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ER/Studio and Databricks are now Natively Integrated

Experience more efficient data management, improved accuracy in data modeling, and streamlined workflows for managing complex data environments.
Databricks and ER/Studio integration

Unlocking The Power Of Native Integration

This integration empowers data professionals to exploit their data assets fully, propelling business success and fostering innovation.

Discover the full potential of ER/Studio and Databricks native integration

ER/Studio Core Platform Support Features

Reverse Engineer from Unity Data Catalog

Comprehensive Schema Understanding

Reverse engineering from Unity Data Catalog allows users to extract and document existing data schemas and structures within Databricks.

Accurate Documentation

By extracting schemas directly from Unity Data Catalog, ER/Studio ensures that all data structures are accurately documented enhancements.

Simplified Migration and Integration

Understanding the existing schema is crucial for any data migration or integration project.
Unity Data Catalog
The DDL for a Databricks table in ER/Studio

DDL Code Generation

Efficient Database Creation

DDL (Data Definition Language) code generation automates the creation of database structures within Databricks. 

Consistency and Accuracy

 Automatically generating DDL code ensures that the database structures are consistent with the data models defined in ER/Studio. 

Rapid Prototyping and Development

DDL code generation facilitates rapid prototyping and development cycles. 

DDL Code Import

Seamless Integration with Existing Systems

DDL code import allows users to bring existing database definitions into ER/Studio. 

Enhanced Collaboration

By importing existing DDL code, teams can ensure that all database structures are documented and managed within a single, unified environment. 

Facilitates Auditing and Compliance

Importing DDL code into ER/Studio helps in maintaining comprehensive records of database structures. 
Code generated in ER/Studio which then generates this Physical Model (with Nested Object)
Compare and merge to generate ALTER scripts

ALTER Script Generation

Streamlined Database Modifications

ALTER script generation automates the creation of scripts needed to modify existing database structures.

Minimized Downtime

By generating accurate ALTER scripts, organizations can apply changes to their databases with minimal downtime.

Risk Mitigation

Automated ALTER script generation reduces the risk of errors that can occur during manual script writing. 

Version Control and Change Management

ALTER scripts provide a clear record of changes made to the database structures. 

Powerful Integration with Delta Lake Tables

By offering deep support for Databricks Unity Data Catalog and its latest features, ER/Studio ensures that users can effectively work with Delta Lake tables across all cloud platforms.
Nested Object Support

Denormalized Nested Structures

  • Nested Objects Performance: Standard RDBMS platforms support only fully normalized data. We know that performance is affected when using joins across tables. Databricks supports nested objects to increase the performance of read operations. ER/Studio has unique and powerful features that allow you to design, manage, and document nested objects.
Reverse Engineering from Unity Data Catalog

Schema Documentation and Analysis

  • Comprehensive Understanding: Extracting schemas directly from Unity Data Catalog allows data architects to understand existing data structures fully.
  • Accurate Documentation: Reverse engineering ensures that all data structures are accurately documented, providing a reliable reference for managing and optimizing data environments.
Primary Key, Foreign Key, and Check Constraints

Ensuring Data Integrity and Consistency

  • Data Integrity: Enforcing primary and foreign key constraints ensures that relationships between tables are maintained, preventing data anomalies and ensuring data accuracy.
  • Validation: Check constraints help validate data, ensuring it meets specific business rules and standards.
Materialized Views, Clustering, and Partitioning

Optimizing Query Performance

  • Faster Query Performance: Materialized views store pre-computed results, reducing query execution time and improving performance, especially for complex queries.
  • Efficient Data Management: Clustering and partitioning strategies help organize data for efficient querying, enabling faster access to relevant data and improving overall performance.
Bloom Filter Indexes

Enhancing Data Retrieval Efficiency

  • Efficient Searches: Bloom filter indexes significantly speed up data retrieval processes by reducing the amount of data scanned during searches. This efficiency is crucial for handling large datasets and ensuring quick access to required information.
Functions and Mask Functions in Tables

Data Security and Transformation

  • Data Security: Mask functions enhance data security by masking sensitive information, ensuring only authorized users can access or view it.
  • Data Transformation: Built-in functions enable efficient data transformations within tables, simplifying data processing tasks and ensuring consistency.

Flexible Data Presentation

  • Simplified Data Access: Views provide a simplified way to access data, presenting it in a user-friendly format. This flexibility is essential for users interacting with data without modifying the underlying structures.
  • Customizable Data Representation: Views allow for the customization of data presentation, enabling users to create specific views tailored to their needs.
Denormalization with Nested Structures

Advanced Data Modeling

  • Complex Data Structures: Handling denormalized data with nested structures is crucial for representing complex data relationships and hierarchies. This capability ensures that data models accurately reflect real-world scenarios.
  • Effective Documentation: Advanced denormalization support enables the effective generation and documentation of nested structures, ensuring that all data model aspects are thoroughly captured and understood.
Platform-Independent Logical Models

Standardized Data Management

  • Consistency Across Platforms: Using platform-independent logical models ensures consistency in data management practices across different environments. This standardization simplifies data integration and reduces the risk of discrepancies.
  • Seamless Translation: Translating logical models to physical models with support for denormalization patterns ensures that complex data structures are accurately represented and documented.
ER/Studio icon plus Databricks icon


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