Data Knowledge Graph
Data Knowledge Graph by Datafold is an AI-powered context layer designed for data engineering teams that rely on AI coding agents.
Publisher review
Data Knowledge Graph by Datafold is an AI-powered context layer designed for data engineering teams that rely on AI coding agents. It automatically collects and unifies context across data, pipelines, and analytical products, serving lineage, business logic, usage, ontology, and organizational knowledge via the Model Context Protocol (MCP). This tool is built for organizations that want to reduce manual data engineering work, maintain data integrity, and enhance transparency around data quality and metrics. It is particularly useful for teams using dbt, SQL, and BI tools, as it helps prevent catastrophic errors like deleting important columns downstream and improves the accuracy of business metrics.
The Data Knowledge Graph operates through four layers of knowledge: ontology, business context, data flow, and source code. The ontology layer automatically derives business entities and their relationships, so agents understand the domain, not just tables. The business context ingests documentation, Slack conversations, Notion pages, and other unstructured sources to capture tribal knowledge. The data flow maps column-level lineage across the entire data stack, from source tables through transformations to BI dashboards and reverse ETL syncs. The source code indexes SQL, dbt models, stored procedures, and pipeline definitions across repositories, including git history and code ownership. This enables real-time data diffing and automated data engineering tasks like code translation and validation.
Data Knowledge Graph competes with data lineage and catalog tools like Atlan and Informatica. Unlike traditional data catalogs that rely on human curation, Data Knowledge Graph is built and maintained by AI, optimized for consumption by MCP-compatible agents. It provides a more dynamic and automated approach to context management, reducing the need for manual documentation and metadata entry. However, it is not a standalone data catalog or governance platform; it is a context layer that enhances AI agents, and some features are still in private beta.
Trade-offs include the fact that graph databases, which underpin the knowledge graph, are not recommended over relational databases for certain operations like joins, grouping, and filtering on large datasets. The tool's reliance on AI means its accuracy depends on the quality of ingested data and the AI models used. Additionally, some features are in private beta, limiting immediate access. Pricing is not explicitly mentioned in the sources, so potential users need to contact Datafold for details. The tool is best suited for teams already using MCP-compatible agents and may require integration effort with existing data stacks.
How it works
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Automated data engineering tasks
Reduces manual work for data engineers by automating code translation, validation, and migration using AI agents.
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Data lineage visualization
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AI-powered code translation and validation
Translates and validates code automatically, ensuring data integrity during migrations and optimizations.
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MCP-based context serving
Serves lineage, business logic, usage, ontology, and organizational knowledge via MCP for any compatible agent.
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Unified context collection
Collects and unifies context across data, pipelines, and analytical products automatically.
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Automatic business entity derivation
Derives business entities and their relationships from data, helping agents understand the domain.
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Unstructured source ingestion
Ingests documentation, Slack conversations, Notion pages, and other unstructured sources to capture tribal knowledge.
Strengths and trade-offs
Strengths
- Reduces manual work for data engineers by automating context collection and serving it to AI agents, minimizing repetitive tasks.
- Maintains data integrity by mapping column-level lineage across the entire stack, preventing errors like deleting important columns downstream.
- Enhances transparency around data quality and metrics by providing a unified view of data flow and business logic.
- Improves accuracy of business metrics by automatically deriving business entities and their relationships from data.
Trade-offs
- Graph databases, which underpin the knowledge graph, are not recommended over relational databases for operations like joins, grouping, and filtering on large datasets.
- Some features are in private beta, limiting immediate access and requiring users to wait for full availability.
- Relies on AI models, so accuracy depends on the quality of ingested data and the AI's ability to interpret unstructured sources like Slack conversations.
- Pricing is not publicly disclosed, requiring potential users to contact Datafold for a quote, which may hinder upfront evaluation.
Pricing context
Not explicitly mentioned in the sources; contact Datafold for pricing details.
Getting started with Data Knowledge Graph
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Sign up for Data Knowledge Graph
Visit Datafold's website and sign up for Data Knowledge Graph. Provide your email and organization details. Since pricing is not public, contact sales to discuss access and setup requirements for your team.
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Connect your data stack
Integrate Data Knowledge Graph with your data stack by connecting dbt, SQL databases, and BI tools. Follow the setup wizard to authenticate and grant read access to your data sources, pipelines, and repositories.
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Configure context layers
Set up the four knowledge layers: ontology, business context, data flow, and source code. Ingest documentation, Slack conversations, and Notion pages to capture tribal knowledge. Map column-level lineage across your stack.
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Run a data lineage query
Use an MCP-compatible AI agent to query the Data Knowledge Graph for column-level lineage. Ask about the impact of deleting a specific column or the source of a metric. Review the returned lineage and business context.
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Schedule automated context updates
Configure automated context collection to run on a schedule or trigger. Set the tool to re-ingest source code, pipeline definitions, and unstructured documents regularly, ensuring the knowledge graph stays current for your AI agents.
Frequently Asked Questions
What is Data Knowledge Graph by Datafold?
Data Knowledge Graph is an AI-powered context layer for data engineering teams using AI coding agents. It automatically collects and unifies context across data, pipelines, and analytical products, serving lineage, business logic, and organizational knowledge via the Model Context Protocol (MCP).
How does Data Knowledge Graph differ from traditional data catalogs like Atlan?
Unlike traditional data catalogs that rely on human curation, Data Knowledge Graph is built and maintained by AI, optimized for consumption by MCP-compatible agents. It provides a more dynamic and automated approach to context management, reducing manual documentation and metadata entry.
What are the four layers of knowledge in Data Knowledge Graph?
The four layers are ontology, business context, data flow, and source code. Ontology derives business entities and relationships. Business context ingests unstructured sources like Slack. Data flow maps column-level lineage. Source code indexes SQL, dbt models, and pipeline definitions.
What are the main features of Data Knowledge Graph?
Key features include automated data engineering tasks, data lineage visualization across the entire stack, AI-powered code translation and validation, MCP-based context serving, unified context collection, automatic business entity derivation, and ingestion of unstructured sources like Slack and Notion.
What are the trade-offs of using Data Knowledge Graph?
Graph databases underpinning the knowledge graph are not recommended for joins or filtering on large datasets. Accuracy depends on ingested data quality and AI models. Some features are in private beta, limiting access. Pricing is not public, requiring contact with Datafold.
How much does Data Knowledge Graph cost and is it available now?
Pricing is not explicitly mentioned in available sources; potential users need to contact Datafold for details. Some features are still in private beta, so immediate access may be limited. It is best suited for teams already using MCP-compatible agents.
Alternatives
How Data Knowledge Graph compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Data Knowledge Graph
- Pricing
- Not explicitly mentioned in the sources; contact Datafold for pricing details.
- Target
- Data Knowledge Graph by Datafold is an AI-powered context layer designed for data engineering teams that rely on AI coding agents.
- Strength
- Reduces manual work for data engineers by automating context collection and serving it to AI agents, minimizing repetitive tasks.
- Watch for
- Graph databases, which underpin the knowledge graph, are not recommended over relational databases for operations like joins, grouping, and filtering on large datasets.
Neo4j
- Pricing
- $65/user/month for AuraDB Professional
- Target
- Graph database for enterprise knowledge graphs and analytics
- Deployment
- Cloud, on-premises
- Strength
- Native graph storage with Cypher query language
- Watch for
- Pricing escalates steeply at scale; recent license changes
TigerGraph
- Pricing
- Custom/Contact sales; free community edition with limits
- Target
- Deep link analytics and real-time graph queries
- Deployment
- Cloud, on-premises
- Strength
- Deep link analytics with parallel graph processing
- Watch for
- Complex setup and high cost for production deployments
ArangoDB
- Pricing
- Free community; $1,400/month for ArangoDB Cloud
- Target
- Multi-model database for graph, document, and key-value
- Deployment
- Cloud, on-premises
- Strength
- Multi-model support with SQL, Cypher, and Gremlin
- Watch for
- Graph performance lags native engines in deep traversals
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Sources
Reporting on this tool draws on these publicly available sources.