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. The Infosys Data Model Converter reduces 50-60% of the effort typically spent in schema design. 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 Discover more ways in which Infosys can help you unlock value from modernization. Contact us for any modernization questions.
Legacy Modernization Leveraging MongoDB and Infosys iDSS
Large enterprises need to refresh their digital strategies to handle the growing demand, emerging competition, and ground dynamics. Infosys and MongoDB started working jointly to help these large organizations reach their full potential by focusing on data modernization. With inputs from MongoDB, Infosys teams have developed a framework called Infosys Data Services Suite ( iDSS ), which allows users to move data from various siloed and legacy relational systems to MongoDB, the most popular document-based modern data platform in the world. By working with iDSS, enterprises can maintain business continuity while seamlessly adapting to the modern demands of analytics and data growth. iDSS is a no-code framework specifically designed and developed to move data from legacy relational systems to MongoDB in an efficient and cost-effective way to ensure predictability, reduced total cost of ownership (TCO), increased productivity, and faster time to market. Because the relational and document model are two opposing database models, you may logically ask How is it possible to map the rows and columns of a relational database to the JSON-like structure of MongoDB? Agreed. Moving data from a relational database management system (RDBMS) to MongoDB is not a trivial task, and that is why experts from Infosys designed iDSS. The tool works like any modern-day extract, transform, and load (ETL) product that can extract data from the RDBMS, transform the data from the relational data model to the document model in a no-code way, and then load the data into MongoDB. Demo iDSS helps you efficiently carry out data modernizations while reducing ETL development effort by as much as 37%. Infosys Data Services Suite (iDSS) Key Features iDSS Features for Migration to MongoDB RDBMS to MONGODB Transform and Load : RDBMS-to-MongoDB migrations with structure changes/transformations/filters RDBMS to JSON Transform and Load: RDBMS-to-JSON file generation with structure changes/ transformations/filters MongoDB to MongoDB: Support for MongoDB-to-MongoDB data migrations, with structure changes/transformations/filters, using MongoDB aggregation framework Other Features Summary Data profiling, business data validation, data quality checks Diversified connectors for enterprise packaged systems such as SAP ECC and S/4HANA, SuccessFactors, Microsoft Dynamics, and Salesforce Enabled for major cloud platforms such as GCP, AWS, and Microsoft Azure Business reviews, approvals/signoff Change data capture (CDC) and incremental data loading Data comparison/ata reconciliation Easy custom development for client requirements Dedicated expert support for tool deployments Learn more about our Modernization Program