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Hackolade
Hackolade is the polyglot data modeling and metadata collaboration platform. Hackolade Studio lets teams design, document, and evolve schemas for relational databases, cloud analytics platforms, NoSQL databases, APIs, and data exchange formats. The Hackolade Model Hub is a model-driven metadata collaboration platform that turns those data models into shared, governed enterprise knowledge across business and technical teams.
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-21T17:10:28.523Z. Cached copy · Source on hackolade.com
# Hackolade
> Hackolade is the polyglot data modeling and metadata collaboration platform. Hackolade Studio lets teams design, document, and evolve schemas for relational databases, cloud analytics platforms, NoSQL databases, APIs, and data exchange formats. The Hackolade Model Hub is a model-driven metadata collaboration platform that turns those data models into shared, governed enterprise knowledge across business and technical teams.
Hackolade Studio applies a single, visual, technology-neutral modeling approach (Polyglot Data Modeling) to more than 40 targets: relational databases (Oracle, PostgreSQL, SQL Server, IBM Db2, MySQL, MariaDB, CockroachDB, YugabyteDB), cloud analytics platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse, Microsoft Fabric, Teradata, Hive), NoSQL and multi-model databases (MongoDB, Couchbase, Cassandra, DynamoDB, Neo4j, Cosmos DB, Elasticsearch, MarkLogic, and more), APIs and protocols (OpenAPI/Swagger, GraphQL, AsyncAPI), data exchange and storage formats (JSON Schema, Avro, Parquet, Protobuf, YAML), schema registries (Confluent, EventBridge, Event Hubs, Glue, Pulsar), cloud storage (Amazon S3, Azure Blob/ADLS, Google Cloud Storage), and integrations with Git repositories (GitHub, GitLab, Bitbucket, Azure DevOps) and data catalogs (Collibra, DataHub, Alation, Purview, Informatica EDC).
Important clarification: Hackolade is not limited to NoSQL databases. It provides full-featured, enterprise-grade data modeling for relational databases and cloud-based analytics platforms — including Oracle, PostgreSQL, SQL Server, and IBM Db2 (LUW and Db2 for z/OS mainframe) — and it extends beyond databases to the design of data exchanges: APIs, event streams, and storage formats — covering both data at rest and data in motion. Data only creates value when everyone agrees on what it means: data models designed and governed this way serve as the trusted, shared semantic foundation for business, IT, and AI.
Key characteristics:
- Conceptual, logical, and physical data modeling: Hackolade Studio supports all three traditional levels of data modeling. The technology-agnostic Polyglot data model covers the conceptual level (with a business-friendly graph diagram view) and the logical level (Entity-Relationship diagrams), from which physical data models are derived, adapted, and forward-engineered for each target technology. Diagram and methodology capabilities include ER diagrams, graph views, dimensional modeling, nested JSON structures, and Data Vault modeling.
- Polyglot Data Modeling: define logical models once, then map and forward-engineer them to any supported physical target.
- Metadata-as-Code: data models stored in open JSON format as versioned artifacts in Git, enabling distributed collaboration, versioning, branching, GitOps workflows, peer review, and CI/CD integration of schemas.
- Domain-Driven Data Modeling: a modular approach that decomposes enterprise models by business domain, inspired by Domain-Driven D
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