Databricks Partner, Lakehouse and AI Delivery
Databricks Consulting for Regulated Data and AI Teams
Unity Catalog permissions that match your org chart, Delta tables with an owner, notebooks that become scheduled jobs, and models that reach a serving endpoint only after someone signs for them.
Databricks consulting is outside help to build and run a governed lakehouse on Databricks: Unity Catalog access and lineage, Delta tables organized into clear layers, notebook code turned into tested production jobs, and ML or GenAI models served under an approval process. Kriv AI offers this as a Databricks partner; our listed status is Partner, Active. We do not yet have a named Databricks case study to publish, so we scope from your workspace, not from a slide.
databricks consulting scope
What Databricks Consulting Covers When the Data Is Regulated
Most Databricks workspaces we see did not fail at the platform level. They grew one notebook at a time: shared clusters, tables in the default schema, personal access tokens in scheduled jobs, and a model somebody deployed from a laptop. Databricks consulting, as we practice it, is the work of turning that into something an owner can run and a reviewer can follow.
Our practice is built around healthcare, life sciences, financial services and insurance. That changes the order of work. In a regulated workspace, permissions and lineage come before the first new pipeline, because retrofitting them onto live tables means re-pointing every job and dashboard that reads them.
We are a good match if you have chosen Databricks and need the platform design and its controls done together, or if you inherited a workspace that grew faster than its governance. We are a poor match if you only need a one-off dashboard, or if you are still choosing a platform and want a referee: we also partner with Microsoft, so treat our view on that choice as informed but not neutral.
unity catalog access lineage
Unity Catalog Access and Lineage, Designed Before the First Table
Unity Catalog is Databricks' governance layer for data and AI assets, with a three-level namespace of catalog, schema and object. Enabling it is a setup task. The decisions inside it are what make it useful.
A naming standard you can grant against
We set catalogs by environment and domain, for example a production claims catalog separate from a development one, with schemas for each layer inside. Permissions are then granted to groups at the catalog or schema level, never to individual users on individual tables. When someone changes role, one group membership changes and nothing else.
An ownership register
Every production catalog and schema gets a named owner group, recorded in the catalog itself and in a short register: owner, data classification, who approves access, and which downstream jobs depend on it. Access requests go to that owner, not to the platform team.
Sensitive columns and lineage
Columns holding identifiers such as member, patient or account numbers are tagged, and row filters or column masks are applied where a group should see the table but not every value. Lineage is then used for impact analysis before changes: before altering a table, the owner can see which jobs, dashboards and models read it.
medallion lakehouse
Lakehouse Consulting: Delta Tables and the Medallion Layers
Databricks stores tables in Delta Lake, an open table format on cloud object storage. The common medallion pattern sorts those tables into bronze for raw ingested data, silver for cleaned and conformed data, and gold for business-ready tables. The pattern is widely known; the value is in the rules at each boundary.
| Layer | What we decide with you | Who can read it |
|---|---|---|
| Bronze | Ingestion method per source, retention period, how raw sensitive fields are stored, and whether reprocessing from raw must be possible. | Platform engineers and pipeline service principals only. |
| Silver | Quality checks that block a load versus checks that only warn, deduplication keys, conformed reference data, and which identifiers are masked. | Data engineers and approved analytical groups, under column masks where needed. |
| Gold | A written contract per table: grain, refresh schedule, owner, and what a breaking change is and how consumers are told about it. | Business consumers, dashboards and models, through groups the owner approves. |
notebooks to jobs
Databricks Implementation: From Notebooks to Production Jobs
The most common gap we find in a Databricks implementation is the step between a notebook that works and a job that runs every night without its author watching.
1. Code in version control
Notebooks and libraries live in a Git repository linked to the workspace. Production never runs a notebook that was edited in place.
2. Deployment as code
Jobs, their schedules and their compute are defined in configuration and deployed by a pipeline, using Databricks' bundle-based tooling or your existing CI/CD, so development, test and production differ only by parameters.
3. Service principals, not people
Production jobs run as service principals with only the grants the job needs. No job depends on an engineer's personal token or personal permissions.
4. Tests and data checks
Unit tests on transformation logic, plus data checks at layer boundaries, run before a change is promoted.
5. Alerting and ownership
Each job has an owning team and a failure alert routed to that team, plus a short runbook on what to rerun and what not to touch.
model serving governance
Governing ML and GenAI: Mosaic AI and Model Serving
Mosaic AI is Databricks' name for its machine learning and generative AI tooling, including experiment tracking, a model registry in Unity Catalog, and model serving endpoints. Because models are registered in the same catalog as the data, the access model you built for tables can extend to models.
Our governance work here has four controls. Registration: every model that can reach a serving endpoint is registered with an owner, a training data reference and a version. Promotion: moving a version to production uses an alias or stage change that only an approver group can make, so serving a model is a recorded decision rather than a side effect of a notebook run. Retrieval scope: GenAI and retrieval workloads read only from catalog-governed sources, so a user cannot retrieve through a model what they could not read directly. Monitoring: serving endpoints log requests and responses to tables that the model owner reviews, with a rollback path to the prior version.
For regulated teams, we write these rules to the policy and frameworks you already answer to, such as SR 26-2 model risk guidance, HIPAA, the NAIC Model Bulletin or the NIST AI RMF.
cluster cost controls
Cost Controls on Clusters and Jobs
Databricks cost problems are usually compute problems: all-purpose clusters left running, oversized clusters chosen once and never revisited, and jobs that run on interactive compute.
We put compute policies in place that limit which cluster types, sizes and runtime settings each group can create, require auto-termination, and enforce tags for team, project and environment. Scheduled work moves to job compute, which is created for the run and released after it. SQL workloads get their own warehouses sized to their concurrency. Usage data from the system tables is then reported by tag, so a cost question has an answer that names a team and a workload instead of a shared total nobody owns.
migration to databricks
Moving onto Unity Catalog and Databricks SQL
Migration work on Databricks comes in two shapes, and they need different plans.
Upgrading an existing workspace to Unity Catalog
If your tables still sit in a workspace-local Hive metastore, the upgrade means moving table definitions into catalogs, replacing table access control and mount points with catalog grants and external locations, and re-pointing jobs. We sequence this by dependency, so readers move only after the tables they read have moved.
Moving from a legacy warehouse or Hadoop
From an older warehouse or Hadoop cluster, the work is translating SQL and stored procedures, rebuilding orchestration as Databricks jobs, and landing data into the bronze layer. No consumer switches until its tables match the old system on row counts and key totals.
engagement shape
How a Databricks Engagement Runs
Engagements start with a review of your actual workspace: catalogs, grants, cluster policies, job definitions, and who owns what. From that we agree which of the areas above to tackle first, usually Unity Catalog structure and job productionisation, because everything else depends on them. We build alongside your engineers, and hand over with a named internal owner for each catalog, job group and serving endpoint. Kriv AI sends a senior practitioner who both scopes and delivers.
industry depth
Databricks in Financial Services, Healthcare and Wealth Management
This page covers the platform work. The sector-specific detail lives in separate guides.
For banks and insurers, see our guide to Databricks AI for financial services. For healthcare and life sciences buyers weighing partner accelerators, see our review of Databricks Brickbuilder for healthcare and life sciences. For advisory firms, our guide to wealth management AI on Databricks covers advisor supervision of client-facing output.
databricks partner
Kriv AI as a Databricks Partner
Kriv AI is a "Databricks partner". Our listed status is Partner, Active. We also hold Microsoft's "Solutions Partner for Cloud & AI Platforms · Partner ID 7036589" and are in the "Claude Partner Network, Select partner · Services Track".
We do not have a named Databricks client story we can publish yet. If references matter to your decision, ask during discovery and we will tell you plainly what we can share.
get started
Starting a Databricks Consulting Engagement
Databricks consulting is scoped and priced after a short discovery call. Bring a description of your workspace and the first use case you need in production; we will tell you where we would start and why.
Related resources
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Straight answers
Frequently asked questions about Databricks Consulting for Regulated Data and AI Teams
What does a Databricks consulting partner actually do?
It designs the Unity Catalog structure and permissions, organizes Delta tables into layers with owners, turns notebook code into tested jobs that run as service principals, puts approval and monitoring around model serving, and sets compute policies so costs have an owner. A good partner also tells you which of those to skip for now. Kriv AI offers this work as a Databricks partner.
How much does Databricks consulting cost?
Databricks consulting is scoped and priced after a short discovery call. The size of the work depends on how many catalogs, jobs and sources you have, whether a Unity Catalog upgrade or a warehouse migration is involved, and how many models need serving governance. Our pricing page explains how we structure engagements, and the discovery call turns that into a proposal for your workspace.
We already enabled Unity Catalog. What is left to do?
Usually quite a lot. Enabling it creates the structure; it does not decide catalog naming, which groups own which schemas, where column masks belong, or how access requests are approved. We review the grants you have today, find individual-user grants and tables still outside the catalog, and leave you with an ownership register and a naming standard new work can follow.
How do you move notebooks into production safely?
Code goes into Git, jobs and their compute are defined in configuration and deployed by a pipeline, and production jobs run as service principals with narrow grants. Tests on transformation logic and data checks at layer boundaries run before promotion. Each job gets an owning team, an alert, and a runbook, so a failure at night does not depend on finding the original author.
How do you govern models on Databricks model serving?
Models are registered in Unity Catalog with an owner and a training data reference. Only an approver group can promote a version to production, so serving is a recorded decision. Retrieval workloads read only governed sources, and endpoint requests and responses are logged for review. We write those rules to the policy and frameworks you already answer to, such as SR 26-2.
Can you help reduce our Databricks compute spend?
We start with where the spend goes, using usage data broken down by tag, then apply compute policies: auto-termination, limits on cluster size and type per group, required cost tags, and moving scheduled work from all-purpose clusters to job compute. We do not promise a specific saving before we have seen your usage, because the answer depends entirely on how the workspace is used today.
Which industries do you focus on for Databricks work?
Healthcare, life sciences, financial services and insurance. The platform work is the same everywhere, but in these sectors permissions, lineage and model approval have to be designed in from the start. For sector detail, see our separate guides on Databricks AI for financial services, Brickbuilder accelerators in healthcare and life sciences, and wealth management AI on Databricks.
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