News From MongoDB.local NYC: Game-changing Migration Tool Now Available

Jon Lewis
June 22, 2023 | Updated: October 2, 2023
#Relational Migrator

This post is also available in: Deutsch, Français, Español, Português

The database has always been the heart of an application. Teams recognize that the choice of a database is one of the most critical decisions in developing software, influencing not only the speed at which developers can build and iterate, but also an application’s performance, functionality, and overall user experience.

Today, tens of thousands of organizations are being held back by their legacy systems built on relational databases. They struggle with the headaches, high costs, and poor developer agility caused by data silos and architecture sprawl built up over decades as they try to address new requirements and build transformative experiences.

Modernizing the data infrastructure means reducing architectural complexity, and adopting flexible data platforms like MongoDB that can support a wider range of use cases and data types. But every IT leader knows that transitioning away from legacy databases is easier said than done, and that they are often overwhelming and time-consuming projects. This is even more true when trying to shake the relational database mindset and instead adopt a developer-first approach to data.

MongoDB's Relational Migrator is now available to help organizations streamline their transition from relational databases to MongoDB by tackling some of the most common challenges we see in migration projects: effective data modeling, migrating data, and modernizing app code. Today, Relational Migrator supports migrations from Oracle, SQL Server, MySQL, and PostgreSQL directly.

By switching to MongoDB, companies can reap major rewards when it comes to modernizing an essential component of their software. Not only do they enjoy faster development and performance, but also improved reliability, scalability, and user experience. Additionally, MongoDB's advanced features, such as full-text search, time-series data support, and edge-to-cloud synchronization enable applications to take on more use cases on an efficient, easy-to-maintain data infrastructure.

With Relational Migrator, teams preparing for app modernization projects can accelerate and reduce the risk of their relational database migrations so they can start taking advantage of everything MongoDB has to offer.

Take the guesswork out of the migration process

Data modeling in MongoDB is one of the most common barriers we see when organizations come over from a relational background. MongoDB can be treated like a relational database, but to unlock the full potential of the document model organizations need to change how they think about their data and model it for how their applications are going to use it.

Relational Migrator takes the guesswork out of data modeling by analyzing an application's existing relational schema and proposing a recommended MongoDB schema based on best practices. As teams use the visual interface to compare and build their schema, Relational Migrator will continue to offer recommended mappings to further expedite schema design, but more importantly, help developers do it well.

Not only does Relational Migrator visually compare schemas, but it also applies the transformations as it moves data into MongoDB in an efficient manner, providing users with the ability to run both systems in parallel and apply changes from the source database to MongoDB on an ongoing basis.

Migrating data is only one part of application modernization. To give developers a head start, Relational Migrator even generates app code for various development languages and frameworks that reflects the new MongoDB schema they’ve designed.

With the launch of Relational Migrator, MongoDB is unlocking the full potential of its powerful developer data platform, providing new implementation possibilities for customers and a wider range of applications.

Learn more about what Relational Migrator can do for you.

Head to the MongoDB.local hub to see where we'll be showing up next.

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Introducing Atlas Stream Processing - Simplifying the Path to Reactive, Responsive, Event-Driven Apps

Update May 2, 2024: Atlas Stream processing is now generally available. Read our blog to learn more . This post is also available in: Deutsch , Français , Español , Português , 中文 Atlas Stream Processing is now in public preview. Learn more about what’s new! Today, we’re excited to announce the private preview of Atlas Stream Processing ! The world is increasingly fast-paced and your applications need to keep up. Responsive, event-driven applications bring digital experiences to life for your customers and accelerate time to insight and action for the business. Think: Notifying your users as soon as their delivery status changes Blocking fraudulent transactions during payment processing Analyzing sensor telemetry as it is generated to detect and remediate potential equipment failures before costly outages. In each of these examples, data loses its value as the seconds tick by. It needs to be queried and actioned continuously and with low latency. To do this, developers are increasingly turning to event-driven applications fueled by streaming data so that they can instantly react and respond to the constantly changing world around them. Atlas Stream Processing will help developers make the shift to event-driven apps faster. Over the years, developers have adopted the MongoDB database because they love the flexibility and ease of use of the document model, along with the MongoDB Query API which allows them to work with data as code. These foundational principles dramatically remove friction from developing software and applications. Now, we are bringing those same principles to streaming data. Atlas Stream Processing is redefining the developer experience for working with complex streams of high velocity, rapidly changing data, and unifying how developers work with data in motion and at rest. While existing products and technologies have offered many innovations to streaming and stream processing, we think MongoDB is naturally well suited to help developers with some key remaining challenges. These challenges include the difficulty of working with variable, high volume, and high-velocity data; the contextual overhead of learning new tools, languages, and APIs; and the additional operational maintenance and fragmentation that can be introduced through point technologies into complex application stacks. Introducing Atlas Stream Processing Atlas Stream Processing enables processing high-velocity streams of complex data with a few unique advantages for the developer experience: It’s built on the document model, allowing for flexibility when dealing with the nested and complex data structures common in event streams. This alleviates the need for pre-processing steps while allowing developers to work naturally and easily with data that has complex structures. Just as the database allows. It unifies the experience of working across all data, offering a single platform – across API, query language, and tools – to process rich, complex streaming data alongside the critical application data in your database. And it’s fully managed in MongoDB Atlas , building on an already robust set of integrated services. With just a few API calls and lines of code, you can stand up a stream processor, database, and API serving layer across any of the major cloud providers. Watch the MongoDB .local NYC Keynote to see Atlas Stream Processing announced by our Chief Product Officer, Sahir Azam. He covers the emergence of streaming data and how it powers a variety of use cases, key streaming challenges, and how Atlas Stream Processing can help you build modern, event-driven applications. Head of Streaming Products, Kenny Gorman, then goes through a live demo of Atlas Stream Processing in action. How does Atlas Stream Processing work? Atlas Stream Processing connects to your critical data, whether that lives in MongoDB (through change streams ) or in an event streaming platform like Apache Kafka. Developers can easily and seamlessly connect to Confluent Cloud, Amazon MSK, Redpanda, Azure Event Hubs, or self-managed Kafka using the Kafka wire protocol. And by integrating with the native Kafka driver, Atlas Stream Processing offers low-latency native performance at its foundation. In addition to our long-standing strategic partnership with Confluent, we are also excited to announce partnerships with AWS, Microsoft, Redpanda, and Google, at launch. Atlas Stream Processing then provides 3 key capabilities required to turn your firehose of streaming data into differentiated customer experiences. Let’s go through these one by one. Continuous processing First, developers can now use MongoDB’s aggregation framework to continuously process rich and complex streams of data from event streaming platforms such as Apache Kafka. This unlocks powerful new ways to continuously query, analyze, and react to streaming data without any of the delays inherent in batch processing. With the aggregation framework, you can filter and group data, aggregating high-velocity event streams into actionable insights over stateful time windows, powering richer, real-time application experiences. Continuous validation Next, Atlas Stream Processing offers developers robust and native mechanisms to handle incorrect data issues that can otherwise cause havoc in applications. Potential issues include passing inaccurate results to the app, data loss, and application downtime. Atlas Stream Processing solves these problems to ensure streaming data can be reliably processed and shared between event-driven applications. Atlas Stream Processing: Provides Continuous Schema Validation to check that events are properly formed before processing – for example rejecting events with missing fields or containing invalid value ranges Detects message corruption, and Detects late-arriving data that has missed a processing window. Atlas Stream Processing pipelines can be configured with an integrated Dead Letter Queue (DLQ) into which events failing validation are routed. This avoids developers having to build and maintain their own custom implementations. Issues can be quickly debugged while the risk of missing or corrupt data bringing down the entire application is minimized. Continuous merge Your processed data can then be continuously materialized into views maintained in Atlas database collections. We can think of this as a push query. Applications can retrieve results (via pull queries) from the view using either the MongoDB Query API or Atlas SQL interface. Continuously merging updates to collections is a really efficient way of maintaining fresh analytical views of data supporting automated and human decision-making and action. In addition to materialized views, developers also have the flexibility to publish processed events back into streaming systems like Apache Kafka. Creating a Stream Processor Let’s show you how easy it is to build a stream processor in MongoDB Atlas. With Atlas Stream Processing, you can use the same aggregation pipeline syntax for a stream processor that you’re familiar with from the database. Below we’re showcasing a simple stream processing instance from start to finish. It takes just a few lines of code. First, we’ll write an aggregation pipeline that defines a source for your data, performs validation ensuring data is not coming from the localhost/127.0.0.1 IP address, creates a tumbling window to collect grouped message data every minute, and then merges that newly processed data into a MongoDB collection in Atlas. Then, we’ll create our Stream Processor called “netattacks” specifying our newly defined pipeline p as well as dlq as arguments. This will perform our desired processing, and by using a Dead Letter Queue (DLQ), will store any invalid data safely for inspection, debugging, or re-processing later. Lastly, we can start it. That’s all it takes to build a stream processor in MongoDB Atlas. Request private preview We’re excited to get this product into your hands and see what you build with it. Learn more about Atlas Stream Processing and request private preview to participate in the private preview once it opens to developers. New to MongoDB? Get started for free today by signing up for MongoDB Atlas . Head to the MongoDB.local hub to see where we'll be showing up next. Safe Harbor The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.

June 22, 2023
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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024