Databricks
Databricks consulting for lakehouse data and AI
Databricks consulting helps data, platform and engineering leaders turn a Databricks workspace into a governed lakehouse that feeds analytics, machine learning and AI agents. We build pipelines, Unity Catalog governance, cost controls and Mosaic AI applications for mid-size and large US companies, working inside your cloud account. Each engagement starts with a free review and a fixed quote, and everything we build is code your team owns.
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Quick answer
What is Databricks consulting?
Databricks consulting is outside help to design and build a Databricks lakehouse: workspace and Unity Catalog setup, ingestion and transformation pipelines, governance, cost controls and AI workloads with Mosaic AI. The aim is one governed copy of data that serves analytics, machine learning and AI agents, delivered as code your team owns and can maintain.
- A lakehouse keeps open-format data in your cloud storage with warehouse-style governance.
- Unity Catalog centralizes permissions, lineage and audit logs across Databricks workspaces.
- Mosaic AI supports agents, vector search and model serving on governed lakehouse data.
- Cluster policies and system tables are the first tools for controlling DBU spend.
01
What Databricks consulting covers
Databricks is a data and AI platform built on Apache Spark and Delta Lake that runs on AWS, Azure and Google Cloud. It combines data engineering, SQL analytics, data science and AI model serving on one copy of data stored in open table formats in your own cloud storage. That breadth is the appeal, and also why many workspaces end up with scattered notebooks, oversized clusters and unclear ownership.
Our Databricks consulting work brings structure: clear environments, governed catalogs, tested pipelines and predictable compute spend. We treat the workspace as a production system with source control, deployment pipelines and documented ownership.
- Workspace and Unity Catalog design across dev, test and prod
- Batch and streaming pipelines with Lakeflow Declarative Pipelines (formerly Delta Live Tables) and Jobs
- Ingestion from SaaS apps, databases and files with Lakeflow Connect, Auto Loader or partner tools
- Cluster policies, serverless compute choices and cost tagging by team
- Databricks Asset Bundles for CI/CD and repeatable deployments
- Mosaic AI agents, vector search and model serving on governed data
02
What is a lakehouse and when does it fit
A lakehouse keeps data in low-cost cloud object storage in an open format such as Delta Lake or Apache Iceberg, then adds the transactions, schema enforcement and governance you would expect from a warehouse. The same tables can serve SQL dashboards, Python notebooks, streaming jobs and machine learning without copying data between systems.
It fits best when you have large or semi-structured data (sensor readings, logs, documents, images), real-time or near-real-time feeds, and data science teams who work in Python. A medallion design, with bronze raw data, silver cleaned data and gold business-ready tables, gives everyone a shared map of where data is and how trusted it is.
WHAT WE BUILD
What we automate
Typical automations, each scoped and quoted at a fixed price after a free review.
Auto Loader file ingestion
New files in S3, ADLS or Google Cloud Storage are picked up incrementally and loaded into bronze Delta tables, with schema changes tracked rather than breaking the job.
Medallion pipelines
Lakeflow Declarative Pipelines clean and join raw data into silver and gold tables with built-in data quality expectations that quarantine bad rows.
Streaming sensor data
Plant historian or IoT data from AVEVA PI or Kafka streams into Delta tables for near-real-time dashboards and anomaly checks.
Cost monitoring from system tables
A scheduled job reads Databricks system tables and reports DBU spend by workspace, job and team, flagging clusters that run idle.
Document question-answering agent
PDFs such as SOPs or manuals are chunked, embedded into Mosaic AI Vector Search and served through an agent that cites its sources.
Job failure alerts
Failed or late Databricks Jobs notify the owning team in Teams or Slack and open an incident in ServiceNow or PagerDuty.
Gold tables to BI
Curated gold tables feed Power BI, Tableau or Databricks AI/BI dashboards through a serverless SQL warehouse on a refresh schedule.
03
Databricks consultant vs Databricks developer
A Databricks consultant shapes the platform: how workspaces and catalogs are organized, who can access what, which workloads run on which compute, and how the lakehouse connects to existing systems such as SAP S/4HANA, AVEVA PI, Salesforce or ServiceNow. A Databricks developer writes the PySpark, SQL and pipeline code inside that design.
We cover both, usually starting with a short architecture and cost review that your platform, security and data owners sign off. Development then runs in sprints, with your engineers reviewing pull requests so the team can extend the work after handover.
04
Mosaic AI and AI agents on Databricks
Mosaic AI is the set of Databricks capabilities for building and running AI on lakehouse data, including model serving, vector search, an agent framework, evaluation tools and an AI gateway for governing access to external and open models. MLflow tracks experiments, models and agent quality over time.
We use these to build agents that answer questions from maintenance manuals, summarize quality deviations, classify incoming documents or draft responses using data Unity Catalog already governs. As one example from our case studies, a European procurement firm working on nuclear infrastructure saved about 30 hours a month with a supplier intelligence tool. Every agent we ship has an evaluation set, logging and a human review step where decisions matter.
05
Governance, security and enterprise integration
Unity Catalog centralizes permissions, lineage and audit logs across workspaces, which makes it the foundation for vendor security reviews and internal audit. We set up SSO and SCIM from Okta or Microsoft Entra ID, group-based grants, row filters and column masks for sensitive fields, and service principals for automated jobs.
Changes flow through Git and your change control process rather than being edited live in production notebooks. For regulated teams, such as GxP environments in pharma or FFIEC-guided banks, we document pipeline logic, access and lineage to support your own validation work, without claiming compliance on your behalf.
06
Databricks vs Snowflake, and what consulting costs
Databricks tends to suit engineering-heavy teams with Spark, streaming and machine learning workloads and a preference for open formats. Snowflake tends to suit SQL-first analytics teams that want a more managed experience. Both now offer SQL warehousing, Python and AI features, and many companies run both, so we design around your workloads rather than a platform preference.
We quote a fixed price after the free review. Cost depends on the number of sources, the state of existing notebooks, governance requirements and whether AI agents are in scope. A cost and architecture review typically takes 1-2 weeks, a first production pipeline 3-5 weeks, and larger lakehouse programs ship in stages over 6-8 weeks or more. Databricks compute (DBUs) and cloud infrastructure are billed to your account directly.
TOOLS
Tools we connect
FAQ
Databricks Consulting questions
What does a Databricks consultant do?
A Databricks consultant designs and builds your lakehouse: workspace and Unity Catalog structure, ingestion and transformation pipelines, governance, cost controls and AI workloads. Most engagements include hands-on development and handover documentation so your team can run it.
How do we lower our Databricks bill?
Common fixes include cluster policies, auto-termination, moving suitable work to serverless or job compute, right-sizing SQL warehouses and making pipelines incremental. System tables show which jobs drive spend, which is where a review usually starts.
Do we need Unity Catalog?
For most organizations, yes. Unity Catalog gives one place to manage permissions, lineage and audit logs across workspaces, and many newer Databricks features depend on it. We can migrate existing Hive metastore tables in stages.
What is Mosaic AI used for?
Mosaic AI covers building, serving, evaluating and governing AI models and agents inside Databricks. Teams use it for retrieval-based assistants, document classification and custom models that work directly on lakehouse data.
Can dbt run on Databricks?
Yes. dbt works with Databricks SQL warehouses and Unity Catalog, and many teams use it for SQL transformations while keeping Spark jobs for heavier Python or streaming work.
Are you a Databricks partner?
We do not claim partner status. We are an independent team that builds inside your Databricks workspace and cloud account, and you own all code, configuration and data.
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Tell us what you want automated
Describe the process that eats your team's week. We reply within one business day with a first take on what can be automated, which tools fit and roughly what it would cost.
- Free 30-minute automation review
- Fixed quote before any work starts
- You own every workflow, account and line of code