Databricks services
Lakehouse platforms that hold up in production.
We design, migrate, and run Databricks lakehouses, governed with Unity Catalog from the first workload, and take analytics, machine learning, and AI agents into production.
Databricks Consulting & System Integrator Partner
Datagist and Databricks.
As a Databricks Consulting & System Integrator Partner, our team has access to Databricks partner enablement, which keeps our advice in step with the platform.
Our background is master data and data platforms in life sciences, healthcare, insurance, and energy, so our Databricks work starts with the data your business depends on. If another tool suits part of your estate better, we will say so.
Many Databricks workspaces start as a sandbox and grow without a plan. Clusters run all day, notebooks become production jobs, and access is granted workspace by workspace. Costs climb, nobody knows which tables to trust, and getting a model or agent into production takes months.
We set up the lakehouse as a platform: Unity Catalog for access, lineage, and audit; quality checks between medallion layers; pipelines deployed from source control; and cost controls on compute. Then we build the analytics, ML, and GenAI workloads on it and hand over runbooks your team can operate.
Our Databricks services.
A governed lakehouse your teams can build on, with costs you can explain.
- 01
Lakehouse architecture and setup
Workspace design, Unity Catalog, networking and security, environment strategy, and infrastructure as code.
- 02
Migration to Databricks
Moves from Hadoop, legacy data warehouses, and ETL tools such as Informatica PowerCenter, with automated reconciliation to prove the numbers match.
- 03
Data engineering with Lakeflow and Delta Lake
Batch and streaming pipelines, declarative pipelines, change data capture, and data quality expectations.
- 04
Unity Catalog governance
Access policies, data classification, lineage, audit, and migration off the legacy Hive metastore.
- 05
Databricks SQL and BI
SQL warehouses, shared metric definitions, and dashboards connected to the BI tools your teams already use.
- 06
Machine learning and GenAI with Mosaic AI
MLflow model lifecycle, feature engineering, model serving, and retrieval and agents with evaluation and guardrails.
- 07
Cost and performance optimization
Compute policies, cluster right-sizing, serverless adoption, job tuning, and spend reporting by team.
- 08
Platform operations
Monitoring, incident response, upgrades, and ongoing enhancement after go-live.
Databricks across our services.
Modern Data
Lakehouse design, migration, pipelines, and master data delivered on Databricks.
Modern DataAI & ML
MLflow, feature pipelines, model serving, and monitoring for models in production.
AI & MLGenAI & Agents
Retrieval, agents, and evaluation built with Mosaic AI on governed data.
GenAI & AgentsGovernance & Operations
Unity Catalog policy, lineage, audit, and day-to-day platform operations.
Governance & OperationsWhat we bring to Databricks work.
Starting points we adapt to your data and systems. You keep what we adapt.
The first step.
We start with a review of your Databricks environment, or of the systems you plan to move onto it: workloads, costs, governance, and the use cases coming next. You get a prioritized list of fixes and a plan for the first production workload.
How we work
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Plan your next Databricks step.
Tell us what you want Databricks to do for your business. We will tell you the first step and whether we are the right fit.
Databricks and the Databricks logo are trademarks of their respective owner, used here to identify our partnership.