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One MCP connection. Full lakehouse context. No more SQL bottlenecks — just ask.
Stop writing ad-hoc SQL and waiting for notebook runs. Ask your AI agent to query Unity Catalog tables, explore Delta Lake schemas, surface data quality issues, and pull business metrics — across any catalog, schema, or workspace.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Go beyond querying. Your AI agent can trigger pipeline runs, update table properties, create jobs, and modify Unity Catalog metadata — from a single prompt, without opening the Databricks UI.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set AI-powered watches on pipeline health, data quality metrics, and table freshness. Get proactive alerts when row counts drop, null rates spike, or jobs fail — before downstream reports are impacted.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Go beyond querying. Your AI agent can trigger pipeline runs, update table properties, create jobs, and modify Unity Catalog metadata — from a single prompt, without opening the Databricks UI.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
When custom field mappings and standard Improvado conversions both write to the same Databricks destination, fields don't appear as expected. Diagnosing whether the issue is in the extraction, transformation, or load layer requires querying multiple tables and tracing lineage — a task that can consume an entire day.
Ask your AI agent to trace a specific field through the pipeline layers: raw → staged → harmonized. It identifies where the value drops off or gets overwritten, and returns the root cause with the relevant table and column.
When LinkedIn campaign names or ad names arrive as null in the Databricks destination, attribution models silently misattribute spend. Identifying the scope — which accounts, which date ranges, which connectors — requires querying raw and harmonized tables and joining them with account metadata.
Ask your AI agent to quantify the null name issue across all accounts and date ranges in one prompt. It surfaces the affected records, scopes the impact on attribution, and identifies whether the issue is upstream (connector) or downstream (transformation).
Running a proof-of-concept that spans Databricks and Azure services means querying both environments, comparing schemas, validating data consistency, and documenting findings — typically across multiple tools and windows. The coordination overhead slows down POC cycles significantly.
Ask your AI agent to query both Databricks and Azure data sources in the same session. It validates schema alignment, checks row count parity, and produces a POC readiness report — without switching tools or environments.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Databricks MCP is a Model Context Protocol server that connects your Databricks lakehouse — including Unity Catalog, Delta Lake tables, jobs, and pipelines — to AI agents like Claude, ChatGPT, and Gemini. It lets you query and manage Databricks in natural language.
Unity Catalog tables and schemas, Delta Lake data, Databricks SQL warehouses, job runs and pipeline statuses, cluster metadata, and workspace configuration. Queries execute against your existing Databricks SQL warehouse.
Both. Read operations cover querying tables, exploring schemas, and checking job statuses. Write operations include triggering job runs, creating and updating tables, modifying Unity Catalog tags and properties, and inserting data. All operations require appropriate Databricks service principal permissions.
Improvado connects via Databricks SQL warehouse using your workspace URL, personal access token or service principal credentials. Queries run on your existing warehouse — no additional compute is provisioned by Improvado.
Yes. Improvado stores all Databricks credentials in an encrypted vault certified to SOC 2 Type II. The AI model never has direct access to your lakehouse — requests are proxied through Improvado's secure layer with prompt injection protection.
Under 5 minutes. Provide your Databricks workspace URL and access token, add the MCP server URL to your config, and start querying. No infrastructure changes required on your Databricks side.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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