Modernizing Telecom Legacy Applications with MongoDB

The telecommunications industry is currently undergoing a profound transformation, fueled by innovations in 5G networks, the growth of Internet of Things applications, and the rapid rise of AI. To capitalize on these technologies, companies must effectively handle increasing volumes of unstructured data, which now represents up to 90% of all information, while also developing modern applications that are flexible, high-performance, and scalable. However, the telecommunications industry's traditional reliance on relational databases such as PostgreSQL presents a challenge to modernization. Their rigid structures limit adaptability and can lead to decreased performance as table complexity grows.

With this in mind, this blog post explores how telecom companies can modernize their legacy applications by leveraging MongoDB’s modern database and its document model. With MongoDB, telecom companies can take advantage of the latest industry innovations while freeing their developers from the burdens of maintaining legacy systems.

Navigating legacy system challenges

Legacy modernization refers to the process of updating a company’s IT infrastructure to align it with the latest technologies and workflows, and ultimately advancing and securing strategic business goals. For telecom companies, this modernization involves overcoming the limitations of their legacy systems, which hinder adjustment to changing market conditions that demand greater system scalability and availability to run real-time operations.

The main drawbacks of legacy technologies like relational databases stem from their design, which wasn’t built to support the data processing capabilities required for modern telecom services. These limitations, as illustrated in Figure 1 below, include rigid data schemas, difficulty handling complex data formats, limited scaling ability, and higher operational costs for maintenance.

Figure 1. The limitations of legacy systems.
The limitations of legacy systems includes 4 categories. The first is hard to change, which features the description Rigid data schemas that hinder the ability to adapt to evolving business needs. The second category is untapped data, which is described as Mobile, IoT, and AI applications produce data types difficult to handle with relational databases. The third category is fragility & downtime, which is described as legacy systems not built for the scale and uptime demanded by today's 24/7 data streams. The final category is high costs, expensive hardware, increasing costs as workloads grow, and costly licensing fees.

Expanding on these limitations, relational databases depend on a predefined schema, which becomes difficult to modify once established, as changes entail extensive restructuring efforts. In telecommunications, handling growing data volumes from connected devices and 5G networks can rapidly become burdensome and costly due to frequent CPU, storage, and RAM upgrades. Over time, technology lock-in can further escalate costs by hindering the transition to alternative solutions. Altogether, these factors hold back modernization efforts urging telecoms to transform their legacy systems to newer technologies.

To overcome these challenges, telecom companies are replacing these legacy systems with modern applications that effectively provide them with greater scalability, enhanced security, and high availability, as shown in Figure 2. However, achieving this transition can be a daunting task for some organizations due to the complexity of current systems, a lack of internal technical expertise, and the hurdles of avoiding downtime. Therefore, before transforming their outdated systems, telecom companies must carefully select the appropriate technologies and formulate a modernization strategy to facilitate this transition.

Figure 2. Characteristics of modern applications.
This diagram showcases 6 characteristics of modern applications, which are advanced use cases, massive scale, always on, available everywhere, secure, and responsive.

Getting onboard with MongoDB

Enter MongoDB. The company’s document-oriented database offers a flexible data model that processes any information format, easily adapting to specific application requirements. MongoDB Atlas—MongoDB’s unified, modern database—delivers a robust cloud environment that efficiently manages growing data volumes through its distributed architecture, ensuring seamless connectivity and enhanced performance.

Moreover, as telecom providers prioritize cybersecurity and innovation, MongoDB includes robust security measures—comprising encryption, authentication, authorization, and auditing—to effectively protect sensitive information and ensure regulatory compliance. Additionally, leveraging MongoDB’s document model with built-in Atlas services like Vector Search, Atlas Charts, and Stream Processing allows telecommunications organizations to streamline advanced industry cases, including single customer view, AI integrations, and real-time analytics.

Figure 3. Core MongoDB modernization features for Modernization.
This diagram showcases 4 core MongoDB modernization features. The first is the document model, which is described as the flexible data model that accommodates to unstructured data and adapts to evolving schemas. The second is availability, which is described as built-in replica sets with self-recovery properties that ensure the resilience of your modernized apps. The third is scalability, which is described as horizontal scalability through sharding to handle the ingest of large data volumes. Finally, the fourth feature is vector search, which is described as operational database and vector embeddings into a single platform, enabling advanced AI solutions.

Recognizing these benefits, leading telecom companies like Nokia, Swisscom, and Vodafone have successfully modernized their applications with MongoDB. However, selecting the right technology is only part of the modernization process. In order to ensure a successful and effective modernization project, organizations should establish a comprehensive mod

  1. Data-driven modernization: this approach transfers all data from the legacy system to the new environment and then migrates applications.

  2. Application-driven modernization (all-or-nothing): this approach executes all reads and writes for new applications in the new data environment from the start, but leaves the business to decide when to retire existing legacy applications.

  3. Iterative modernization (one-step-at-a-time): this approach blends the previous paths, starting with the modernization of the least complex applications and incrementally moving forward into more complex applications.

Read this customer story to learn more about telecoms migrating to MongoDBL

With this overview complete, let's dive into the migration process by examining the iterative modernization of a telecom billing system.

Modernizing a telecom billing system

Telecom billing systems often consist of siloed application stacks segmented by product lines like mobile, cable, and streaming services. This segmentation leads to inefficiencies and overly complex architectures, highlighting the need to simplify these structures. With this in mind, imagine a telecom company that has decided to modernize its entire billing system to boost performance and reduce complexity.

In the initial stage, telecom developers can assess the scope of the modernization project, scoring individual applications based on technical sustainability and organizational priorities. Applications with high scores undergo further analysis to estimate the re-platforming effort required. Later on, a cross-functional team selects the first component to migrate to MongoDB, initiating the billing system modernization. This journey then follows the steps outlined in Figure 4:

Figure 4. The modernization process.
The modernization process begins with an analysis of legacy system, then goes to create end-to-end tests, then to design architecture, then rewrite & add code, then user testing & fixing, and ends with migrate data & deploy.

  1. First, developers analyze legacy systems by examining the codebase and the underlying architecture of the chosen billing system.

  2. Then, developers create end-to-end tests to ensure the application functions correctly when deployed.

  3. Later, developers design an architecture that incorporates managerial expectations of the desired application.

  4. Next, developers rewrite and recode the legacy application to align with the document model and develop APIs for MongoDB interaction.

  5. Following this, developers conduct user tests to identify and resolve any existing application bugs.

  6. Finally, developers migrate and deploy the modernized application in MongoDB, ensuring full functionality.

Throughout this process, developers can leverage MongoDB Relational Migrator to streamline the transition. Relational Migrator helps developers with data mapping and modeling, SQL object conversion, application code generation, and data migration—corresponding to steps three, four, and five. Additionally, telecom companies can accelerate modernization initiatives by leveraging MongoDB Professional Services for dedicated, tailored end-to-end migration support. Our experts work closely with you to provide customized assistance, from targeted technical support and development resources to strategic guidance throughout the entire project.

Building on this initial project, telecom companies can progressively address more complex applications, refining their approach to support a long-term modernization strategy.

Next steps

By revamping legacy applications with MongoDB, telecom companies can improve their operations and gain a competitive edge with advanced technology. This shift allows telcos to apply the latest innovations and free developers from the burdens of maintaining legacy systems. Start your journey to migrate core telecom applications to MongoDB Atlas, by visiting our telecommunications solutions page to learn more.

If you would like to discover how to upgrade your TELCO legacy systems with MongoDB, discover how to start with the following resources:

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Building Gen AI with MongoDB & AI Partners | February 2025

February was big for MongoDB—and, more importantly, for anyone looking to build AI applications that deliver highly accurate, relevant information (in other words, for everyone building AI apps). MongoDB announced the acquisition of Voyage AI , a pioneer in state-of-the-art embedding and reranking models that power next-generation AI applications. Because generative AI is by nature probabilistic, models can “hallucinate”, and generate false or misleading information. This can lead to serious risks, especially in cases or industries (e.g., financial services) where accurate information is paramount. To address this, organizations building AI apps need high-quality retrieval; they need to trust that the most relevant information is extracted from their data with precision. Voyage AI’s advanced embedding and reranking models enable applications to extract meaning from highly specialized and domain-specific text and unstructured data. With roots at Stanford and MIT, Voyage AI’s world-class team is trusted by AI innovators like Anthropic, LangChain, Harvey, and Replit. Integrating Voyage AI’s technology with MongoDB will enable organizations to easily build trustworthy, AI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational data. For more, check out MongoDB CEO Dev Ittycheria’s blog post about Voyage AI , and what this means for developers and businesses (in short, delivering high-quality results at scale). Onward! P.S. If you’re in Vegas for HumanX this week, stop by booth 412 to say hi to MongoDB! Welcoming new AI and tech partners The Voyage AI news was hardly the only exciting development last month. In February 2025, MongoDB welcomed three new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! CopilotKit Seattle-based CopilotKit provides open source infrastructure for in-app AI copilots. CopilotKit helps organizations build production-ready copilots and agents effortlessly. “We’re excited to be partnering with MongoDB to help companies build best-in-class copilots that leverage RAG & take action based on internal data,” said Uli Barkai, Co-Founder and Chief Marketing Officer at CopilotKit. “MongoDB made it dead simple to build a scalable vector database with operational data. This collaboration enables developers to easily ship production-grade RAG applications.” Varonis Varonis is the leader in data security, protecting data wherever it lives—across SaaS, IaaS, and hybrid cloud environments. Varonis’ cloud-native Data Security Platform continuously discovers and classifies critical data, removes exposures, and detects advanced threats with AI-powered automation. “Varonis’s mission is to protect data wherever it lives,” said David Bass, Executive Vice President of Engineering and Chief Technology Officer at Varonis. “We are thrilled to further advance our mission by offering AI-powered data security and compliance for MongoDB, the database of choice for high-performance application and AI development. With this integration, joint customers can automatically discover and classify sensitive data, detect abnormal activities, secure AI data pipelines, and prevent data leaks.” Xlrt Xlrt is an automated insight-generation platform that enables financial institutions to create innovative financial credit products at scale by simplifying the financial spreading process. “We are excited to partner with MongoDB Atlas to transform AI-driven financial workflows,” said Rupesh Chaudhuri, Chief Operating Officer and Co-Founder of Xlrt. “XLRT.ai leverages agentic AI, combining graph-based contextualization, vector search, and LLMs to redefine data-driven decision-making. With MongoDB's robust NoSQL and vector search capabilities, we’re delivering unparalleled efficiency, accuracy, and scalability in automating financial processes.” To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem. And visit the MongoDB AI Applications Program (MAAP) page to learn how MongoDB and the MAAP ecosystem helps organizations build applications with advanced AI capabilities.

March 12, 2025
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IFG Life Modernizes and Unlocks the Power of Data With MongoDB

Financial services customers across the Asia-Pacific (APAC) region speak often about the need to modernize their infrastructures to unlock the full potential of data. Frequently, companies are encumbered by legacy data architectures that create inefficiencies and complexities for their developer teams. IFG Life overcame these challenges by migrating to MongoDB Atlas . Garry Alfanzo, Head of IT and Architecture for IFG Life, shared insights during a presentation at MongoDB.local Jakarta 2024 . Modernizing by migrating to the cloud A subsidiary of Indonesia Finance Group (IFG), IFG Life specializes in life and health insurance. The company was established in 2020 with an initial purpose of migrating policies from former state-owned insurance company Jiwasraya and bringing them under the IFG umbrella. IFG Life had to overhaul an outdated infrastructure, which included multiple legacy systems and coding frameworks. To kick-start its modernization journey and enhance agility, IFG Life migrated to the cloud and adopted a microservices architecture. This led to the creation of the Customer 360 model, a platform designed to manage IFG Life’s four main user applications: customer, corporate, sales force, and hospital network. “The purpose of developing this division [was] to create a single integrated platform. In this digital era, we realized that just having an outstanding app is not the only thing that matters. Data is also crucial,’ said Alfanzo. The need for a robust database solution to support Customer 360 operations became evident. Getting a false start with a graph database “In 2022 the hype around graph databases was very high, leading up to a surge in various graph database solutions,” said Alfanzo. IFG Life began pursuing this option. However, while implementing a graph database, the company experienced resource and knowledge challenges. “In 2022, the hype around graph databases was very high, leading to a surge in various graph database solutions. However, this excitement was not matched by sufficient resources in Indonesia,” said Alfanzo. This prompted IFG Life to shift its focus and seek a database that would meet its engineering team’s needs. Finding the optimal solution with MongoDB Atlas IFG Life needed a database with integrated graph capabilities—one that was widely adopted and supported by a large community in Indonesia. Additionally, the company needed to ensure access to a robust pool of local expertise. “The solution we aimed to implement revolves around data integration, allowing us to unify our various cores and streamline our storage solutions,” said Alfanzo. “Data mining is essential to extract the collected data, and we require data science to perform analytics, including data streaming through CDC [change data capture] and batch processing, alongside visualization.” Alfanzo’s team had already determined that it needed to move away from a relational approach, which was deemed ill equipped to meet IFG Life’s requirements. The team evaluated several NoSQL vendors, and MongoDB Atlas ticked all the boxes, and more. “When we evaluated MongoDB, we identified features that were essential for us,” said Alfanzo. Among these were $graphLookup , MongoDB Atlas Triggers , and MongoDB Atlas Device SDKs . “There’s also MongoDB Aggregation Pipeline , which is crucial for crunching data, creating views, and performing queries,” Alfanzo added. Alfanzo also mentioned how JSON , the text-based data format MongoDB uses, simplified IFG Life’s process of storing, retrieving, and viewing data. However, what Alfanzo highlighted the most was the unique expertise, knowledge, and support that the MongoDB team in Indonesia provided: “One of the critical factors during our evaluation was the ecosystem and community support,” said Alfanzo. “Having access to people with technology skills is essential, as it has been our biggest problem. That’s why we chose MongoDB. What I truly appreciate is that [it is] not just focused on selling but also [focused on] providing solutions for the future.” Integrating MongoDB into IFG Life’s ecosystem was a strategic decision that paid off. It facilitated the creation of a customer-to-system model, crucial for the Customer 360 initiative. This advancement enables IFG Life to better understand and serve its customers and has paved the way for more personalized and effective service offerings. Unlocking generative AI capabilities with MongoDB Atlas Vector Search Looking ahead, IFG Life is eyeing new frontiers with MongoDB, particularly as gen AI is fast spreading and being adopted by Indonesian enterprises. “We’ve already used MongoDB for our Customer 360, which is currently ongoing,” said Alfanzo. “To stay aligned with this trend, we recognize that MongoDB offers a feature that enhances query similarity— MongoDB Atlas Vector Search —and we plan to incorporate this into our future implementations.” Moreover, IFG Life intends to implement retrieval-augmented generation (RAG) to handle personally identifiable information (PII) securely. “We understand that PII should not be sent to large language models (LLMs). Incorporating sensitive data can expose the system to vulnerabilities,” explained Alfanzo. “In this context, RAG is a more suitable solution as, when querying RAG, we can add an additional layer to provide context, and this context enhances [the] query—we can ensure that we have real-time context that aligns with the standards of insurance or banking.” IFG Life is a great example of how MongoDB Atlas’s capabilities can help companies modernize their complex legacy systems in the data-dependent, highly regulated banking and insurance industry. This story also shows how the MongoDB Atlas platform can prepare organizations that want to reap the benefits of gen AI. To learn more about MongoDB Atlas , visit our product page. Get started with Atlas Vector Search today by checking out our quickstart guide .

March 18, 2025