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.

← Previous

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
Next →

MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024