Databricks Consulting Services for Lakehouse, Data Engineering and AI
Databricks consulting services: lakehouse design, Delta Lake, Unity Catalog governance, Lakeflow pipelines, Mosaic AI and cost control for enterprise data.
Get a free proposal
Tell us what you need. A consultant replies within one working day.
01
A Databricks lakehouse your engineers and analysts can both rely on
Databricks gives a data team a great deal of power: open table formats, Spark at any scale, streaming and batch in one place, and machine learning next to the data. That freedom is also the usual problem. Without a design, workspaces fill with notebooks nobody owns, clusters run longer than they need to, and three teams build three versions of the same table.
Our Databricks consulting services put structure around the platform: a lakehouse architecture with clear layers, Unity Catalog governing every table and model, pipelines that are declared and tested rather than hand-run, and compute policies that keep the bill in line with the value. We work in your cloud account and workspaces, and hand over code your engineers can own.
We do a lot of this work for pharma and life sciences teams. Our founder, Harsh Khubchandani, came up through pharma commercial operations consulting at ZS, so the data on the other side of the pipeline (prescriptions, specialty pharmacy feeds, claims, HCP masters) is familiar ground.
02
What our Databricks consulting covers
Lakehouse architecture
A layered bronze, silver and gold design on Delta Lake, with workspaces, catalogs and environments laid out so development, testing and production stay separate.
- Medallion layers with clear ownership
- Delta Lake tables with schema enforcement
- Workspace and environment design
Unity Catalog governance
One place to manage access, lineage and auditing across tables, files, models and functions, including migration from legacy Hive metastore setups.
- Catalog and schema structure
- Grants, row filters and column masks
- Lineage and audit for regulated data
Pipelines with Lakeflow
Ingestion through Lakeflow Connect or your existing tools, transformations in Lakeflow Spark Declarative Pipelines (the product formerly called Delta Live Tables), and orchestration in Lakeflow Jobs.
- Declared tables with data quality expectations
- Incremental and streaming loads
- Monitoring and alerting on failures
Machine learning and GenAI
Models and agents built with Mosaic AI: MLflow for tracking, Model Serving for deployment, Vector Search for retrieval, and Agent Bricks where a managed agent build fits.
- Feature tables governed in Unity Catalog
- Evaluation before anything reaches users
- Monitoring of model inputs and outputs
Cost control
Compute policies, job clusters instead of always-on clusters, serverless where it is cheaper, and spend reporting by workspace and team.
- Cluster and compute policies
- Right-sized jobs and SQL warehouses
- System tables for usage reporting
Migration to Databricks
Hadoop, on-premise Spark, legacy ETL tools or older warehouses moved onto the lakehouse, with outputs reconciled before old jobs are switched off.
- Job and code inventory
- Conversion and parallel runs
- Reconciliation against current outputs
03
Databricks or Snowflake?
Both are capable, and many enterprises run both. The choice usually comes down to who does the work and what the work is.
| Databricks | Snowflake | |
|---|---|---|
| Primary users | Data engineers and data scientists working in Python, SQL and Spark | Analysts and engineers working mostly in SQL |
| Storage | Open formats (Delta Lake) in your own cloud storage | Managed storage, with support for open table formats |
| Strength | Heavy data engineering, streaming and machine learning | Managed warehousing with little tuning, and data sharing |
| Governance | Unity Catalog | Roles, policies and Horizon governance features |
| Choose it when | ML and engineering are central and you want open formats | BI and SQL analytics are central and you want low operational effort |
We build on both and will recommend what fits. See Snowflake consulting for the other side.
04
Where Databricks fits for pharma and life sciences teams
05
Case snapshot: patient access reporting
A specialty therapy provider was rebuilding its patient access reporting by hand each cycle, with identifiers that did not match across sources. We built clean identifiers and an MDM structure, automated case pipelines, standardised reporting and dashboards, which cut manual work by 60 to 80 percent and kept reporting stable. On Databricks, the same pattern maps to declared pipelines feeding a governed master and gold reporting tables. For the master data side, see patient master data management.
06
How an engagement runs
Assess
We review workspaces, pipelines, governance, compute spend and the outputs the business depends on.
Design
Lakehouse layers, Unity Catalog structure, pipeline standards and compute policies are agreed in writing.
Build
Pipelines and tables go in source by source, with quality expectations and tests from the start.
Reconcile and cut over
New outputs are run in parallel with the old ones and switched over only when they match.
Hand over and extend
Your team gets documentation and walkthroughs, and ML or GenAI use cases are added on the governed foundation.
07
How we handle your data
08
Databricks consulting questions
What do Databricks consulting services include?
Do you need a Databricks implementation partner?
What is a Databricks lakehouse?
What happened to Delta Live Tables?
How do you reduce Databricks costs?
Is Databricks suitable for life sciences data?
09
Related services
See data architecture and data ingestion for the layers around the lakehouse, AI consulting for use cases beyond the platform, and Microsoft Fabric and Azure consulting if you run Azure Databricks alongside Fabric.
Tell us what your lakehouse has to deliver
Describe your workspaces, sources and the outputs that matter. We will come back with how we would approach it and what to fix first.
A reply from a consultant, usually within one working day.
Part of our Data Management and Engineering hub. Start with our data management services.
Data Management and Engineering
More on data management services
AWS and Google Cloud Consulting for Data, Automation and AI
AWS and Google Cloud consulting for data, automation and AI workloads: pipelines, warehouses, serverless automation and AI services, built and run cost-aware.
Microsoft Fabric Consulting and Azure Data Engineering Services
Microsoft Fabric consulting and Azure data engineering: OneLake, Data Factory, Synapse to Fabric migration, Purview governance and Azure AI for enterprise data.
Snowflake Consulting Services for Enterprise and Life Sciences Data
Snowflake consulting services: architecture, dbt pipelines, cost governance, RBAC, data sharing and Cortex AI, for enterprise and life sciences data.
Common Data Quality Challenges in Pharma MDM (and How to Fix Them)
Common Data Quality Challenges in Pharma MDM (and How to Fix Them) In pharmaceutical organizations, Master Data Management (MDM) sits at the center of analytics, commercial operations, regulatory...
Data Transformation Service
Challenges of Data Transformation How Data Transformation Service can Benefit Businesses Types of Data Transformation Data Cleansing Removing errors, inconsistencies, and duplicates from the data...
Get started
Tell us where the week goes
Two weeks, fixed scope, a costed plan at the end. No obligation after it.
- We map your workflows and where the time actually goes
- You get the three that cost the most, with what automating them takes
- Delivered as a document, in 5 to 7 working days
Tell us where the week goes
A senior consultant will map where the time goes, name what is worth automating and what it takes. No obligation after it.