Legacy Modernization Leveraging MongoDB and Infosys iDSS

Paresh Saraf and Dhiraj Khare
October 1, 2020 | Updated: July 8, 2021
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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

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Customer Service Expert Wati.io Scales Up on MongoDB

Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .

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