The 5-Step Guide to Mainframe Modernization for Banks

Joerg Schmuecker
April 21, 2022 | Updated: June 6, 2022

Enriched, convenient, and personalized are the watchwords for any business building a modern, digital customer experience. It’s no different for traditional retail banks, especially as they try to fend off challenger banks and design their own online banking and in-branch experiences to win new business and retain existing customers.

But in order to beat the competition and build experiences that best those offered by neobanks, established retail banks need to master their data estate. Specifically, they need to free themselves from the rigid data architectures associated with legacy mainframes and monolithic enterprise banking applications.

Only then can established banks have their developers get to work building high-quality customer-facing applications rather than managing thousands of SQL tables, scrambling to rework schema, or maintaining creaky legacy systems.

The first step on this journey is modernizing the mainframe.

Enriched modernization in 5 phases

The best way to modernize is through a phased model that uses an operational data layer (ODL).

An ODL acts as a bridge between a bank’s existing systems and its new ones. Using an ODL allows for an iterative approach, allowing banks to see progress toward modernization at each step along the way while still protecting existing assets and business-critical operations.

Banks can see rapid improvements in a relatively short amount of time while preserving the legacy components for as long as they’re needed to keep the business running. MongoDB’s five-phase approach to modernization enables banks to modernize iteratively while balancing performance and risk.

If banks are eager to modernize and their customers are demanding modern banking experiences, what’s taking banks so long to move away from the legacy systems that are restricting their ability to innovate? And why do so many legacy modernization efforts fall short? Download The 5 Phases of Banking Modernization to start plotting your path forward.

Mainframe modernization techniques

With an ODL, the legacy infrastructure can be switched off piece by piece and retired as more functionality is added. In this scenario, database operations become much more efficient because objects get stored together rather than in disjointed locations. Reads are executed in parallel via the nodes in a replica set. Writes are largely unaffected.

To bring similar benefits to writes, banks may choose to implement an ODL with sharding and regional shards, bringing writes closer to the actual user. Workloads can then be gradually moved from legacy systems to the ODL, with the ultimate goal to decommission the legacy system.

The beauty of this approach to modernization is that it starts with the use case: What problems does the bank face in its data management and what functionalities are customers requesting?

If the first priority is giving customers access to historical transaction data, then banks can tackle that problem immediately by building a repository (or domain) to offload customer data from the mainframe. If the priority is cost reduction, then an ODL can act as an interim layer, allowing applications to access the data they need without the need to run expensive queries against mainframe data.

The advantages of an ODL

MongoDB is ideal for connecting legacy mainframes and databases to newer architectures, such as a data mesh, by way of an ODL. An ODL has a number of advantages. Combined, these advantages make data massively easier to access and use — and applications easier and faster to build.

An ODL allows an organization to process and augment data that resides in separate silos, and then use that data to power a downstream product, such as a website or an ATM. With an ODL, data is physically copied to a new location. A bank’s legacy systems remain in place, but new applications can access data through the ODL rather than interacting directly with legacy systems.
An ODL can draw data from one or many source systems and power one or many consuming applications, unifying data from multiple systems into a single real-time platform.
An ODL relieves the mainframe of workloads. One useful by-product is in avoiding consumer service interruptions brought about by maintenance windows on legacy systems, like Oracle Exadata.
An ODL can be used to serve only reads, accept writes that are then written back to source systems, or evolve into a system of record that eventually replaces legacy systems and simplifies the enterprise architecture. Because of its ability to work with legacy systems, or to gradually replace them, and its ability to support an evolutionary approach to legacy modernization, many banks find that an ODL is a critical step on the path to full modernization of their enterprise architecture.

In terms of architectural setup, some banks may want one ODL for each of their data domains but others may find certain domains can share an ODL. The ODS/ODL template can be applied in a variety of ways — without breaking the bank’s internal standards.

For example, imagine an ATM terminal connected to a MongoDB-based ODL. With the ODL in place, data from the mainframe is replicated in real time and made available for the consumer to check their most recent transactions and account balance on the ATM. Customer balance information, however, also still resides on the source system.

Using the ODL to replicate and display information from the mainframe avoids customers having to face annoying delays while they wait for the information from a mainframe to load. At the same time, risk management and regulatory reports can still be run against a mainframe as a batch “end of day” process.

With an ODL in place, data can flow from the mainframe to a newer architecture, giving the ATM broader capabilities that expand customers’ banking experiences, such as the ability to pay invoices, change addresses, or even open additional accounts.

Nightly batch, bulk load, or real-time updates: MongoDB is flexible enough to connect to any data source, be it classic DB2 for zOS, Oracle, SQL Server, Hadoop-based legacy, or even Excel spreadsheets. MongoDB has the appropriate connectivity to ingest any data at any time from anywhere.
Enrichment, data domains, and data marketplaces: With its document data model, MongoDB has the capability to bring data into data domains versus using convoluted table schema and ETL processes. The domains emerge naturally based on the application and user community requirements.
Security, schemas, and validation: MongoDB has multiple layers of security, including password protection over encryption in flight and at rest, plus granular field-level encryption. All with external key management.

*MongoDB can be used as an operational data layer*

Take the next step in mainframe modernization

Because many core banking capabilities are transactional and can be handled with daily batch processing, mainframes remain the backbone of our financial system. Mainframe modernization might sound daunting, but it doesn’t have to be. Banks can choose to proceed along a straightforward and predictable path that allows them to modernize iteratively. They can receive the benefits of modernization in one area of the organization even if other groups are earlier in their modernization path.

It’s possible to do this while supporting increasingly complex data privacy regulations and, importantly, minimizing risk.

Banks and other financial institutions that have successfully modernized have seen cost reductions, faster performance, simpler compliance practices, and rapid development cycles. New, flexible architectures have accelerated the creation of value-added services for consumers and corporate clients.

If you’re ready to learn more about how you can accelerate your digital transformation and minimize risk, “The 5 Phases of Banking Modernization” now.

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Finding The Right Career Move Thanks to Flexa and MongoDB

Anyone who has ever searched for a job knows just how frustrating the process can be. Whether you’re switching companies in the same industry, searching for something completely different, or a little bit of both, it’s difficult to find the right fit. Thankfully, Flexa was built to make that process smoother for employees and companies alike. Flexa was designed with one simple mission - bring flexible working to everyone. The company says it isn’t as simple as asking every company to tick a box; Flexa understands that flexibility means something different to everyone. Flexa is here to provide some clarity to help everyone find work that works for them. MongoDB for Startups spoke with Flexa’s CEO and co-founder Molly Johnson-Jones, co-founder Maurice O'Brien, and CTO & co-founder, Tim Leppard, to discuss their company, their investment partner QVentures , and the MongoDB for Startups program. MongoDB for Startups: What exactly is Flexa? Johnson-Jones: Flexa is an employer brand and talent acquisition platform. What we do is help companies get discovered for their brilliant working environments. It could be flexible working, dog friendly offices, enhanced parental leave. That's all searchable and filterable by our users and it means that we're bringing transparency to the global job hunting market. You'll know exactly what it's like to start at a company before you've even applied there, not at the end of an interview process. Flexa's mission is to bring true transparency to the global job hunting process, meaning that you could be in New York looking for a job in London and you would know that you could bring your dog and work from home whenever you wanted and travel around the world at your next company. MongoDB for Startups: How has Flexa changed or pivoted since its inception? Johnson-Jones: The initial vision for Flexa was actually pretty different to what we do today. When we first launched in February 2020, we were more of what you'd call a pure job platform. We found companies, we verified them as flexible through our two stage benchmarking process. But then we focused much more on specific individual jobs and whether they were flexible. You'd come on a site like Indeed or LinkedIn and you'd apply and you'd apply through Flexa. But we started to realize that actually the problem was much bigger than just individual jobs. It should be tackled at a company level. Providing transparency on a company level all over the world and looking more at culture and purpose and working environment was the right way to go. People didn't need to apply to more jobs in more places, they needed a reference to something that would enable them to actually find transparency and to know exactly what it was like to work at a company, not just a specific job for reactive hiring. MongoDB for Startups: Can you speak about the partnership Flexa has with QVentures? O’Brien: Our partnership with QVentures has been very important for us in terms of our scaling. QVentures actually led the very first funding round that we did. It's been huge for us in our growth. It gave us the capacity to hire more people, to market our products, and to build brand awareness and reach out to a much wider user base than we would have had the chance to otherwise. MongoDB for Startups: How has QVentures helped in Flexa’s growth? O’Brien: QVentures has helped us in a number of ways, aside from just obviously financially. They provide a lot of advice for us, whether that's strategic or hiring. They've also been great for introductions on the B2B side, so some of our Flexified clients have come through QVentures. They're always willing to help out with spreading the word around general brand awareness pieces that we do, like webinars, which is really helpful for us as a growing business. MongoDB for Startups: How has MongoDB enabled you to not only build out but also scale the business? Leppard: At Flexa, we collect data on the flexibility and employee benefits of companies, and the desires of our users, and the requirements they have for their new roles. MongoDB offers the ability for us to rapidly change the data we collect. The world of work is changing all the time, whether it’s the benefits, the flexible working practices that people expect or the change in company offerings, and we need to adapt to those very quickly. MongoDB has allowed us to adapt and scale very rapidly with those things in mind. MongoDB for Startups: What has your experience been with the scalability of MongoDB? Leppard: When it comes to scalability, we decided to use MongoDB Atlas. There's plenty of things to think about when you're founding a company, and infrastructure and operations is one that you want to try and minimize. Atlas gives us the ability to scale as we need to and scaling has been pretty painless as the company has grown. MongoDB for Startups: What features have you leveraged on MongoDB Atlas? Leppard: One of the features we use is MongoDB Atlas's text search features. This has been huge for us. It allows us to offer search for companies and jobs to our users, and to really offer a very accurate text search for people looking for particular requirements and flexibility options. MongoDB for Startups: What are the compliance needs for your data? Leppard: At Flexa, we store users' personal information, and so security is very important to us. Our GDPR requirements are very important to us, and MongoDB Atlas gives us the security that we need, so we don't have to worry about compliance. Additionally, MongoDB alleviates the concern of maintaining compliance as things change overtime. Interested in understanding how your company's working environment measures up against the market? Take Flexa’s free quiz to find out! Interested in learning more abour MongoDB for Startups? Learn more about us here .

April 20, 2022

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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024