Empowering Automotive Developers for the Road Ahead

Jessica Baumgardner and Humza Akhtar
August 22, 2023 | Updated: September 8, 2023

MongoDB 7.0 is here, and companies across industries are benefiting from being early adopters of cutting-edge data platform technology.

Let’s take a closer look at the automotive industry specifically, and how many of MongoDB’s new features and capabilities can revolutionize the way automotive developers build, iterate, and scale their applications. In the fast-changing automotive landscape, development teams face the challenge of delivering compelling user experiences faster and smarter than ever before. MongoDB's developer data platform becomes a vital tool for developers striving to innovate quickly and efficiently, supporting a wide range of application use cases while streamlining development and ensuring optimal performance.

MongoDB Atlas Stream Processing

MongoDB Atlas Stream Processing, coming soon in private preview, will be a game-changing advantage for the automotive industry, offering real-time data insights and rapid responses to critical events. As vehicles generate an ever-increasing stream of sensor data, this capability enables automotive developers to process, analyze, and act upon data in real-time. Manufacturers and fleet management companies can monitor vehicle health, track performance, and optimize maintenance schedules on the fly, while proactive safety measures and anomaly detection ensure utmost safety for drivers and passengers. Moreover, MongoDB Atlas Stream Processing enables developers to unlock the potential of connected car applications, making real-time data processing imperative for intelligent navigation, personalized infotainment services, and efficient route planning.

MongoDB Atlas Vector Search

MongoDB Atlas Vector Search, currently in public preview, holds immense potential for revolutionizing the automotive industry. By utilizing vector representations of unstructured data such as audio, images, and text, MongoDB Atlas enables developers to store, index, and query data based on similarities in high-dimensional vector spaces alongside operational data. For the automotive industry, this means unlocking a world of possibilities in data analysis, anomaly detection, and predictive maintenance. In fact, as mentioned in the MongoDB.local Chicago keynote, a top 10 auto manufacturer leveraged Vector Search to enable engine diagnostics based on engine audio. Watch the video below to learn more.

Atlas Vector Search empowers automotive developers to create smarter, data-driven applications that deliver more relevant and accurate insights, ultimately enhancing the driving experience and safety for all. MongoDB Atlas Vector Search allows manufacturers to query and qualify possible equipment and product failure causes and get AI-generated recommendations on how to adjust operational parameters and extend the life of their equipment and products. The automotive industry thrives on innovation and efficiency, and Atlas Vector Search opens new avenues for optimizing vehicle performance, predicting maintenance needs, and enhancing overall user experiences on the road.

MongoDB Relational Migrator

In the ever-evolving automotive industry, legacy relational databases often pose challenges in scalability, flexibility, and performance. Relational databases are very prevalent in the manufacturing industry and hinder innovation due to rigid data models and limited scalability. MongoDB Relational Migrator addresses these pain points by assisting with several critical steps in the path to modernization for automotive developers. By migrating data from common relational databases to MongoDB, automotive companies can break free from the limitations of legacy systems and embrace the full potential of a NoSQL database. This migration process streamlines data transfer, offers valuable data modeling recommendations, and empowers developers to refactor applications quickly and efficiently.

Embracing MongoDB's flexible document data model optimizes performance, scales applications effortlessly, and unlocks the potential for real-time analytics, enabling the industry to stay ahead in the race for innovation. MongoDB Relational Migrator becomes a catalyst for driving transformative change in the automotive sector, enabling faster and more efficient data processing for mission-critical applications and paving the way for sophisticated AI-driven solutions. As automotive companies embrace data-driven insights and strive to deliver unparalleled user experiences, MongoDB Relational Migrator empowers the industry to leverage the full potential of NoSQL databases, enabling automotive applications to zoom ahead in the fast lane of innovation.

MongoDB 7.0 promises to be a game-changer for developers across industries, empowering them to build innovative, scalable, and secure applications that drive the future. With the power of MongoDB, developers can accelerate their journey toward automotive innovation and build the vehicles and experiences of tomorrow.

Watch the full MongoDB.local lineup to learn more.

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Crafting Precision: Query Enhancements in MongoDB 7.0

Writing performant queries is the holy grail for any developer working with databases. It’s akin to an art form – balancing speed, efficiency, and functionality in one seamless package. At MongoDB .local Chicago, we announced the General Availability of MongoDB 7.0 , the latest version of our database which includes a number of new features targeted at making the application development experience streamlined and boosting developer productivity. Constructing efficient queries frequently entails navigating complex optimization and index management processes. With MongoDB 7.0, this process has been significantly streamlined. Introducing compound wildcard indexes Now, imagine writing less code and utilizing fewer system resources to get the results you need faster. With MongoDB 7.0, Compound Wildcard Indexes make this dream a reality. Compound wildcard indexes support creating compound indexes on a combination of any field and all sub-fields in an embedded sub-document. This supports performant queries against a combination of an unknown or arbitrary field and an always-present field. For example, consider a collection named customer_attributes with a diverse schema. Not every customer has every attribute filled out. Compound wildcard indexes can now support queries on Region or Tier and any of the sub-fields under Attributes , thus embracing MongoDB’s schema flexibility. db.customer_attributes.createIndex({"Region": 1, "Attributes.$**": 1}); This index could support a query like this, efficiently and easily: db.customer_attributes.find({"Region": "North America", "Attributes.CSM": "John Doe"}); Bitwise operators: The power in your pipeline Before MongoDB 7.0, developers can leverage faster computations with Bitwise Operators in the Aggregation Pipeline . While this has been available for some time with find and update queries, MongoDB 7.0 allows you to compute common bitwise operators within aggregations, right on the database, significantly enhancing query performance, especially for application-driven and operational analytics. Let's take an example of filtering documents based on binary data flags: { $match: { $expr: { $eq: [{$bitAnd: ["$userFlags", 4]}, 4] } } } This filter checks whether the fourth bit of the userFlags field is set, a common scenario in permission systems. Percentile operators: Unlocking efficient data analysis Calculating percentiles is a cornerstone of data analysis. With MongoDB 7.0, Approximate Percentile Operator enables developers to perform these calculations directly on the database server, significantly enhancing efficiency and reducing complexity. For example, to find the 90th percentile of response times in a weblogs collection: { $group: { _id: null, responseTime90thPercentile: { $percentile: { input: "$responseTime", p: [0.9], method: 'approximate'} } } } Role-based data access with $$USER_ROLES The new $$USER_ROLES variable introduces an elegant way to deliver user-specific views right from your queries, offering an enhanced layer of security and data privacy. No more separate views for each user role – now it’s seamless and simplified. Consider a sales application, where account data can be seen by everyone but sensitive information like contract details can only be seen by specific managers. By leveraging the new variable and setting up a view as below, you can ensure only managers with that specific role can view the data: pipeline = [ { $set: { "contractDetails": { $cond: { if: { $in: [ "csmApp", '$$USER_ROLES.role' ] }, then: "$contractDetails", else: "$$REMOVE" } } } } ] db.createView('accounts_view', 'accounts', pipeline ) This ensures that users can only view the data tied to their account, based on their role privileges. Conclusion: Elevating the developer experience MongoDB 7.0’s new query features are more than just a set of tools; they represent a profound upgrade in the developer experience. With these features, you're not just writing queries - you are sculpting your application’s data interaction in the most efficient way, right from the start. From Compound Wildcard Indexes to new operators in the aggregation pipeline, MongoDB 7.0 empowers developers to write cleaner, faster, and more intuitive queries. With less time spent on query optimization, you can focus on what truly matters: building amazing applications. Build your next application with a streamlined development experience .

August 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