AI Data Management Platform That Eliminates Busywork

Busywork shouldn’t block high-impact work. DataHub Cloud delivers context-aware AI that automates documentation generation, metadata enrichment, and quality monitoring. Intelligent workflows across your entire data estate mean your teams focus on insights while AI handles the tedious work.

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Automate the metadata work that slows teams down

Connect AI agents to your data stack

DataHub’s hosted MCP Server connects AI tools like Claude, Cursor, and Windsurf to your metadata. Agents search datasets, understand lineage, and generate documentation.

Find data instantly with conversational AI

Ask DataHub answers questions in Slack , Teams, and DataHub using natural language. Find trustworthy datasets, assess data quality, generate SQL, and understand impact analysis without memorizing table names or catalog structures.

Generate documentation automatically

AI analyzes schema, lineage, sample values, and usage patterns to create comprehensive table and column descriptions. Click once and get context-aware documentation that refreshes as your data landscape evolves.

Catch data quality issues before they break pipelines

AI analyzes historical patterns to suggest freshness thresholds, volume expectations, and quality checks with one-click setup. Assertions adapt automatically as your data evolves.

React to data changes with automated workflows

DataHub Actions Framework executes workflows when quality alerts fire, schemas evolve, or data changes. Send notifications, create tickets, or run custom actions with event-driven rules.

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How teams use DataHub to eliminate data incidents

Data analysts find data in seconds using natural language

Ask DataHub answers questions in natural language with complete business context. Find datasets using business terms instead of technical table names.

Data engineers eliminate hours of manual documentation work

AI generates docs, propagates changes, and fields data consumer questions. Manual enrichment becomes automated background operation.

Data scientists vet training data without manual checks

Assess freshness, quality scores, and validation status through Ask DataHub. Vet datasets before feature engineering begins.

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Real automated data management results from enterprise teams

Block Logo

Block accelerates incident response from hours to minutes

“Something that might have taken hours, or days, or even weeks turns into just a few simple, short conversation messages.”

SAM OSBORN
Senior Software Engineer, Block

CHALLENGE

Block manages 50+ data platforms under strict financial compliance. Engineers spent hours searching internal docs, checking Slack channels, manually tracing dependencies, and hunting for stakeholder contact information during incidents.

SOLUTION

Integrated their open source AI agent Goose with DataHub’s MCP Server, enabling conversational access to schema, lineage, ownership, and documentation. Engineers query metadata through natural language without leaving their workflow.

IMPACT

Incident response that previously took hours or weeks now completes in minutes. Engineers verify tables, assess downstream impact, identify data owners, and retrieve stakeholder contact information through simple conversational messages. 

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Built to meet enterprise AI data management requirements

Context-aware automations
  • Hosted MCP Server connects AI agents to your catalog
  • Ask DataHub conversational chat agent
  • AI documentation generation
  • AI-powered classification and propagation
Enterprise performance
  • Real-time bi-directional metadata sync
  • 99.5% uptime SLA
  • Cross-platform coverage
  • Multi-cloud deployment support
Security and extensibility
  • 100+ pre-built connectors
  • Role-based access controls
  • SOC 2 Type II certified infrastructure
  • Comprehensive API documentation

Ready to let AI handle data management busywork?

Data teams shouldn’t spend hours documenting tables and answering the same questions daily.

DataHub Cloud delivers AI data management processes that generate documentation, monitor data quality,  answer user questions, and enrich metadata across your data ecosystem.

Let us show you how it works. Book a demo.

FAQs

AI-powered data management software (like DataHub) eliminates repetitive metadata work that consumes data engineering capacity, shifting team focus from maintenance to building:

  • Find data using business language: Analysts search using business terminology instead of technical schema names—reducing discovery time from hours or days to minutes and eliminating interruptions for engineers.
  • Reduce alert noise with adaptive detection: Smart alerts adapt to seasonal patterns and trends in data quality monitoring—reducing false positives that train teams to ignore alerts.
  • Find data using business language: Analysts search using business terminology instead of technical schema names—reducing discovery time from hours to minutes and eliminating interruptions for engineers.
  • Automate classification and documentation: AI automatically suggests glossary terms (including sensitive data classifications) for hundreds of columns based on your business glossary—eliminating the need to manually tag each field.

These productivity gains come from automation handling routine work at scale. Engineers spend less time answering “where is this data?” and more time on pipeline development, while analysts validate datasets independently instead of waiting for engineer availability.

An AI data platform (like DataHub) automates repetitive metadata work that traditionally requires manual effort across thousands of assets:

  • Generate documentation automatically: AI creates table and column descriptions using context from schema definitions, lineage relationships, and usage patterns—eliminating blank fields that make catalogs useless.
  • Detect anomalies without false alarms: Smart assertions learn normal data behavior patterns, adapting to seasonal trends and business cycles—reducing false positives while maintaining high-quality data.
  • Propagate metadata through lineage: Automated workflows spread glossary terms, tags, and documentation across lineage relationships—ensuring consistent classification without manually tagging every related asset.
  • Suggest relevant glossary terms: AI analyzes column names, data types, descriptions, and sample values to automatically suggest relevant glossary terms from your business vocabulary—streamlining data classification without manual tagging.

These data management automations enable data governance and data quality standards to apply across hundreds of thousands of assets without proportionally scaling team size.

DataHub’s Smart Assertions use machine learning to detect data anomalies that may indicate pipeline issues by learning normal data behavior and detecting deviations before they cascade downstream. The system analyzes historical patterns, accounting for seasonality, trends, and natural variations.

Predictive monitoring types catch failures early:

  • Volume assertions predict expected row counts: Detect incomplete data loads, like alerting when daily ETL loads 500 rows instead of the typical 10,000-12,000, preventing dashboards from breaking.
  • Freshness assertions learn expected update schedules: Catch stale data, like flagging when tables that update every 4 hours go 8 hours without refresh, indicating broken pipelines.
  • Column metric assertions monitor statistical patterns: Track null rates, distributions, and statistical patterns to detect data quality degradation before corrupted inputs reach AI models or reports.
  • Custom SQL assertions track business metrics: Monitor business-specific metrics that indicate pipeline health.

Continuous learning incorporates feedback when engineers mark false positives as expected, refining predictions based on organizational context. Adjustable sensitivity controls balance detection accuracy with alert fatigue.

Yes. DataHub uses AI to automatically generate table and column descriptions—eliminating the blank fields that make traditional catalogs useless for efficient data discovery.

Machine learning analyzes schema definitions, column names, data types, lineage relationships, and sample values to create meaningful descriptions without manual writing. The AI generates documentation that explains what datasets contain, how they’re used, and how they relate to other assets in your platform.

Generated descriptions appear with clear attribution, so data owners can review and refine AI-created content while maintaining governance and accuracy standards.

This automation scales documentation across thousands of enterprise data assets where manual writing creates insurmountable backlogs. Teams that struggle to document datasets get AI-generated baseline descriptions that provide data discovery context immediately, with the option for domain experts to enhance descriptions for high-value assets over time instead of starting from blank fields.

AI automates data governance policy enforcement by identifying compliance gaps and applying corrective actions without manual audits:

  • Classify and tag automatically: Machine learning scans datasets to detect PII, sensitive data, and regulated information—suggesting appropriate glossary tags that can be configured to trigger data access controls and retention policies without waiting for manual steward reviews.
  • Propagate policies through lineage: When AI tags a source column as containing email addresses, that classification flows downstream through transformations—ensuring consistent data security policy enforcement  across all derived assets containing the same sensitive data.
  • Validate governance completeness: AI identifies datasets missing required metadata like ownership, documentation, or domain assignment—flagging non-compliant assets until data governance standards are met.

This scales data governance policies across platforms without proportionally scaling manual oversight, enabling compliance requirements to apply consistently even as data ecosystems grow beyond what human audits can maintain.

Yes. DataHub provides Smart Assertions—AI-powered anomaly detection that learns from historical data patterns and adapts to your specific behaviors. The system trains on past patterns while accounting for seasonality, weekly trends, and natural variations to predict what “normal” looks like.

Smart Assertions monitor multiple data health dimensions:

  • Volume monitoring detects unexpected row count changes: Catch incomplete loads or pipeline failures.
  • Freshness tracking learns update schedules: Alert when tables go stale beyond expected intervals.
  • Column metrics monitor statistical patterns: Track null rates, distributions, and data quality indicators.
  • Custom SQL patterns track any metric: Monitor any business metric or validation rule you define.

Adaptive features include tunable sensitivity controls, configurable training windows that exclude irrelevant historical data, and exclusion periods for maintenance or known anomalies. Feedback loops let teams mark false positives as “normal” or retrain models with new baselines—continuously refining detection accuracy and reducing alert fatigue over time.

Yes. DataHub surfaces ownership information automatically by capturing it from source platforms and propagating it through cross-platform data lineage.

Automated ownership extraction captures ownership directly from platforms like Snowflake and dbt by syncing technical owners from Snowflake role permissions and dbt transformation definitions without manual entry. This eliminates the investigation work typically required to track down who owns what.

DataHub displays ownership prominently on asset pages, showing both technical owners who maintain pipelines and business owners responsible for data quality and data governance. You can search and filter by owner to quickly find all assets a person is responsible for.

Ownership propagation through data lineage automatically identifies responsible parties for derived datasets. For example, if table A has an owner and table B is derived from A, DataHub can propagate that ownership relationship downstream.

DataHub integrates with organizational directories like LDAP and Active Directory to sync your organizational structure, making it easy to see team hierarchies and reporting relationships alongside asset ownership.

Yes. DataHub enforces data governance requirements and validates data quality before issues reach production—preventing compliance violations and pipeline failures rather than discovering them downstream.

  • Pre-commit policy validation: Block non-compliant metadata at ingestion time. Engineers receive immediate feedback when datasets lack required owners or miss PII classifications—eliminating the need to audit catalogs after publication.
  • Event-driven governance: Configure custom actions to respond to metadata changes in real-time through the DataHub Actions Framework. For example, when the tag “PII” appears on a dataset, configure an action for DataHub to automatically notify compliance teams via Slack and create audit tickets in Jira.
  • Proactive data quality monitoring: Validates data freshness, schema changes, volume anomalies, and custom business rules continuously through the Assertions framework. Teams catch breaking changes before they cascade to dashboards, reports, or AI models—reducing time spent firefighting production incidents.

This shift-left approach prevents compliance issues before they require remediation, automates governance workflows that previously consumed engineering hours, and scales policy enforcement across thousands of assets without adding headcount.

Yes. DataHub captures end-to-end lineage across Snowflake, Databricks, dbt, Airflow, and 100+ platforms in the modern data stack. Native connectors automatically extract lineage from data warehouses, transformation layers, orchestration tools, and BI platforms—stitching together dependencies that span multiple systems into a unified lineage graph.

Additional Resources

AI-Driven Data Operations with DataHub Cloud Demo

How Block Enables AI Agents with DataHub MCP Server

Context Management Is the Missing Piece in the Agentic AI Puzzle