Stemma Data Catalog
Stemma is an AI-powered data catalog platform, founded in 2020 and headquartered in Redmond, Washington, that automates metadata management and data discovery.
Publisher review
Stemma is an AI-powered data catalog platform, founded in 2020 and headquartered in Redmond, Washington, that automates metadata management and data discovery. It is designed for data engineers, analysts, and business users who need to find, understand, and trust data across a complex, multi-tool data stack. The platform integrates with data warehouses, business intelligence tools, collaboration software, and security tools, offering nearly 20 built-in data connectors. Stemma’s primary value proposition is its ability to accelerate analytics productivity by reducing the time spent on manual documentation and data search, making it particularly suited for organizations using Teradata’s Vantage platform after its acquisition.
The platform works by automatically crawling and indexing metadata from connected sources, then applying AI to surface relevant datasets, metrics, and lineage. It supports a wide range of connectors including AWS Athena, AWS Redshift, Google BigQuery, PostgreSQL, Snowflake, dbt, Apache AirFlow, Tableau, Mode Analytics, Looker, Metabase, Slack, Jira, any email provider, Okta, G-Suite, Azure Active Directory, and Duo. This breadth allows Stemma to ingest metadata from data warehouses, transformation tools, BI dashboards, and identity providers, creating a unified catalog that spans technical and business contexts. The AI-based search and exploration capabilities help users find data using natural language queries, while automated lineage tracking shows how data flows through pipelines and transformations.
Stemma competes in a crowded market against established players like Collibra Data Catalog, Alation Data Catalog, Atlan, Informatica, Acryl Datahub, Amundsen, Microsoft Purview, Select Star, and Data.world. Its key differentiator is its deep integration with Teradata’s Vantage platform, which it was acquired to enhance. The acquisition by Teradata in 2023 brought all Stemma employees, including engineers and metadata experts, into Teradata to advance product roadmap in data lineage, data governance, and data compliance. This move positions Stemma as a core component of Teradata’s data fabric strategy, rather than a standalone catalog competing head-to-head with broad-platform vendors.
The honest trade-offs: Stemma’s tight coupling with Teradata means it is less relevant for organizations not using Vantage, and its standalone product roadmap may deprioritize non-Teradata integrations. The platform’s pricing is not publicly specified, creating uncertainty for budget planning. Persistent concerns about review authenticity and incentivized review bias on third-party sites like G2 make it difficult to gauge real user satisfaction. Additionally, the pay-to-play model of many review platforms creates structural conflicts of interest, and selective moderation has been reported, so prospective buyers should rely on direct trials and reference calls rather than online ratings.
How it works
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AI-powered data search
Uses AI to let users find datasets and metrics with natural language queries, reducing time spent on manual data discovery.
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Automated metadata ingestion
Automatically crawls and indexes metadata from nearly 20 built-in connectors, including Snowflake, dbt, and Tableau.
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Broad connector support
Integrates with AWS Athena, Redshift, BigQuery, PostgreSQL, Snowflake, dbt, AirFlow, Tableau, Mode, Looker, Metabase, Slack, Jira, Okta, and more.
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Data lineage tracking
Shows column-level lineage across pipelines and transformations, helping users understand data provenance and impact analysis.
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Integration with security tools
Connects with Okta, G-Suite, Azure Active Directory, and Duo for role-based access control and governance.
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Teradata Vantage integration
Deeply integrated into Teradata’s Vantage platform to accelerate analytics productivity and strengthen data fabric.
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Collaboration and alerting
Sends notifications via Slack, Jira, and email to keep teams informed about schema changes, data quality issues, and new assets.
Strengths and trade-offs
Strengths
- Provides nearly 20 built-in data connectors, covering major data warehouses, BI tools, and identity providers like Snowflake, Tableau, and Okta.
- Uses AI to automate metadata ingestion and enable natural language search, reducing manual documentation effort for data teams.
- Deep integration with Teradata’s Vantage platform accelerates analytics productivity and strengthens Teradata’s data fabric strategy.
- Acquisition by Teradata brought all engineers and metadata experts into the company, ensuring continued development of data lineage and governance features.
Trade-offs
- Pricing is not publicly specified, making it difficult for organizations to budget or compare costs without a sales conversation.
- Tight coupling with Teradata’s Vantage platform may limit its appeal for organizations using other primary data warehouses or analytics stacks.
- Persistent concerns about review authenticity and incentivized review bias on third-party platforms like G2 make it hard to gauge real user satisfaction.
- Selective moderation and inconsistent review approval reported by users on review sites create uncertainty about the reliability of online ratings.
Pricing context
Not publicly specified; contact Teradata for pricing details.
Getting started with Stemma Data Catalog
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Sign up for Stemma
Go to the Stemma website and click the sign-up button. Provide your work email and create a password. Verify your email address to activate your account and access the onboarding wizard.
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Connect your data sources
In the Stemma dashboard, click Add Connection. Choose from the list of supported connectors like Snowflake, BigQuery, or Tableau. Enter the required credentials and test the connection to ensure metadata ingestion starts.
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Configure metadata ingestion
Set the ingestion schedule to automatic or manual. Define which schemas, tables, or dashboards to crawl. Enable column-level profiling and lineage tracking to capture data transformations from tools like dbt or AirFlow.
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Search for datasets
Type a natural language query in the search bar, such as 'monthly sales by region'. Review the AI-ranked results, click a dataset to see its schema, lineage, and related metrics. Use filters to narrow by source or owner.
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Set up alerts and collaboration
Navigate to the Alerts section and create rules for schema changes or data quality issues. Connect Slack or Jira to receive notifications. Share dataset links with your team via email or Slack to improve data discovery.
Frequently Asked Questions
What is Stemma Data Catalog and what does it do?
Stemma is an AI-powered data catalog platform that automates metadata management and data discovery. It helps data engineers, analysts, and business users find, understand, and trust data across complex data stacks using natural language search and automated lineage tracking.
How does Stemma integrate with Teradata Vantage?
Stemma is deeply integrated into Teradata’s Vantage platform after its 2023 acquisition. All Stemma employees joined Teradata to advance product roadmap in data lineage, governance, and compliance, positioning Stemma as a core component of Teradata’s data fabric strategy.
What data connectors does Stemma support?
Stemma offers nearly 20 built-in connectors including AWS Athena, Redshift, Google BigQuery, PostgreSQL, Snowflake, dbt, Apache AirFlow, Tableau, Mode Analytics, Looker, Metabase, Slack, Jira, Okta, G-Suite, Azure Active Directory, and Duo for comprehensive metadata ingestion.
What are the main strengths of Stemma Data Catalog?
Stemma provides nearly 20 built-in data connectors, uses AI to automate metadata ingestion and enable natural language search, and offers deep integration with Teradata’s Vantage platform. Its acquisition by Teradata ensures continued development of data lineage and governance features.
What are the weaknesses or limitations of Stemma?
Stemma’s pricing is not publicly specified, making budget planning difficult. Its tight coupling with Teradata Vantage limits appeal for organizations using other primary data warehouses. Concerns about review authenticity on third-party sites like G2 also create uncertainty about user satisfaction.
How does Stemma compare to other data catalogs like Collibra or Alation?
Stemma competes with Collibra, Alation, Atlan, and others, but its key differentiator is deep integration with Teradata’s Vantage platform. Unlike broad-platform vendors, Stemma is now a core component of Teradata’s data fabric, making it less relevant for non-Teradata users.
Alternatives
How Stemma Data Catalog compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Stemma Data Catalog
- Pricing
- Not publicly specified; contact Teradata for pricing details.
- Target
- Stemma is an AI-powered data catalog platform, founded in 2020 and headquartered in Redmond, Washington, that automates metadata management and data discovery.
- Strength
- Provides nearly 20 built-in data connectors, covering major data warehouses, BI tools, and identity providers like Snowflake, Tableau, and Okta.
- Watch for
- Pricing is not publicly specified, making it difficult for organizations to budget or compare costs without a sales conversation.
Atlan
- Pricing
- Custom/Contact sales
- Target
- Enterprise data teams
- Deployment
- Cloud, on-prem
- Strength
- AI-driven active metadata
- Watch for
- Pricing escalates with usage
Alation
- Pricing
- Custom/Contact sales
- Target
- Data governance-focused teams
- Deployment
- Cloud, on-prem
- Strength
- Behavioral Analysis Engine
- Watch for
- Complex setup for smaller teams
Collibra
- Pricing
- Custom/Contact sales
- Target
- Governance-heavy enterprises
- Deployment
- Cloud, hybrid
- Strength
- Strong compliance features
- Watch for
- Steep learning curve
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Sources
Reporting on this tool draws on these publicly available sources.