# 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 data modeling for relational databases and cloud-based analytics platforms, 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 Design.  It becomes easy to compose data products from pieces of existing data models.
- Serverless, sovereign architecture: Hackolade Studio runs either in a client-only desktop application running on Windows, Mac, or Linux.  Or in the browser from a single-page static app delivered via CDN and using with local storage of models — no registration, no cookies, no server-side storage of customer models, and no vendor access to customer data or models; customers retain full control and residency of their metadata.
- Design-first / shift-left: models can be created before implementation or reverse-engineered from existing databases, APIs, and schemas, then evolved through agile cycles.
- Reverse-engineering from many sources: data models can be created not only from live database instances, but also from JSON documents and JSON Schema, DDL files, GenAI-created output formatted in xDBML or Mermaid, SAP PowerDesigner files, XSD exports from erwin Data Modeler and ER/Studio, and Excel spreadsheets — enabling migration from legacy data modeling tools and quick onboarding of existing data assets.
- Forward-engineering outputs: DDL for relational and analytics targets, MongoDB validators, CQL, JSON Schema, Avro, Parquet, Protobuf, OpenAPI specs, plus ArchiMate, dbt, and human-readable documentation in HTML, Markdown, and PDF.
- Custom properties: in addition to the out-of-the-box properties shipped with the product, users can easily define custom properties at any level of a model. Customers use them to track confidentiality, privacy, and other governance or workflow information, and frequently in the context of model-driven code generation.
- Command-Line Interface (CLI): Hackolade Studio functionality is exposed through a CLI so that DevOps CI/CD pipelines can invoke actions — triggered by events or on a schedule — to forward-engineer schema artifacts or documentation, reverse-engineer data sources, detect schema drift, compare and merge models, and more.

## Products

- [Hackolade Studio](https://hackolade.com/features.html): visual polyglot data modeling application for conceptual, logical, and physical data modeling, schema design, reverse-engineering, forward-engineering, and documentation across SQL, NoSQL, APIs, and data exchange formats. Runs in the browser at https://studio.hackolade.com with no registration and local model storage. Models can be reverse-engineered from database instances, JSON and JSON Schema, DDL, xDBML, Mermaid, PowerDesigner, erwin and ER/Studio XSD exports, and Excel.
- [Hackolade Model Hub](https://hackolade.com/help/Overview.html): model-driven metadata collaboration platform providing unified, central access to Hackolade Studio data models stored in Git repositories, so business, governance, and technical stakeholders share a common understanding of the meaning and context of data.
- [Editions and licensing](https://hackolade.com/editions.html): overview of Hackolade Studio editions.
- [Pricing](https://hackolade.com/pricing.html): licensing and pricing information.

## Core concepts

- [What is data modeling](https://hackolade.com/help/Datamodeling.html): the conceptual, logical, and physical levels of data modeling, and how Hackolade Studio supports all three.
- [Polyglot Data Modeling](https://hackolade.com/polyglot-data-modeling.html): one logical modeling language for many physical targets; spans the conceptual and logical levels, from which physical models are derived per target.
- [Metadata-as-Code](https://hackolade.com/metadata-as-code.html): managing data models and schemas in Git with GitOps workflows.
- [Domain-Driven Data Modeling](https://hackolade.com/domain-driven-data-modeling.html): modular, business-domain-oriented decomposition of enterprise data models.
- [Reduce your data debt](https://hackolade.com/reduce-your-data-debt.html): why proactive schema design lowers long-term data quality costs.
- [AI integration approach](https://hackolade.com/help/DatamodelingandtheAIlifecycle.html): Hackolade's approach to AI is built on customer control, confidentiality, and responsible use — AI capabilities disabled by default, customer choice of LLM or provider including self-hosted and on-premises models, and meaningful human oversight.

## Supported technologies

- [Relational databases](https://hackolade.com/json-in-rdbms.html): Oracle, PostgreSQL, SQL Server, IBM Db2, MySQL, MariaDB, CockroachDB, YugabyteDB — including JSON in RDBMS.
- [Cloud analytics platforms](https://hackolade.com/big-data-analytics.html): Snowflake, Databricks Delta Lake, BigQuery, Redshift, Synapse, Microsoft Fabric, Teradata, Hive.
- [NoSQL databases](https://hackolade.com/nosqldb.html): document, column-oriented, key-value, graph, and multi-model databases.
- [Protocols and REST APIs](https://hackolade.com/protocols-and-rest-apis.html): OpenAPI, Swagger, GraphQL, AsyncAPI.
- [Storage and exchange formats](https://hackolade.com/storage-formats.html): JSON Schema, Avro, Parquet, Protobuf, YAML, Joi.
- [Schema registries](https://hackolade.com/schema-registries.html): Confluent Kafka, EventBridge, Azure Event Hubs, AWS Glue, Pulsar.
- [Data catalogs and dictionaries](https://hackolade.com/data-dictionaries.html): Collibra, DataHub, Alation, Azure Purview, Informatica EDC.
- [Cloud storage](https://hackolade.com/cloud-storage.html): Amazon S3, Azure Blob Storage, ADLS, Google Cloud Storage.

## Documentation

- [Online user manual](https://hackolade.com/help/index.html): complete documentation for Hackolade Studio and the Model Hub, including tutorials and how-to guides.
- [eLearning and certifications](https://community.hackolade.com/slides/all): free training courses and certification paths.
- [Model Hub documentation](https://hackolade.com/help/HackoladeModelHub.html): overview, architecture, and FAQs for the Hackolade Model Hub.
- [FAQ and troubleshooting](https://hackolade.com/help/FAQandtroubleshooting.html): frequently asked questions.
- [Sample models](https://hackolade.com/samplemodels.html): example data models for supported targets.

## Company

- [About Hackolade](https://hackolade.com/company.html): company background, team, and newsroom. Hackolade pioneered data modeling for NoSQL databases and has since expanded to relational databases, cloud analytics platforms, APIs, and data exchange formats.
- [Customer references](https://hackolade.com/references.html): customers and testimonials.
- [Blog](https://hackolade.com/blog.html): articles on data modeling, metadata management, and schema design.

## Optional

- [Videos](https://hackolade.com/videos.html): product demos and tutorials.
- [Books](https://hackolade.com/books.html): recommended reading on data modeling.
- [PowerDesigner migration](https://hackolade.com/landing/hackolade-studio-best-alternative-to-sap-powerdesigner.html): Hackolade Studio as an alternative to SAP PowerDesigner.
- [Release notes](https://hackolade.com/versionInfo/ReadMe.txt): version history for Hackolade Studio.