Neotask makes your Databricks lakehouse conversational -- Neotask queries Unity Catalog, runs SQL, and orchestrates data workflows so your analysts get answers without writing notebooks.
Query your lakehouse data with plain English instead of writing SQL or Spark code manually
Manage Unity Catalog assets, vector search indexes, and custom functions through conversation
Automate data exploration, report generation, and pipeline monitoring across your Databricks workspace
What You Can Do
Databricks is the engine behind your data and AI strategy. Neotask puts that engine at everyone's fingertips, not just your data engineers.
Natural Language Data Queries
Describe the data you need and your agent translates it into SQL against your Databricks SQL warehouse. Business analysts get answers without waiting for engineering to write queries.
Unity Catalog Exploration
Browse your catalog, understand table schemas, and discover datasets across your organization. Your agent knows what data exists and where to find it.
Vector Search and AI
Query vector search indexes for semantic similarity searches. Power recommendation engines, content discovery, and RAG applications through conversation.
Pipeline Monitoring
Ask about the status of your data pipelines, check job runs, and get notified when something fails. Your agent surfaces errors with context so your data team can fix issues faster.
Every action runs autonomously or requires your approval -- you decide.
Try Asking
"What tables are available in the sales catalog and what do their schemas look like?"
"Show me total revenue by region for Q1 2026 from the sales.orders table"
"Search our product knowledge base for items similar to 'wireless noise-canceling headphones'"
"Which data pipeline jobs failed in the last 24 hours and what were the error messages?"
"Create a custom function that calculates customer lifetime value from our transaction data"
"Run a query to find all customers with more than 5 orders but no activity in the last 90 days"
Pro Tips
Let the agent explore Unity Catalog before writing complex queries -- it writes better SQL when it knows the exact schema.
Schedule daily data quality checks as automations to catch pipeline issues before they affect downstream reports.
Use vector search for customer-facing features like product recommendations or help article suggestions.
Pair Databricks queries with your BI tools or Slack to auto-distribute daily metrics to stakeholders.
Use Genie for business-user-friendly data exploration alongside direct SQL for precise analytical queries.
Enable approval gates for write operations against production catalogs to prevent accidental data modifications.
Multiple workspaces and capacity for larger teams.
Works Well With
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