Dremio + Improvado MCP — Query Your Lakehouse in Plain English
Improvado MCP extracts data from Dremio's semantic layer and makes your virtual datasets, spaces, and catalogs instantly queryable by AI agents.
46K+ metrics · Read & Write access · 500+ platforms · <60s setup
Ask Questions Across Your Entire Data Lakehouse
Stop copying SQL from docs. Improvado MCP connects your AI agent to Dremio so analysts, engineers, and ops teams can query virtual datasets and reflections conversationally.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Show revenue by region from lakehouse last month
1 hr → 2 min
List top 10 tables by query volume this week
45 min → 1 min
Pull pipeline metrics from Arctic catalog
30 min → 45 sec
Example prompts
"Show anomalies across all accounts"
2h → 40s
"CPL in New York vs. California?"
1h → 30s
"ROAS by campaign type, last 30 days"
45m → 15s
Works with
Claude
ChatGPT
Cursor
+5
Write Derived Insights Back to Connected Systems
After querying Dremio, push summaries, anomalies, and curated datasets downstream. Your AI agent closes the loop between lakehouse analytics and operational tools.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Export flagged anomalies to incident tracker
Manual → auto
Write query performance report to shared drive
2 hrs → 5 min
Push curated dataset snapshot to BI layer
1 hr → 3 min
Write actions
"Launch A/B test, $5K budget"
5 days → 20m
"Shift 20% of Display to PMax"
2h → 1m
"Pause all ad groups with CPA > $50"
30m → 10s
🛡
Every action logged · Fully reversible · SOC 2 certified
Monitor Data Quality and Pipeline Health
Set AI watches on reflection staleness, query latency, and dataset freshness. Improvado MCP keeps your agent aware of lakehouse health without manual dashboards.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Alert when any reflection is stale over 2 hours
Manual → auto
Track failed queries by space daily
2 hrs → 5 min
Monitor row count changes in critical datasets
1 hr → 2 min
Monitor prompts
"Flag ad groups over 120% budget"
3h → 1m
"Weekly report: spend, CPA, anomalies"
3h → auto
"Which creatives are fatiguing?"
2h → 30s
Alerts sent to Slack, email, or your AI agent
One conversation. All six phases. Every platform.
The Closed Loop: Read → Decide → Write → Monitor
After querying Dremio, push summaries, anomalies, and curated datasets downstream. Your AI agent closes the loop between lakehouse analytics and operational tools.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
SQL Expertise Gates Lakehouse Access
THE PROBLEM
Non-technical stakeholders can't query Dremio directly, creating bottlenecks where analysts must serve as intermediaries for every data request.
HOW MCP SOLVES IT
Improvado MCP translates natural language into Dremio queries, letting any role access governed data without writing SQL.
What were last quarter's top revenue sources?
Try asking
"Show ROAS across all 120 accounts"
⚡
Answer in seconds
All data sources, one query
Reflection Staleness Goes Unnoticed
THE PROBLEM
Stale reflections silently degrade query performance. Teams only discover the problem after slow dashboards trigger complaints.
HOW MCP SOLVES IT
Improvado MCP lets your AI agent proactively check reflection status and alert on staleness before it impacts downstream users.
Which reflections haven't refreshed in 4+ hours?
Try asking
"What's my CPL in New York vs. California?"
🔍
Full detail preserved
No data loss on export
Cross-Source Joins Require Complex Setup
THE PROBLEM
Joining data across S3, databases, and SaaS sources in Dremio requires careful virtual dataset configuration that slows time-to-insight.
HOW MCP SOLVES IT
Ask cross-source questions in plain English. Improvado MCP leverages Dremio's existing virtual dataset layer to surface joined results instantly.
Join sales data from S3 with CRM pipeline data
Try asking
"PMax vs. Search ROAS for Q1?"
⚖️
Unified data model
Compare anything side by side
Agency CEO
Portfolio health. Client risk. Revenue signals.
Media Strategist
70% strategy, not 70% ops. Auto campaign QA.
Marketing Analyst
Zero wrangling. Cross-platform. AI narratives.
Account Manager
QBR decks auto-generated. Call prep in 30s.
Creative Director
Performance-to-brief. Predict winners before spend.
👥 Teams
One Framework. Five Roles. Zero Setup.
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.
Frequently Asked Questions
What Dremio objects can I query via MCP?
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Improvado MCP exposes virtual datasets, spaces, Arctic catalog branches, reflections, and any physical data sources connected to your Dremio environment.
Does this work with Dremio Cloud and self-managed?
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Yes. Improvado MCP supports both Dremio Cloud and self-managed deployments. You connect your Dremio endpoint once inside Improvado.
Do queries respect Dremio's row-level security?
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Yes. All queries run under the credentials you configure in Improvado. Dremio's access controls and row-level security policies are enforced as normal.
Can I query Iceberg tables through Dremio MCP?
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Yes. Any Iceberg table registered in your Dremio catalog is accessible through Improvado MCP using natural language queries.
How does Improvado handle large result sets?
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Improvado MCP applies intelligent pagination and sampling for large datasets, surfacing representative summaries to your AI agent while keeping response times fast.
Can I monitor data pipeline health through this integration?
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Yes. You can ask your AI agent about query job status, reflection refresh schedules, and dataset update timestamps — all pulled live from Dremio.
What Dremio objects can I query via MCP?
Improvado MCP exposes virtual datasets, spaces, Arctic catalog branches, reflections, and any physical data sources connected to your Dremio environment.
Does this work with Dremio Cloud and self-managed?
Yes. Improvado MCP supports both Dremio Cloud and self-managed deployments. You connect your Dremio endpoint once inside Improvado.
Do queries respect Dremio's row-level security?
Yes. All queries run under the credentials you configure in Improvado. Dremio's access controls and row-level security policies are enforced as normal.
Can I query Iceberg tables through Dremio MCP?
Yes. Any Iceberg table registered in your Dremio catalog is accessible through Improvado MCP using natural language queries.
How does Improvado handle large result sets?
Improvado MCP applies intelligent pagination and sampling for large datasets, surfacing representative summaries to your AI agent while keeping response times fast.
Can I monitor data pipeline health through this integration?
Yes. You can ask your AI agent about query job status, reflection refresh schedules, and dataset update timestamps — all pulled live from Dremio.
Stop Reporting. Start Executing.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
SOC 2 Type II
GDPR
500+ Platforms
46K+ Metrics
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