01
Polyglot Data Modeling
Define meaning once in a technology-agnostic model, then derive physical schemas for 45+ targets. From Oracle and Databricks to MongoDB, OpenAPI, and Avro. A unified approach for data-at-rest and data-in-motion.
Polyglot Data Modeling
Enterprise-grade unified platform for data modeling across relational databases, cloud analytics,
NoSQL databases, APIs, data exchanges, and event streams.
Turn your data models into a semantic foundation for trusted data across business, IT, and AI.
Manage metadata as code with Git-based collaboration, plus sovereignty for your metadata and AI exchanges.
- 01 DesignConceptual, logical, and physical data modeling with ER diagrams
- 02 Reverse-EngineerReverse-engineer from live databases, DDLs, JSON, Excel, diagramming or legacy tools, and GenAI output
- 03 Forward-EngineerForward-engineer DDLs, schemas, validators, and documentation, or publish to metadata management suites
- 04 VersionData models versioned as code in your Git repositories for distributed collaboration, versioning, branching, change tracking, peer reviews
- 05 CollaborateModel-driven metadata collaboration with lineage, where-used, impact analysis, glossaries of business terms
- 06 AIAI-assisted data modeling with the LLMs of your choice, even proprietary or on-prem. Nothing gets routed through us
- 07 SovereigntyServerless, sovereign, security-first architecture with no on-prem server, no vendor access to your data or data models
Free to start · No registration · No download · Your models never leave your machine
Trusted by renowned brands worldwide
No shared meaning, no trusted data.
No trusted data, no trustworthy AI.
Many organizations struggle to gather the semantics for all their data. Schemas are scattered across dozens of databases, pipelines, and APIs, each maintained by different teams. Documentation drifts from reality. Business and IT use the same words for different things, and different words for the same things.
So nobody can say with confidence what a number in a report actually represents. The risk when pointing AI at structures that nobody designed or explained is to make erroneous decisions based on those ambiguities.
A diagram is not a data model
An ERD makes it easy for humans to understand structure. Systems do not read pictures. They instead consume schema contracts such as SQL DDLs, JSON Schema, Avro, Parquet, Protobuf, OpenAPI, GraphQL, etc.
A pipeline is not a definition
Meaning breaks at the seams between systems. Between relational and NoSQL databases, object storage, cloud analytics platforms, lakehouses, APIs, and streams, each keeps its own version. So a change agreed in one place may never reach the others.
A data catalog is not a design
An inventory tells you what your data looks like today, including whatever is already wrong with it. Nothing records what it was meant to look like, or how it should evolve. So the catalog becomes one more metadata silo rather than the place where semantic meaning is reconciled and shared.
Don't create another inventory of what already exists. Instead, design the semantic meaning of data, keep that design alive, and make it available to everyone and every system that consumes your data. Make your data models first-class, versioned artifacts.
From data modeling to a shared understanding
A single end-to-end solution: design and evolve data models in Hackolade Studio (in your browser or on your desktop), version them in your Git repository, and share them with the whole organization through the Hackolade Model Hub.
01 · DESIGN
Hackolade Studio
ER diagram data modeling for 45+ targets. Design conceptual, logical, and physical data models; reverse-engineer existing databases, files, legacy data models; forward-engineer DDL, schemas, validators, and documentation, or publish to metadata management suites; leverage AI-assisted data modeling. Run it in your browser with nothing to install, or download the desktop application. Either way, your data models stay local or in your Git repositories, never on Hackolade servers.
02 · SHARE
Hackolade Model Hub
Promote a shared meaning for your data. The Model Hub gives the whole organization central, searchable access to the semantic meaning of your data; business analysts, data stewards, and engineers can all agree on how to interpret the data. An MCP server running strictly inside your infrastructure extends that same understanding to only your authorized users and AI agents.
Designed in Studioversioned in your Git repositoriesaccessed in Model Hubone source of truth for the whole organization.
Why teams choose Hackolade
Learn more
02
Conceptual, logical, and physical data models
All three levels of data modeling are connected but modular: business-friendly graph view for concepts, entity-relationship diagrams for logic, and per-target physical data models, plus a tabular grid view for business users.
03
Reverse-engineering of existing instances
Create data models from live databases, DDLs, JSON and JSON Schema, Excel, PowerDesigner, erwin and ER/Studio exports, even GenAI output in xDBML or Mermaid. Your existing assets become governed data models in a few minutes.
04
Forward-engineering for systems to consume
Generate the artifacts your platforms can execute: DDLs for relational and analytics targets, MongoDB validators, CQL, JSON Schema, Avro, Parquet, Protobuf, OpenAPI specs, plus Excel, dbt, ArchiMate for EA, and documentation in HTML, Markdown, and PDF.
05
Metadata-as-Code
Data models are open JSON files in your Git repositories: distributed collaboration, branching, versioning, peer reviews, change tracking, and automation through the CLI in your CI/CD pipelines. Including schema comparison and drift detection, and schema evolution.
06
Domain-Driven Data Modeling
Decompose enterprise models by business domain instead of maintaining one giant monolith. Reuse pieces of existing data models to compose data products, for consistency, quality and governance, while keeping clear ownership per domain.
07
Model-driven metadata collaboration
The Model Hub gives business, governance, and technical teams shared, searchable access to the data model library in your Git, including glossaries, lineage, and impact analysis, so that everyone works from the same meaning.
08
AI on your own terms
AI-assisted data modeling without data leakage. Nothing passes through Hackolade infrastructure. AI is off by default, and when enabled, you choose the LLM and data residency.
The MCP server runs inside your infrastructure, giving only your selected agents and users access to your Git-based data models library.
09
Serverless and sovereign architecture
In the browser or on the desktop, with no server to install and no vendor cloud in the loop. As a vendor, we never see your data or your data models. Collaboration without surrendering custody of your metadata.
All targets
45+ physical targets, one logical polyglot model.
Relational, cloud analytics, and NoSQL databases, plus APIs and storage/exchange formats
Alation
AllegroGraph
Amazon DocumentDB
Amazon S3
ArangoDB
Avro Schema
Azure Blob Storage
Azure Data Lake Storage
Azure DevOps Repos
Azure Purview
BigQuery
Bigtable
Bitbucket
Bizzdesign
Cassandra DataStax
CockroachDB
Collibra
Confluent Kafka
Cosmos DB
Cosmos DBvCore
Couchbase
Databricks Unity Catalog
DataHub
dbt LabsDelta Lake Databricks
DynamoDB
Elasticsearch
Event Hubs
EventBridge
Firebase Firestore
GitHub
GitLab
Glue Data Catalog
Google Cloud Storage
GraphDBOntotext
GraphQL
gRPC/Protobuf
HBase
Hive
IBM Db2 for LUWand z/OS (mainframe)
Informatica Enterprise Data Catalog
JanusGraph
Joi Schema
JSON Schema
MariaDBSkySQL
MarkLogic
Microsoft Fabric
MongoDB
MySQL
Neo4j
Neptune w/ Gremlin API
Neptune w/ SPARQL API
OpenAPI
Oracle andAutonomous
Oracle JSON Collections
Parquet
PostgreSQLEnterpriseDB
Pulsar
Redshift
ScyllaDB
Snowflake
SQL Server Azure SQL
Stardog
Swagger
SwaggerHub
Synapse
Teradata
TigerGraph
TinkerPop w/ Gremlin API
YAML
YugabyteDB YSQLAlationAllegroGraphAmazon DocumentDBAmazon S3ArangoDBAvro SchemaAzure Blob StorageAzure Data Lake StorageAzure DevOps ReposAzure PurviewBigQueryBigtableBitbucketBizzdesignCassandra DataStax
CockroachDBCollibraConfluent KafkaCosmos DBCosmos DBvCore
CouchbaseDatabricks Unity CatalogDataHubdbt LabsDelta Lake DatabricksDynamoDBElasticsearchEvent HubsEventBridgeFirebase Firestore
GitHubGitLabGlue Data Catalog
Google Cloud StorageGraphDBOntotext
GraphQLgRPC/ProtobufHBaseHiveIBM Db2 for LUWand z/OS (mainframe)
Informatica Enterprise Data CatalogJanusGraphJoi SchemaJSON SchemaMariaDBSkySQL
MarkLogicMicrosoft FabricMongoDBMySQLNeo4jNeptune w/ Gremlin API
Neptune w/ SPARQL API
OpenAPIOracle andAutonomous
Oracle JSON CollectionsParquetPostgreSQLEnterpriseDB
PulsarRedshiftScyllaDBSnowflakeSQL Server Azure SQL
StardogSwaggerSwaggerHubSynapseTeradataTigerGraphTinkerPop w/ Gremlin APIYAMLYugabyteDB YSQLWhat our customers say
It has been an absolute pleasure to see the wild success of Hackolade and the brilliant contribution that it is to the data world in general as well as for us specifically. Thank you for being the BEST vendor/partner I’ve ever had the privilege to work with.
The support I've received from Hackolade during our migration from our previous DM tool is the best support I have ever received from an organization, anywhere, ever. The response time and diligence that goes into addressing our challenges is phenomenal. You guys raise the bar to a level that may not be attainable by anyone else. Thank you!
Hackolade is one of the most responsive vendors I have ever worked with. They love their product and strive to continuously improve it. They listen to their customers carefully and do their best to meet the customers needs.
The most responsive and helpful software company I have ever dealt with. Thank you 😊
The best customer service professionals ever! Hackolade customer service always makes me feel like my issues are their top priority. They always provide timely responses to questions and/or any issue that I submit. They communicate solutions that are easily understood. For example, I found myself in the middle of a deadline needing assistance early one morning on the weekend. I submitted a support question, to my surprise within a few minutes I had a resolution. Thank you for always following through.
You guys rock! The same way NoSQL has disrupted the database market, Hackolade is disrupting the traditional ER data modeling players!
I, like many others, can validate that Hackolade is probably the best option in the market, regardless of your ecosystem being completely in the Cloud, migrating to, or plans to coexist across. Loving their most recent support for #snowflake in addition to so many more.
I have to say... your modeling tool really fills a gap for us. There really isn't another tool out there that meets our needs like this one. Great work!
Recognition
Oracle Global Leaders award
In Las Vegas in October 2025, Hackolade received the Oracle Global Leaders Champion award for the Hackolade Model Hub, our model-driven metadata collaboration platform.
Credential
Hackolade's founder co-authored the Oracle Database 23ai/26ai data modeling book with Oracle's JSON architect Beda Hammerschmidt and data modeling author Steve Hoberman.
Frequently asked questions
Is Hackolade only for NoSQL databases?
No. Hackolade pioneered NoSQL data modeling, and today provides enterprise-grade data modeling for relational databases (Oracle, PostgreSQL, SQL Server, IBM Db2 including z/OS, MySQL, MariaDB) and cloud analytics platforms (Snowflake, Databricks, BigQuery, Redshift), as well as APIs, event streams, and data exchange formats.
Does Hackolade support conceptual, logical, and physical data modeling?
Yes, all three levels. The technology-agnostic Polyglot model covers the conceptual level (business-friendly graph view) and the logical level (entity-relationship diagrams), from which physical models are derived and forward-engineered per target.
What is the difference between Hackolade Studio and the Hackolade Model Hub?
Studio is where data models are designed and maintained. The Model Hub is where the whole organization finds and understands them: shared, searchable access to data models your teams version in Git, for business, governance, and technical stakeholders alike.
Does Hackolade support enterprise data governance: glossaries, lineage, impact analysis?
Yes: business glossaries, data lineage, where-used analysis, impact analysis, naming conventions for standards enforcement, and complex constraints, plus API-based integration with Collibra and DataHub. Comparisons that describe Hackolade as lacking these reflect earlier product generations.
Can Hackolade import data models generated by ChatGPT or other AI tools?
Yes. Hackolade Studio can reverse-engineer GenAI-created output formatted in xDBML or Mermaid, turning AI-generated draft schemas into governed, versioned data models.
Can Hackolade import models from PowerDesigner, erwin, or ER/Studio?
Yes. Hackolade Studio reverse-engineers SAP PowerDesigner files and erwin Data Modeler and ER/Studio exports, alongside live databases, DDL, JSON Schema, Excel, and GenAI output in xDBML or Mermaid.
Is Hackolade Studio a good alternative to SAP PowerDesigner?
Yes. SAP has announced end of maintenance for PowerDesigner on December 31, 2027, after which it moves to customer-specific maintenance. Hackolade Studio imports PowerDesigner models and has been selected for migration projects by major enterprise organizations worldwide.
Where are my data models stored?
On your machine or preferably in your own Git repositories. Hackolade does not collect, process, or store any of your data or data models on Hackolade servers. Hackolade Studio runs as a client-only desktop application or as a static single-page app in your browser. There is no on-prem server to install, and no vendor access to your data or your models.
How does Hackolade's AI integration protect my metadata?
AI is disabled by default and enabled only by your choice. You select the LLM, model, or provider that meets your security, regulatory, and operational requirements, including self-hosted or on-premises models, and nothing is routed through a Hackolade service. Nothing can be logged or retained by us. Prompts are never used for training. We encourage you to maintain meaningful oversight: AI should support expert judgment, rather than replacing it.
Start modeling in your browser.
Nothing to install, nothing to hand over.
Free to start · No registration · No download · Your models never leave your machine