Accelerating Sybase-to-MongoDB Modernization With PeerAI

The IT landscape has evolved dramatically over the past decade. Cloud-native architectures, advanced analytics, and AI have reshaped the way businesses use data. But the key requirements for these modern database systems—such as horizontal scalability, real-time insights, and support for AI workloads—are often beyond the capabilities of legacy platforms like Sybase Adaptive Server Enterprise (Sybase ASE). And with SAP announcing the end of life of this platform, organizations relying on it now face a critical decision. Document databases like MongoDB have emerged as transformative alternatives, offering unmatched flexibility and speed.

However, migrating from Sybase to MongoDB is far from a lift-and-shift process—it requires a comprehensive transformation of both the data and application layers. This is where PeerAI, a platform from PeerIslands, can aid organizations in their modernization journeys.

The evolution of Sybase and the need for change

In the 1980s, Sybase emerged as a pioneering relational database, driving innovations in enterprise data management. Its integration into SAP’s HANA ecosystem in 2010 solidified its role as a cornerstone of legacy enterprise systems. However, SAP has announced the end of life for Sybase ASE after 2025.

As many enterprises prepare to migrate, the shift in modern technology has led them to reevaluate their database strategies. And while moving from Sybase to another relational database may seem like the easiest option, such an approach often falls short of delivering the scalability, performance, and adaptability needed to meet modern business demands..

MongoDB Atlas, a fully managed cloud database, stands out as a preferred choice for organizations looking to modernize. With its developer-friendly document model, horizontal scalability, and seamless integration with major cloud providers, MongoDB empowers enterprises to unlock new possibilities.

The complexity of Sybase-to-MongoDB modernization

Migrating from Sybase to MongoDB is a journey that demands thoughtful planning and execution. Legacy systems like Sybase were designed for an era of predictable workloads and monolithic architectures, which struggle to keep pace with today’s real-time, data-intensive demands.

The transition involves more than simply replacing one database with another. It requires a complete rethinking of architectures, workflows, and data models. Key challenges include:

  • Legacy complexity: Decades-old systems often harbor deeply intertwined data and application layers. Extracting and restructuring these requires precision.

  • High costs: Modernization demands up-front investment in resources and tools. Without a clear strategy, costs can quickly escalate.

  • Lengthy timelines: Traditional migrations often take years, requiring businesses to support old and new systems simultaneously.

  • Skills gaps: Expertise in legacy systems is limited, and finding skilled professionals for modern platforms like MongoDB adds to the challenge.

  • Validation difficulties: Ensuring the new environment replicates or improves on the functionality of the legacy system requires extensive testing.

  • Outdated methods: Conventional tools and approaches for relational-to-relational migrations are ill-suited for transitioning to MongoDB’s document-based model.

Despite these challenges, modernization offers immense potential to not only overcome the limitations of legacy systems but also unlock new capabilities.

Simplified Migration to MongoDB with PeerAI

To address these complexities, PeerIslands developed PeerAI, a platform that simplifies and accelerates the migration process. Combining generative AI (gen AI) with the expertise of seasoned developers, PeerAI transforms modernization into a seamless journey.

The process begins with a detailed code-and-database analysis of the Sybase environment. PeerAI uses AI-driven tools to map dependencies, schemas, and business logic, providing a comprehensive understanding of the system. This ensures that no critical functionality is overlooked during migration.

Screenshot of the SQL footprint analysis results on the Peer AI dashboard.
Figure 1: Footprint analysis of database and application artifacts, part 1.

Screenshot of the footprint analysis stored procedures tab on Peer AI. The board provides a breakdown of the percentage of simple, medium, and complex operations.
Figure 2: Footprint analysis of database and application artifacts, part 2.

PeerAI then automates the generation of domain models and microservice architectures tailored for MongoDB’s document model. It refactors legacy code, such as stored procedures and in-line functions, into efficient, modern frameworks.

The platform also validates the migrated system, generating test suites to compare performance and functionality with the legacy setup.

Screenshot of the domain model dashboard in Peer AI, showing the legacy schema of the model.
Figure 3: Legacy and target domain model.

Screenshot of the generation of modernized code.
Figure 4: Generation of modernized code.

Diagram showing the accelerated timeline for modernization when using PeerAI. Instead of a 12 to 18 month timeline for traditional app modernization, PeerAI enables app modernization in 3 to 4 months, with reductions in effort for analysis, design, development, and validation.
Figure 5: Accelerated timeline for modernization using PeerAI.

A real-world transformation: Global-bank case study

A leading global bank faced the end-of-life for its Sybase ASE system, which included 10 application tables, 4 reference tables, and 22 stored procedures. Initially considering Amazon Aurora PostgreSQL, the bank found Aurora’s tooling insufficient for migrating stored procedures and maintaining functionality.

Turning to MongoDB and PeerIslands, the bank embarked on a modernization journey using PeerAI. The platform completed the following steps:

  • Conducted a deep analysis of the Sybase environment, mapping out dependencies and workflows

  • Designed a MongoDB schema optimized for scalability and performance

  • Refactored stored procedures into a Java / Spring Data JPA–based architecture

  • Validated the migration using AI-generated test cases, ensuring the new system exceeded legacy performance

  • Migrated data seamlessly, achieving zero downtime and ensuring alignment with the bank’s operational needs

The results were transformative. PeerAI reduced migration timelines by 75%, enabling the bank to quickly transition to a future-ready MongoDB environment. Beyond addressing the immediate challenge of Sybase’s end of life, the modernization unlocked new opportunities for real-time analytics, scalability, and innovation.

The key benefits of PeerAI

By automating critical steps in the migration process, PeerAI delivers tangible benefits:

  • Faster timelines: Traditional modernization projects take 12–18 months. PeerAI reduces this to just 3–4 months.

  • Cost savings: Automation reduces manual effort, lowering overall project costs by up to 50%.

  • Reduced risk: Comprehensive testing ensures the new system meets performance and reliability standards.

  • Future-ready architecture: MongoDB’s flexible, scalable platform positions businesses for long-term success.

A streamlined migration journey with PeerAI

Modernizing legacy Sybase systems is no longer a choice but a necessity for organizations seeking to thrive in a data-driven world. With MongoDB and PeerIslands’ PeerAI, businesses can navigate this transformation efficiently and confidently.

PeerAI turns what was once a lengthy, costly process into a streamlined journey, helping organizations transition to modern, cloud-native platforms with less risk and greater rewards. By embracing modernization, businesses not only address immediate challenges but also unlock the potential to innovate and grow in a rapidly changing digital landscape.

The future of data management is here, and it’s powered by MongoDB and PeerAI.

PeerIslands has joined the MongoDB AI Application Program (MAAP) to accelerate gen AI application development for organizations at any stage of their AI journeys. Visit the MAAP page to learn how ecosystem partners like PeerIslands can help your organization reduce time-to-market, lower risks, and maximize the value of your AI investments.

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MongoDB Named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems

I’m pleased to announce that MongoDB has been named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMSs) for the third consecutive year. In our view, this recognition cements MongoDB’s status as the only pure-play database provider in the cloud database management system category, underscoring MongoDB’s innovation, execution, and customer-centric approach. According to Gartner, “The cloud DBMS market remains as vibrant as ever and is transforming in important ways, especially in the use of gen AI and how DBMSs interact with other data management components. This Magic Quadrant will help data and analytics leaders make the right cloud DBMS choices in this essential market.” We believe this continued recognition by Gartner is a testament to MongoDB’s commitment to serving developers, as well as the investments we’ve made in our unified platform and integrated services. Driving innovation for enterprises MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. 2024 was a year of innovation and accolades at MongoDB, and I’m proud to share some of its highlights: In October, we released MongoDB 8.0 , the best performing version of MongoDB yet. MongoDB 8.0 is over 30% faster than the previous version of the database, it’s more secure than ever, horizontal scaling is faster and easier (at a lower cost), and MongoDB 8.0 gives teams greater control for optimizing database performance. We also launched—and grew—the MongoDB AI Applications Program (MAAP) . With MAAP, MongoDB offers customers a full AI stack and an integrated set of professional services to help them keep pace with innovation, identify the best AI use cases, and to help them future-proof AI investments. MongoDB became a founding member of the U.S. Artificial Intelligence Safety Institute Consortium . Established by the U.S. Department of Commerce’s National Institute of Standards and Technology, the Consortium supports the development and deployment of safe and trustworthy AI. MongoDB released hundreds of features and enhancements to accelerate innovation, manage costs, and simplify building applications at scale. MongoDB was recognized as the most loved vector database in Retool’s State of AI report —for the second consecutive year. The Gartner Magic Quadrant for cloud database management systems “Gartner defines the cloud database management systems (DBMSs) market as solutions designed to store, manipulate, and persist data, primarily delivered as Software-as-a-Service (SaaS). These systems must support transactional, analytical, and hybrid workloads while enabling enterprises to innovate across multi-cloud, hybrid, and intercloud ecosystems.” 1 It’s our opinion that this recognition by Gartner is a testament to MongoDB’s strong ability to execute and support customers today, as well as MongoDB’s comprehensive product vision that positions our platform to support tomorrow's operational workloads. What is the Magic Quadrant, and what is a Leader? “A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors.  By applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps you quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner’s market view.” 2 According to Gartner, “Leaders execute well against their current vision and are well positioned for tomorrow.” Overall, Magic Quadrants can help you “get quickly educated about a market’s competing technology providers and their ability to deliver on what end-users require now and in the future.” Powering innovation at scale with MongoDB Atlas Enterprises choose MongoDB Atlas because it gives them the freedom and agility they need to succeed in a rapidly evolving digital landscape. MongoDB Atlas’s multi-cloud architecture—including availability across Amazon Web Services, Google Cloud, and Microsoft Azure—ensures customers can design for unmatched scale and resilience. By automating functions like scaling and performance optimization , and giving them the ability to leverage industry-first capabilities like MongoDB Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data), with MongoDB Atlas customers can spend less time managing infrastructure and more time delivering experiences. MongoDB Atlas’s integrated capabilities to support multi-modal data types and use cases—like full-text and vector search , stream processing , and data federation —accelerate innovation, helping enterprises quickly respond to market changes, power AI-driven insights, and deliver meaningful digital experiences to their end users—all without the burden of operational complexity. Modernizing and building for the future In our opinion, the Gartner Magic Quadrant provides organizations with a clear and accessible evaluation framework to identify solutions that fit their needs, today and tomorrow. The placement of MongoDB in the Leader quadrant for Cloud Database Management Systems—for the third year in a row!—validates the efforts MongoDB has made to help developers and organizations take advantage of their most valuable resource, their data. I talk to MongoDB customers frequently, and many say the same thing: in today’s digital-first economy, AI-powered applications and scalable data infrastructure aren’t just advantages, they’re absolute necessities. They say that the time to act is now, and they’re looking for solutions that will help them innovate, streamline, and seize the AI-driven future. And when it comes to modernizing their operations, they consistently point to MongoDB as their go-to partner. Begin your cloud journey with MongoDB Atlas today. Contact our sales team or register for a free account to begin building! And to learn how MongoDB can help accelerate your AI journey, visit the MongoDB AI Applications Program page. Footnotes Gartner, Magic Quadrant for Cloud Database Management Systems,  Henry Cook, Ramke Ramakrishnan, et al., 18 December 2024 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 1 Gartner Peer Insights, Cloud Database Management Systems, December 2024 https://www.gartner.com/reviews/market/cloud-database-management-systems 2 Gartner Research Methodologies, Gartner Magic Quadrant, 20 December 2024 https://www.gartner.com/en/research/methodologies/magic-quadrants-research

December 23, 2024
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Test Out Search Like Never Before: Introducing Search Demo Builder

MongoDB is excited to announce the availability of Search Demo Builder , the newest addition to the Atlas Search Playground. The Search Demo Builder allows anyone to jump right in and discover the value of MongoDB Atlas Search without first creating an Atlas account. The Search Demo Builder offers an intuitive environment for testing and configuring common search features—without having to build an index or to write queries from scratch. What is the Search Demo Builder? Search Demo Builder is an interactive tool within the Atlas Search Playground that makes exploring MongoDB Atlas Search simple and accessible. It allows you to explore, configure, and experiment with key features like searchable fields, autocomplete, and facets—all without needing technical expertise, writing queries, or building indexes from scratch. Best of all, with Search Demo Builder you can see exactly how changes affect the search results through the Search Experience Preview. This feature gives you a real-time look at what your experience would look like as you tweak and configure your feature set. Some of the key features of Search Demo Builder include: Searchable fields utilizing dynamic fields as the default, but with the option to specify fields to search against. Autocomplete that can be configured on string fields to enable a search-as-you-type experience, and includes index definition and autocomplete query. Filters and facets that are interactive and can be configured on arrays of strings and numbers. Experience preview screen where features are reflected in an interactive preview experience. Index and query definitions that are auto-generated based on the configured search features Figure 1:   A view of the new Search Demo Builder experience. User benefits associated with Search Demo Builder include: Instant setup: Start immediately with preloaded datasets or upload your own small collection—no sign-up or complex configuration required. Guided exploration: Step-by-step product tours and tooltips make Search Demo Builder accessible for users of all skill levels. Interactive workspace: Experiment with features like autocomplete and facets in a dedicated, visual environment. Shareable indexes and queries: View and copy generated indexes and query definitions for use outside of Search Demo Builder. Search Demo Builder versus Code Sandbox The Search Demo Builder is designed to make Atlas Search accessible for users who prefer a visual interface and makes exploring and testing search features quick. The Code Sandbox , meanwhile, offers deeper customization and hands-on experimentation with JSON queries. Together, these tools provide a comprehensive environment for working with Atlas Search, regardless of your experience level. For more information on the Atlas Search Playground, including the Code Sandbox, check out our initial announcement blog . Get started with Search Demo Builder today Ready to try out Atlas Search for yourself? Head over to Search Demo Builder today and see what you can do with Atlas Search (you can also navigate to it in the lefthand navigation once you visit the Atlas Search Playground UI). Whether you’re testing out ideas for a new project or just getting your feet wet, the new Search Demo Builder provides an easy to navigate experience that makes getting started a breeze. Figure 2: &nbsp Lefthand nav panel with Search Demo Builder. To learn more about the Atlas Search Playground, visit our documentation . And be sure to share what you think in our user feedback portal .

January 8, 2025