Today’s enterprise databases are more complex than ever. It’s not just rows and columns anymore; your SQL Server, Snowflake, Synapse, and PostgreSQL environments may be storing massive amounts of business-critical data inside semi-structured JSON columns. Until now, that data has been invisible to most modeling tools. ER/Studio 20.9 changes that.
With full support for JSON complex columns, ER/Studio 20.9 gives you the power to reverse engineer embedded JSON structures, design hybrid logical models, and generate accurate schemas for developers, all from the same platform.
Before 20.9, even enterprise-grade modeling platforms could only document the surface: tables, columns, and datatypes. But when critical content lives inside a VARIANT or NVARCHAR(MAX) field as raw JSON, that visibility stops.
ER/Studio 20.9 now inspects the data inside supported column types, parses the JSON content, and creates detailed schemas within both the physical and logical models. That means you can finally see and model the nested structures stored within those columns.
You can now define embedded JSON structures in your logical models and tag relationships as “containing,” which instructs ER/Studio to treat certain entities as nested inside others during physical generation.
Once designed, ER/Studio can:
This lets you deliver a complete, governed data specification, whether you’re building for Databricks, MongoDB, BigQuery, or hybrid relational-JSON environments.
This functionality is now supported for:
Use Case #1: What’s Hiding in Your Snowflake Columns?
A global eCommerce company had created a Snowflake table using a developer-requested VARIANT column. No one knew what data it actually held, let alone whether it was compliant. With ER/Studio 20.9, the team reverse-engineered the structure of the embedded JSON, mapped it to business glossary terms, and surfaced previously hidden PII for review.
Use Case #2: Designing a Hybrid Model with JSON in BigQuery
A data architect working on a new application in BigQuery needed to model some entities as structured tables and others as nested JSON blocks. With ER/Studio 20.9, they started with a logical model, used “containment” relationships to designate embedded objects, and generated both SQL and JSON schema output to hand off to developers. The result: faster development, complete governance, and no guesswork.
IPv6 Repository Support
U.S. federal agencies are transitioning to IPv6-only environments. ER/Studio 20.9 fully supports IPv6 communication between Data Architect and the Repository.
Whether you’re reverse-engineering undocumented JSON structures in production or designing hybrid schemas from scratch, ER/Studio 20.9 brings clarity, governance, and structure to every layer of your architecture. No other enterprise modeling tool offers this level of embedded JSON visibility and control.
Watch our recent webinar on how 20.9 helps organizations take control of JSON.
Ready to see the new features of ER/Studio? Request a demo today to see 20.9 in action.
Yes. ER/Studio 20.9 can reverse engineer JSON structures stored inside complex columns like Snowflake VARIANT or SQL Server JSON data types, giving you full visibility into nested data that other tools leave hidden.
The release supports embedded JSON modeling for Microsoft SQL Server 2019 and 2022, Azure SQL Database, Azure Synapse Analytics, Oracle 19c and 23ai, Snowflake, and PostgreSQL.
Absolutely. ER/Studio 20.9 can generate JSON Schema and JSON Sample files directly from your logical models, alongside SQL code for relational structures. This makes it easier for developers to implement designs consistently.
In ER/Studio 20.9, you can define containment relationships in your logical models. This allows JSON objects to be treated as embedded structures, helping you accurately design and document hybrid relational–JSON environments.
Many organizations store sensitive information inside semi-structured JSON columns. With ER/Studio 20.9, you can uncover these structures, link them to business glossary terms, and ensure compliance by documenting data that was previously invisible.
