HighlightSnowflake

Snowflake Information Schema is the per-database data dictionary

Snowflake docs on INFORMATION_SCHEMA views and table functions for database and account-level object metadata.

Snowflake’s Information Schema is a SQL-92 ANSI–based data dictionary implemented as a built-in, read-only INFORMATION_SCHEMA in every database. It exposes views for database objects and account-level objects (roles, warehouses, databases) plus table functions for historical and usage data, mixing ANSI-standard and Snowflake-specific views.

Based on: Snowflake Information Schema | Snowflake Documentation · Snowflake

HighlightNeo4j

Neo4j constraints enforce uniqueness, existence, type, and keys

Neo4j Cypher manual listing property uniqueness, existence, type, and key constraints, and preferring graph types for schema.

Neo4j provides property uniqueness, existence (Enterprise), type (Enterprise), and key (Enterprise) constraints on nodes by label or relationships by type. Older CREATE CONSTRAINT syntax still adds constraints to a database’s graph type, but the docs recommend defining schema via graph types for richer constraint kinds and simpler long-term maintenance.

Based on: Constraints - Cypher Manual · Neo4j

HighlightAstronomer

Astronomer on OpenLineage and Airflow for pipeline lineage

Astronomer Learn guide covering data lineage concepts and why integrating lineage with Apache Airflow matters.

The guide defines data lineage as tracking and visualizing data from origin through downstream consumption, and lists uses such as understanding sources, troubleshooting failures, managing PII, and regulatory compliance. It positions Airflow as a central orchestrator for integrating lineage and introduces how lineage works with Airflow, with pointers to Astro Observe and related webinars.

Based on: Integrate OpenLineage and Airflow - Astronomer · Astronomer

HighlightAirbyte

Airbyte schema-change policies decide how source drift reaches the destination

Airbyte docs on per-connection schema change detection and propagation: new/removed fields and streams, type changes, and breaking cursor/key cases.

Each Airbyte connection can specify how source schema changes are handled. Cloud checks before sync at most every 15 minutes per source; self-managed at most every 24 hours, with manual refresh available. Behaviors cover new and removed columns and streams, type changes, and immediate pause when a cursor is removed.

Based on: Schema change management | Airbyte Docs · Airbyte

HighlightOracle MySQL

MySQL CHECK constraints encode boolean row rules with optional enforcement

MySQL 8.4 reference for CREATE TABLE CHECK constraints: naming, boolean expr, ENFORCED/NOT ENFORCED, table vs column form.

MySQL 8.4 permits core table and column CHECK constraints for all storage engines. A CHECK (expr) must evaluate to TRUE or UNKNOWN per row; FALSE is a violation. Constraints may be ENFORCED or NOT ENFORCED, named with an optional symbol, and declared as table- or column-level.

Based on: MySQL :: MySQL 8.4 Reference Manual :: 15.1.20.6 CHECK Constraints · Oracle MySQL

HighlightApache Software Foundation

Apache Jena’s jena-shacl runs W3C SHACL Core and SPARQL constraints in practice

Jena documentation for jena-shacl: SHACL Core and SPARQL constraints, compact syntax, CLI validate/parse, and Fuseki endpoint integration.

jena-shacl implements W3C SHACL Core and SHACL SPARQL Constraints, plus compact syntax and SPARQL-based targets. The shacl CLI validates and parses shapes; Fuseki can expose a fuseki:shacl operation that accepts a posted shapes graph and optional graph/target parameters.

Based on: Apache Jena - Apache Jena SHACL · Apache Software Foundation

HighlightMorgan & Claypool (open online edition)

A 2018 handbook on RDF validation covering ShEx, SHACL, and data quality

Open HTML edition of Validating RDF Data (Morgan & Claypool, 2018) by Labra Gayo, Prud’hommeaux, Boneva, and Kontokostas.

Validating RDF Data is a Synthesis Lectures book on Semantic Web theory and technology. The contents move from RDF and data quality through Shape Expressions and SHACL, including shapes, constraints, and related tooling. A free HTML edition is maintained with errata and example source.

Based on: Validating RDF Data · Morgan & Claypool (open online edition)

HighlightPostgreSQL Global Development Group

PostgreSQL COMMENT attaches one replaceable note to almost any database object

Official PostgreSQL docs for the COMMENT command: store, replace, or clear a single comment string on tables, columns, functions, and many other objects.

COMMENT ON stores one comment string per database object and replaces any existing comment when reissued. Specifying NULL or an empty string removes the comment. The synopsis lists object kinds from tables and columns through functions, operators, schemas, and more.

Based on: COMMENT · PostgreSQL Global Development Group

HighlightMicrosoft

OneLake catalog centralizes find, govern, and secure for Fabric items

Overview of Microsoft Fabric’s OneLake catalog for discovering items, reviewing governance posture, and managing security roles.

OneLake catalog is a centralized Fabric place to find, explore, and use items and to govern owned data. It is reachable from the Fabric navigation pane and embedded in Teams, Excel, and Copilot Studio; metadata can also be searched via the Catalog Search REST API. Explore, Govern, and Secure tabs cover browsing with filters, governance posture insights, and unified workspace and OneLake security role management.

Based on: OneLake catalog overview - Microsoft Fabric · Microsoft

HighlightSnowflake

Snowflake Horizon Catalog pairs discovery with semantic context for agents

Snowflake documentation introducing Horizon Catalog as an interoperable catalog with governance, lineage, and semantic views for AI.

Horizon Catalog is described as an agentic catalog for data inside and outside Snowflake, open across engines, data, and clouds. It targets discoverability, business context for AI, and trust via protection, quality, lineage, and AI governance. Features include Iceberg interoperability, Internal Marketplace, semantic views, and column-level lineage spanning Snowflake, external systems, BI, and OpenLineage.

Based on: Snowflake Horizon Catalog | Snowflake Documentation · Snowflake

Highlightdbt Labs

dbt Discovery API turns run metadata into queryable project state

dbt Labs docs on the Discovery API for querying models, sources, nodes, and run results from dbt projects.

Each dbt run stores metadata about models, sources, other nodes, and execution results. The Discovery API lets you query that metadata to understand the DAG and the data it produces, and to build monitoring, alerting, lineage exploration, and automated reporting. Access is via ad hoc queries, custom apps, partner integrations, and dbt features such as model timing and data health tiles, at environment or job scope.

Based on: About the Discovery API | dbt Developer Hub · dbt Labs

Highlightdbt Labs

Groups bind DAG nodes to a named owner and private-access boundary

dbt documentation on declaring groups in YAML to organize nodes and restrict access to private models.

A group is a named collection of nodes in a dbt DAG with a required owner. Groups support intentional collaboration by restricting access to private models. Members may include models, tests, seeds, snapshots, analyses, and metrics, but not sources or exposures, and each node belongs to only one group. Groups are declared under a groups key; name and owner are required, with optional description and meta in later versions.

Based on: Add groups to your DAG | dbt Developer Hub · dbt Labs