Accelerate Data Modernization with Infosys Data Model Converter

Are you in the process of migrating applications from a relational database to MongoDB? If so, you’re likely trying to best understand and decide how your enterprise data needs to be modeled.

Our previous blog discussed how Infosys Data Services Suite can help enterprises move data seamlessly from legacy relational databases to MongoDB. But moving data is only one part of the puzzle. The more significant step is choosing the target data model, or schema design, a process that usually requires several hours of highly skilled talent. That’s why we created this follow-up blog to help you get started.

Rethinking Schema Design

Ultimately, schema design can be the difference between an inefficient, disorganized database and a strategic one that empowers the entire company. Schema design in MongoDB requires a change in perspective for data architects, developers, and database administrators. They have to:

• Rethink the legacy relational data model. This model flattens data into rigid two-dimensional tabular structures of rows and columns. The new data model is a rich and dynamic one with embedded sub-documents and arrays
• Rethink how the data platform works. In relational databases, it is extremely difficult to change the data platform as the application evolves. However, in MongoDB, the apps and APIs come first and the data platform dynamically accommodates the data

Getting Schema Design Right

Begin the schema design process by considering the application’s requirements. You’ll want to model the data in a way that leverages the flexibility of the document model. In schema migrations, it may seem easy at first to simply mirror the flat schema of the relational database in the document model. However, this negates the advantages enabled by the rich and embedded data structures of the document model. For example, data that belongs to a parent-child relationship in two RDBMS tables can be collapsed (embedded) into a single document in MongoDB.

The application data access patterns should also drive schema design with a specific focus on:

• The read/write ratio of database operations and whether it is more important to optimize the performance of one operation over another
• The types of queries and updates performed by the databases
• The lifecycle of the data and growth rate of documents

Simplifying Schema Design with Infosys Data Model Converter

Infosys has developed a solution called Infosys Data Model Convertor that processes source relational schema and the above-mentioned signals as inputs and automatically provides target MongoDB schema suggestions. Infosys Data Model Converter is available as part of Infosys Modernization Suite which accelerates enterprises’ modernization journey.

Each schema suggestion is accompanied by a detailed analysis report. The data modeler can use this as a starting point and iterate over the schema to arrive at the final MongoDB schema.

Key Features

• Boosts productivity by augmenting the migration of RDBMS to NoSQL database
• Saves time by automatically extracting schema, query and data patterns from an existing RDBMS
• Comprehensively analyzes the RDBMS entity relations, data and read-and-write patterns
• Applies a rich set of rules and generates a fully compliant NoSQL target state data model
• Offers flexibility by externalizing the rules for organization-specific customizations
• Connects and deploys the model to the target NoSQL platform with sample data