Entropy Data Contracts

Entropy Data data testing

Entropy Data's core feature is data contracts: each one follows ODCS, with schema, quality rules and terms of use. Contract tests run SQL checks through the Data Contract CLI and publish the results to the marketplace.

Compare: data contracts tools

• SQL Table Tests: Quality rules in the data contract include row counts, uniqueness, not-null and custom SQL checks, run by Entropy Data or the Data Contract CLI (docs).

• Data Contracts: Core feature: data contracts follow the Open Data Contract Standard (ODCS) with schema, quality rules and SLAs, tested against real data (docs).

• DataFrame Tests: Checks run against the data contract's servers; no in-memory DataFrame validation is offered in the platform (docs).

• Unstructured Data Tests: No checks on free-text or document data are documented (docs).

• Table Comparison Tests: No comparison of metrics between two tables is documented (docs).

• Data Diff: No row-level diff between datasets is documented (docs).

• Inspect Failed Rows: Check results show which checks failed per server and run; failing row samples are not documented (docs).

Entropy Data data observability

Entropy Data tests contracts rather than observing data: it runs custom SQL checks, defined in YAML or in the UI. It has no profiling or anomaly detection.

• Custom SQL Monitors: Custom SQL quality checks can be defined once under Governance and reused across data contracts, and run on a schedule (docs).

• UI-Based Monitors: Quality rules can be edited in the Data Contract Editor and run with Run Checks in the UI (docs).

• Monitors-as-Code: Data contracts and their quality rules are YAML files, with Git integration and a YAML editor (docs).

• Data Profiling: No data profiling is documented (docs).

• Anomaly Detection: Checks are rule-based from the data contract; no anomaly detection is documented (docs).

• Volume Monitors: Row count checks are contract rules; no volume time-series monitoring is documented (docs).

• Schema Monitors: Contract tests check schema compliance on each run; no continuous schema change monitoring is documented (docs).

• Field Monitors: No column statistics tracked over time are documented (docs).

• Distribution Monitors: No distribution monitoring is documented (docs).

• Segment Monitors: Not documented (docs).

• Partition-Based Monitors: Not documented (docs).

• Window-Based Monitors: Not documented (docs).

• Pipeline Monitoring: Not documented (docs).

• Freshness Monitors: Data contracts can define freshness SLAs, but continuous freshness monitoring is not documented (docs).

Entropy Data incident management

Entropy Data sends alerts when contract tests fail. It has no incident workflow.

• Alert Notifications: Notifications alert owners via Slack, Microsoft Teams, Google Chat and email (docs).

• Incident Triage: No incident workflow is documented (docs).

• Alert Feedback: Not documented (docs).

• Incident Resolution: No incident resolution workflow is documented (docs).

• Ticketing Integrations: ServiceNow is mentioned only for access requests, not data quality issues (docs).

• On-Call Integrations: No PagerDuty or Opsgenie integration is documented (docs).

Entropy Data data quality analytics

Entropy Data shows test results and health scores per data product.

• Data Quality Dashboards: Insights dashboard of governance metrics, plus data quality results per contract (docs).

• Data Health Scores: Data Quality Score gives a mark out of 100 per contract, data product, team, domain or organization, by dimension (docs).

• Incident Resolution Metrics: Not documented (docs).

Entropy Data data lineage

Entropy Data shows table-level lineage between data products.

• Table-Level Lineage: Ingests OpenLineage events and shows a lineage graph of jobs and datasets on data product pages (experimental) (docs).

• Column-Level Lineage: Lineage is shown for jobs and datasets; column-level lineage is not documented (docs).

• End-to-End Lineage: Lineage depends on the OpenLineage events submitted; coverage from source to BI is not documented (docs).

• Impact Analysis: Data product dependencies are shown on a map, but downstream impact analysis is not documented (docs).

Entropy Data data governance

Entropy Data's marketplace, catalog, classification and governance policies make it a data governance tool for data mesh.

• Data Catalog: Data product marketplace for discovering data products, contracts, owners and business definitions (docs).

• Data Catalog Integration: Writes contract descriptions and tags back to Databricks Unity Catalog, Snowflake and the AWS Glue Data Catalog (docs).

• Data Classification: Classification schemes (e.g. GDPR, confidentiality levels) are derived automatically and shown as badges (docs).

• Data Products: Core concept: data products with owners, input and output ports, and data contracts (docs).

Entropy Data workflows

Entropy Data has a REST API, an MCP server and a dbt integration. The Data Contract CLI pushes metadata and runs contract tests from CI/CD pipelines. It has no Python SDK.

• Scheduling: Contract checks run on demand or on a schedule for contracts with configured tags (docs).

• CI/CD Integrations: Push metadata and run contract tests from CI/CD pipelines with the Data Contract CLI (docs).

• REST / GraphQL API: REST API with an OpenAPI spec, including test result publishing (docs).

• MCP Server: MCP server with tools for data products, contracts and access, with OAuth sign-in (docs).

• dbt Integration: The Data Contract CLI turns contract rules into dbt tests and publishes the results (docs).

• Python SDK: The SDK for custom integrations is Java; the separate Data Contract CLI is a Python tool (docs).

• Webhooks: Slack notifications use webhook URLs and events are published as an HTTP feed, but general outbound webhooks are not documented (docs).

Entropy Data AI agents

Entropy Intelligence answers questions about data products and test results in plain language, and writes quality rules from prompts. An MCP server is also available.

• AI Chat Assistant: Entropy Intelligence answers questions about data products and test results in natural language and generates SQL (docs).

• Natural Language Test Generation: AI converts text-based data quality requirements into executable SQL checks (docs).

• Automated Test Generation: Quality rules are written in the data contract; automatic check generation from profiling is not documented (docs).

• AI Issue Triaging: Not documented (docs).

• AI Issue Resolution: Not documented (docs).

Entropy Data data marketplace

Entropy Data lists data products with owners, output ports such as a Snowflake schema, and contracts that consumers request access to.

Compare: data marketplace tools