Ventana Research's Latest Report Highlights MongoDB's Role as a Cloud Data Platform Provider

MongoDB
January 12, 2022 | Updated: July 6, 2022

Ventana Research, a market advisory and research firm, recently published an Analyst Perspective on MongoDB, noting that the MongoDB data platform provides businesses the ability to accelerate development and data-driven decision-making.

As Ventana Research explains the evolution from traditional databases to modern, cloud-based platforms, the study covered multiple trends related to both the present and future of data platform software. We have identified six key trends from MongoDB that were represented in the Ventana Research Analyst Perspective.

  1. Non-relational, or NoSQL, databases are on the rise. We see this as evidence of an unprecedented, widespread change in how businesses perceive and use their databases.

  2. Cloud-based services and products are rapidly gaining popularity. Given the rise of real-time, data-driven applications, organizations are relying more and more on the flexibility, availability, and functionality of cloud-native data platforms. Such products are ideal for quickly building competitive products, delivering highly personalized experiences, and improving business agility.

  3. As a result, operational database requirements will only become more demanding. As applications become more advanced, databases will become a pivotal part of an organization’s success — or failure. We believe that in order to keep up with their applications (and their competition), companies require a comprehensive, powerful platform like MongoDB Atlas.

  4. Convergence is the name of the game. As companies seek out new and better operational data platforms, both relational and non-relational database providers will venture into areas that were traditionally dominated by their competitors. Examples include non-relational databases (like MongoDB) adding relational features like ACID transactions, or relational databases offering compatibility for non-relational data models like graphs or documents.

  5. Companies are increasingly opting for hybrid and multi-cloud models. MongoDB Atlas’ multi-cloud clusters enable users to leverage exclusive provider features (like Google Cloud’s AI tools), improve availability in geographic regions, or migrate data across clouds with no downtime.

  6. Non-relational, cloud-native databases are becoming more powerful — and more attractive to customers. Thanks to convergence and competition, non-relational databases are becoming ever more capable. Their advancements include real-time analytics, rich visualizations, and mobile data sync and storage.

Read Ventana Research Analyst Perspectives to gain insight into the current data landscape and the possibilities of tomorrow.

Updated January 17, 2022.

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Safe Software Deployments: Through the Looking Glass

We’ve covered a lot of ground in this Safe Software Deployment series, from the 180 Rule to Z Deployments to the Goldilocks Gauge. But there is an elephant in the room. Or should I say, a jabberwock. In Lewis Carroll’s novel Through the Looking Glass , Alice discovers that the mirror above her mantle is not a mirror at all, but a doorway to another world in which things work very differently. When developers push software from staging to production, they often have a similar experience. Though they want to believe that staging and production are the same, they discover that staging is not a mirror at all, and production is another world, in which things work very differently. And out of that distortion come bugs and outages. The bottom line is this: Staging ≠ Production. And it never will. There are simply too many variables between these two environments to ever achieve exact alignment. Those environments can be different in hardware: CPU cores, threads per core, cache size, microcode; bus architectures; memory size; firmware. Or different in software or configuration: OS versions; compilers; libraries; network traffic profiles. Or different in network topology, edge caching; DNS and directory services. And of course we know that no matter how diligent you test in staging, customer workloads exercise the software in different ways. But the major reason they are different is because both environments have had a different set of software deployed to them over time, with a different set of configuration parameters - and a different combination of patches, hacks, and rollbacks. That staging is a mirror of production is one of the great delusions of software development. Developers often tell this lie to themselves, to overcome their fear of pushing to prod. Worse, developers often know the truth, but don’t really know how to explain this ambiguity to their management chain, leading to inevitable trust issues when deployments fail. So what can we do about it? First, accept reality. Modern, distributed software systems are nonlinear, and all test environments are simulacrums. It’s particularly important to help managers understand that even if it were possible to create exact duplicates of production – which it’s not – it would be practically and financially unjustifiable. Second, approach testing like an actuary. The leap from staging to production is essentially an exercise in probability. You should know your architecture, operational characteristics, and costs well enough to prioritize tests and reduce risk. You may even want to create two or more test environments that are deliberately different to reduce the odds of failure. And you should continue to run tests after the release, so you can surface bugs before your customers do. Third, if you can, treat both production and staging like cattle, not pets. If you have an enlightened software organization, one that believes in best practices, set up your systems so that you can blow away and recreate both your production and staging environments at regular intervals. This will reset many of the deviations and environment drift that build up over time. Finally, you have to be able to do automated rollbacks (the 180 Rule ) which work reliably ( Z Deployments ), and that are the right size for optimum efficiency and safety (the Goldilocks Gauge . ;-) I saved this column for last because completely eliminating the differences between staging and production is not a solvable problem. And frankly, you shouldn’t even try. But you don’t want to get caught flat-footed either, so you need a system of best practices that greatly reduces risk and fosters confidence. In other words, a system of Safe Software Deployment that helps you overcome the fear of pushing to prod. And most importantly, everyone in your organization needs to have a common understanding of the problem, from the top down. So feel free to print this post out, slide it under the door of your manager, and slink away like that Cheshire Cat. Have another technique for managing the divergence between staging and production? Share it with me at @MarkLovesTech .

January 11, 2022
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The Journey of MongoDB with COVESA in the Connected Vehicle Landscape

There’s a popular saying: “If you want to go fast, go alone; if you want to go far, go together.” I would argue The Connected Vehicle Systems Alliance (COVESA) in partnership with their extensive member network, turns this saying on its head. They have found a way to go fast, together and also go far, together. COVESA is an industry alliance focused on enabling the widespread adoption of connected vehicle systems. This group aims to accelerate the development of these technologies through collaboration and standardization. It's made up of various stakeholders in the automotive and technology sectors, including car manufacturers, suppliers, and tech companies. COVESA’s collaborative approach allows members to accelerate progress. Shared solutions eliminate the need for individual members to reinvent the wheel. This frees up their resources to tackle new challenges, as the community collectively builds, tests, and refines foundational components. As vehicles become more connected, the data they generate explodes in volume, variety, and velocity. Cars are no longer just a mode of transportation, but a platform for advanced technology and data-driven services. This is where MongoDB steps in. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More MongoDB and COVESA As the database trusted for mission-critical systems by enterprises such as Cathay Pacific , Volvo Connect , or Cox Automotive ; MongoDB has gained expertise in automotive, along with many other industries, building cross-industry knowledge in handling large-scale, diverse data sets. This in turn enables us to contribute significantly to vehicle applications and provide a unique view, especially in the data architecture discussions within COVESA. MongoDB solutions support these kinds of innovations, enabling automotive companies to leverage data for advanced features. One of the main features we provide is Atlas Device SDKs : a low-footprint, embedded database directly living on ECUs. It can synchronize data automatically with the cloud using Atlas Device Sync , our data transfer protocol that compresses the data handles conflict resolution, and only syncs delta changes, making it extremely efficient in terms of operations and maintenance. VSS: The backbone of connected vehicle data An important area of COVESA’s work is the Vehicle Signal Specification (VSS). VSS is a standardized framework used to describe data of a vehicle, such as speed, location, and diagnostic information. This standardization is essential for interoperability between different systems and components within a vehicle, as well as for external communication with other vehicles and infrastructure. VSS has been gaining more and more adoption, and it’s backed by ongoing contributions from BMW, Volvo Cars, Jaguar LR, Robert Bosch and Geotab, among others. MongoDB’s BSON and our Object-oriented Device SDKs uniquely position us to contribute to VSS implementation. The VSS data structured maps 1 to 1 to documents in MongoDB and objects in Atlas Device SDKs , which simplifies development, and speeds up applications by completely skipping any Relational Mapper layer. For every read or write, there is no need to transform the data between relational and VSS. Our insights into data structuring, querying, and management can help optimize the way data is stored and accessed in connected vehicles, making it more efficient and robust. Where MongoDB contributes MongoDB, within COVESA, finds its most meaningful contributions in areas where data complexities and community collaboration intersect. First, we can share insights into managing vast and varied data emerging from connected vehicles generating data on everything from engine performance to driver behavior. Second, we have an important role in supporting the standardization efforts, crucial for ensuring different systems within vehicles can communicate seamlessly. Our inputs can help ensure these standards are robust and practical, considering the real-world scenarios of data usage in vehicles. Some of our contributions include an Over the Air update architectural review presented at Troy COVESA’s AMM in October 2023; sharing insights about the Data Middleware PoC with BMW; and weekly contributions at the Data Expert Group. You can find some of our contributions on COVESA’s Wiki page . In essence, MongoDB's role in COVESA is about providing a unique perspective from the database management point of view, offering our understanding from different industries and use cases to support the developments towards more connected and intelligent vehicles. MongoDB, COVESA, and AWS together at CES2024 MongoDB’s most recent collaboration with COVESA was at the Consumer Electronics Show CES 2024 during which MongoDB’s Connected Vehicle solution was showcased. This solution leverages Atlas Device SDKs, such as the SDK for C++ , which enables local data storage, in-vehicle data synchronization, and also uni and bi-directional data transfer with the cloud. Below is a schematic illustrating the integration of MongoDB within the software-defined vehicle: Schema 1: End to end integration for the connected vehicle At CES 2024, MongoDB also teamed up with AWS for a compelling presentation, " AI-powered Connected Vehicles with MongoDB and AWS " led by Dr. Humza Akhtar and Mohan Yellapantula, Head of Automotive Solutions & Go To Market at AWS. The session delved into the intricacies of building connected vehicle user experiences using MongoDB Atlas. It showcased the combined strengths of MongoDB's expertise and AWS's generative AI tools, emphasizing how Atlas Vector Search unlocks the full lifecycle value of connected vehicle data. During the event, MongoDB also engaged in a conversation with The Six Five, exploring various aspects of mobility, self-driving vehicles (SDVs), and the MongoDB and AWS partnership. This discussion extended to merging IT and OT, GenAI, Atlas Edger Server, and Atlas Device SDK. Going forward At the end of the road, it’s all about enhancing the end-user experience and providing unique value propositions. Defect diagnosis based on the acoustics of the engine, improved crash assistance with mobile and vehicle telemetry data, just-in-time food ordering while on the road, in-vehicle payments, and much, much more. What all these experiences have in common is the combination of interconnected data from different systems. At MongoDB, we are laser-focused on empowering OEMs to create, transform, and disrupt the automotive industry by unleashing the power of software and data. We enable this by: Partnering with alliances such as COVESA to build a strong ecosystem or collaboration. Having one single API for In-vehicle Data Storage, Edge to Cloud Synchronization, Time Series storage, and more, improves the developer experience. Focusing on having a robust, scalable, and secure suite of services trusted by tens of thousands of customers in more than 100 countries. Together with COVESA’s vision for connected vehicles, we’re driving a future where this industry is safer, more efficient, and seamlessly integrated into the digital world. The journey is just beginning. To learn more about MongoDB-connected mobility solutions, visit the MongoDB for Manufacturing & Motion webpage . Achieving fast, reliable and compressed data exchange is one of the pillars of Software Defined Vehicles, learn how MongoDB Atlas and Edge Server can help in this short demo .

April 15, 2024