Latest Databricks guides
Coming soon
Coming soonLakeflow Declarative Pipelines: 10 Best Practices for Reliable ETL
How to build reliable ETL with Lakeflow Declarative Pipelines (formerly Delta Live Tables): dataset choices, expectations, AUTO CDC, serverless and monitoring.
Coming soonDatabricks Cost Optimization: 15 Proven Ways to Save Money
A hands-on playbook for cutting Databricks spend on AWS: attribute every DBU with system tables, then fix compute, warehouses, instances and storage.
FAQ
Databricks consulting services: common questions
Do you only work with Databricks on AWS?
Most of our Databricks work runs on AWS, where we also handle the surrounding services such as Amazon S3, IAM, KMS, networking and streaming. The Databricks practices themselves, including Unity Catalog and Lakeflow, apply on any cloud.
Can you migrate us from Amazon EMR or Hadoop to Databricks?
Yes. We inventory your jobs and data, design the target in Unity Catalog, convert and test code, run old and new in parallel and cut over in waves once results reconcile. Decommissioning the old platform is part of the plan.
How do you keep Databricks costs under control?
We make spend visible with system tables and tags, then apply the fixes that matter most: the right compute type per workload, cluster policies, auto-termination, warehouse sizing, liquid clustering and removing unused jobs and tables.
Do you build AI agents on Databricks?
We build the data foundations agents need, such as governed tables, document pipelines, AI Search indexes and evaluation sets, and we implement agents with Databricks tools where they fit your use case.
What does a first engagement look like?
Usually a short, fixed-scope assessment of your workspace, governance, pipelines and spend, ending with a prioritized roadmap. From there you can build with us or on your own.
Want a second opinion on your Databricks setup?
In a free 30-minute call we will review your goals and point out the highest-value next steps.