TranzIndia distributor platform
RATH is part of the stack behind a platform connecting distributor registration, commerce and operational workflows.
Explore the project ↗RATHORMOur in-house ORM framework
Define once.Build on it.
Give your product a shared data foundation. Reuse the same views across queries and APIs, and spend less development time rebuilding what you already have.
Built for
Java
PostgreSQL
JavaPostgreSQLShared
data viewDefine your fields
and relationships
data viewDefine your fields
and relationships
RATH ORM
Customer portalThe customer experience
Operations dashboardThe team’s daily view
Read APIThe next product feature
From our white paper
40%less query-body codeJoined-query example: 6 RathORM lines vs 10 Hibernate lines.
Explore the comparisonUsed in our delivery
Built into the systems we deliver.
RATH is part of the engineering stacks behind these Tranz platforms.
Pills Pharmacy Platform
RATH is part of the engineering foundation for connected pharmacy workflows across stock, prescriptions and delivery.
Explore the project ↗The challenge
Stop rebuilding the same data views for every feature.
A new screen or API often needs information your application already knows how to retrieve.
Repeating selected fields, relationships and common request handling across endpoints creates more code to write, review and maintain. A change to the information you expose can then require updates in several places.
RATH centralizes those definitions so your team can reuse them. Start with a shared business view, adapt it to each feature and keep your engineering effort focused on what makes that feature useful.
Features and benefits
Less repetition from data access to API delivery.
Reusable foundations for the work your engineers do across multiple features.
Define once. Use across your product.
Choose the fields and relationships a business view needs, then reuse that definition across related queries and API endpoints. Keep the same data view consistent as your product grows.
Write less repeated query code
Add filters, sorting and pagination to an existing view instead of repeating its fields and joins. Shorter query bodies leave less repeated code to review and maintain.
Spend less effort on API plumbing
Reuse common filtering, field allowlisting, pagination and list-response handling. Give your team a shared starting point for read APIs while retaining application-specific access rules.
Bring connected information together
Retrieve supported parent and child records in one database select. Build useful business views without requesting each child collection separately.
Understand database activity
Make transaction ownership clear and observe query duration, returned rows and failures. Give engineers better context when they investigate an issue.
Review changes before release
Inspect planned database changes and identify destructive operations before applying them. Keep database evolution part of your team’s release review.
How reuse works
One business view. Several product experiences.
Define the information once, then adapt how it is requested across related features.
Define the business view
Agree on the information the feature needs and how related records should appear.
Reuse it across queries and APIs
Apply the filters, sorting and pagination each screen or endpoint needs to the shared definition.
Evolve with a shared foundation
Review changes to that definition once, then validate the queries and endpoints that depend on it.
Hibernate and RathORM
Choose the foundation that fits your product.
Hibernate offers broad ORM capabilities and database choice. RathORM focuses on explicit data operations for Java and PostgreSQL applications.
| What matters to your team | Hibernate | RathORM |
|---|---|---|
| Best fit | Applications needing Jakarta Persistence standards, broad database support and an established ORM ecosystem. | Java and PostgreSQL products where a focused data foundation and explicit control suit the team. |
| Everyday development | Object mapping and repository tooling reduce repetitive persistence work. | Reusable read projections centralize fields and relationships across related queries and API endpoints. |
| Control over database operations | Automatically tracks changes in its standard session model; also offers stateless operations for direct control. | Explicit changes and transaction ownership make database operations easier to follow. |
| Connected information | Configurable fetching can retrieve related information in a single query. | Can retrieve nested collections in one PostgreSQL query, avoiding repeated requests for each parent record. |
| Database changes | Provides schema generation and validation tooling; the application team chooses its migration process. | Builds reviewable change plans that flag destructive operations before an explicit application step. |
| Adoption and support | A broad community, JPA compatibility and commercial support options. | An in-house Tranz framework with Spring integration and adoption support from our engineering team. |
less query-body code
4 RathORM lines vs 5 Hibernate HQL lines
less query-body code
5 RathORM lines vs 6 Hibernate HQL lines
less query-body code
6 RathORM lines vs 10 Hibernate HQL lines
Counts from the white paper’s fixed-format examples. Shared setup is separate: the starter suite totals 67 RathORM lines versus 53 Hibernate lines with declarations included. Full-application code savings, developer-time savings and comparative runtime speed have not been measured.
Based on the RathORM and Hibernate white paper and Hibernate ORM overview ↗ / Hibernate 7.4 guide ↗.
The evidence
Explore the thinking behind RATH.
Read how reusable projections change query authoring, what the examples show and how to evaluate the tradeoffs.
White paper · 7 October 2026
RathORM and Hibernate
By Karthikeyan, CEO Tranz Technologies
A detailed comparison of developer effort, reusable data views and cache behavior. Includes the exact query examples, shared setup costs and a clear account of what has been verified.
Where RATH fits
A practical fit for repeated PostgreSQL read workflows.
Consider RATH where related features can share data views and common API behavior.
Portals with shared data views
Reuse selected fields and relationships across customer portals, internal screens and related read APIs.
Connected operational platforms
Bring together business records that teams need to act on, with clear transaction boundaries for updates.
Products with growing API portfolios
Use a consistent foundation for repeated list, search, filtering and export requirements as new features are added.
Work with your existing engineering environment
RATH supports Spring integration, common web filtering and CSV exports. We can assess your existing data layer and plan adoption around the features where reuse offers the most value.
Before we begin
Questions about fit and adoption.
Start with your product, your current systems and the improvements you need.
How does RATH help save development time?
Reusable data views and API handling reduce repeated authoring work across similar features. The benefit is strongest when a team can reuse an established view. The white paper has not measured developer hours, so we do not claim a time-saving percentage.
What does the 40% less-code figure mean?
The joined-query example uses 6 RathORM query-body lines versus 10 Hibernate lines, a 40% reduction. Reusing projections can reduce repeated authoring work across related endpoints. This compares the displayed query bodies, not complete APIs or applications. Shared declarations and setup are counted separately in the white paper.
When is RATH a good fit?
RATH is designed for Java and PostgreSQL applications with recurring read views and related API endpoints. A smaller application with only a few simple reads may gain less from the initial setup.
Can we use it with our existing application?
RATH can integrate with Spring and existing transaction management. We review your current data layer, reusable components and delivery process before proposing a scope for adoption.
Are all RATH queries cached?
No. Mapping metadata reuse and cached query results are different capabilities. Optional result caching is limited to eligible read paths; the filtered and paginated query examples in the paper do not use it.
How do we evaluate it for our project?
Share the data views, API requirements and repeated work in your application. We can identify a representative feature, review the integration needs and assess the value of reuse for your roadmap.
Work with Tranz
Discuss RATH for your project ↗Spend more effort on the next feature.
Show us the data views and API work your team keeps repeating. We can assess where RATH fits and plan a focused first integration.