Congratulations to the 2021 Innovation Award Winners!
The MongoDB Innovation Awards honor projects and people who dream big. They celebrate the groundbreaking use of data to build compelling applications and the creativity of professionals expanding the limits of technology with MongoDB. This year the company received entries across dozens of industries, ranging from disruptive, emerging start-ups to industry-leading global enterprises. We are thrilled to announce the 12 winners who are being honored this year during MongoDB.live. William Zola Award: Michael Höller - An independent software architect, system integrator, and backend developer, Michael is the first MongoDB Champion to earn the rare Evergreen forum badge for his unwavering support of the MongoDB Community. Dual-certified as both a MongoDB Developer and DBA, Michael generously shares his expertise with community members of all levels in the MongoDB forums. He also organizes the DACH Virtual Community User Group, and even finds time to #BuildTogether with MongoDB employees on presentations and in chats during online streams. Michael is one of the first-timers, consulting on MongoDB projects since 2014. Customer-first Award: Luma Health - As COVID-19 took the world by storm, Luma Health jumped into action to partner with health systems and providers to leverage their platform to run some of the largest mass vaccination sites and help clinics scale from hundreds to thousands of appointments, ultimately leading to nearly 2 million vaccination appointments. Data for Good Award: Journey Foods - This company solves food science and supply chain inefficiencies with software in order to help companies feed 8 billion people better. To date, Journey Foods has established a database of over 11 billion ingredient insights. From Batch to Real Time Award: CSX - A leading provider of transportation and supply chain solutions, CSX is redefining freight rail. Embracing event-driven architecture, the company has improved engagement with safety information produced by Positive Train Control (PTC) systems by putting PTC data on MongoDB. Leveraging MongoDB, CSX receives the data real-time – enabling smarter and faster decision making and better ensuring safety regulations are met for the company’s around-the-clock operations. Front Line Heroes Award: Ahmad Awais for The “CORONA CLI” Project - Awais built a CLI command-line tool to track COVID-19 in March 2020. As COVID-19 spread, the project termed “corona-cli” became the number one trending repository on GitHub. To date, this project has served several billions of API requests making COVID stats accessible throughout the world with 53 different releases and extensive functionality built/contributed by 15+ developers. Going Global Award: Riot Games - Founded in 2009 to change the ways games were developed, Riot has created the most-played PC game in the world and expanded to 20+ offices worldwide in only 12 years. A game platform developer and his team migrated their data to MongoDB Atlas to manage B2B billing and player IP validation data for all of their games globally. Industry Transformation Award: American Airlines - As a network air carrier, American’s purpose is to care for people on life’s journey. During COVID-19, American Airlines passionately pursued efficiencies, particularly those enabled by technology. American Airlines created an operational data layer on MongoDB in the cloud for critical flight information, which enabled other services to move to the cloud and consume data from the modern cloud-based data fabric. Jackpot Award: Cisco Systems - Cisco is the worldwide leader in technology that powers the Internet. This global brand completed the Cloud native migration of its highly critical Commerce Quoting platform, which serves more than 225K users and 4M application hits daily worldwide. The result has been no application downtime for releases, improved performance, lower TCO, and significantly better developer productivity. Savvy Start-Up Award: Blerp - The audio expression platform that makes it easy to enhance any moment with sound clips. Millions of Blerps are being shared on their two largest integrations on Twitch and Discord. Unbound Award: Yodel - Yodel is an independently owned parcel carrier, delivering around 190 million parcels each year for many of the UK’s leading retailers and businesses. An early adopter of Realm Sync following its GA release in February 2021, Yodel uses MongoDB to sync parcel-scanning data from employee devices up to Atlas - and in the opposite direction, pushing down large data volumes to devices via the MongoDB Kafka Connector. By streamlining the process of scanning parcels and reducing the time drivers need to spend in service centers, Yodel expects to achieve increased productivity and cost savings. Certified Professional of the Year Award: Sydney Herrera - After becoming certified and while assisting a large governmental organization in a mainframe modernization effort - which involved transforming multiple massive, disparate mainframe datastores into a cohesive and application-focused MongoDB data warehouse, Sydney was faced with the challenge of assisting developers with building efficient applications. He created a tool called proactive query analyzer (PQA) that was rolled out into the organization. PQA is an automated tool that analyzes queries sent to MongoDB and provides feedback and suggestions before queries are implemented to aid developer teams. For the People Award & Innovator of the Year Award: The Department for Work and Pensions (DWP) is the UK’s biggest public service department responsible for distributing over £190 billion annually in welfare, pensions and child maintenance to over 20 million citizens. With an unprecedented spike in demand due to the COVID-19 crisis, the Universal Credit platform was able to scale seamlessly, underpinned by MongoDB databases, to meet the tenfold spikes in claims from people who needed DWP’s support. The information contained in the above descriptions was provided by the relevant award winners or obtained from publicly available information.
Fine-Tune Relevance in MongoDB Atlas Search with Function Scoring and Synonyms
MongoDB Atlas Search is an embedded full-text search solution in MongoDB Atlas that gives developers a seamless and scalable experience for building fast, relevance-based application features. We announced its general availability last year at MongoDB.live 2020 and over the past year we’ve introduced many new features, including a visual index builder, search query tester, custom analyzers , and wildcard path queries . This year at MongoDB.live 2021 , we’re excited to highlight two new capabilities that help developers tune the relevance of search results. See how easy it is to get started with MongoDB Atlas Search in this demo video by Marcus Eagan, Senior Product Manager for Atlas Search. Building relevance into search results Understanding the behavior of your users is essential when thinking about search result relevance. People don’t always tell you what they want, and they sometimes use words or phrases that don’t match your content exactly. To cover these scenarios, you can use full-text search features like function scoring and synonyms. Influence search rankings with function scoring There are often multiple factors that influence how search results should be ranked. For example, let’s say you have a restaurant finder application. The explicit inputs are things like the user’s location and what they’re searching for, but what’s implied is that they likely want to see highly rated restaurants or ones with more reviews. What’s Cooking: a sample restaurant finder application using MongoDB Atlas Search Function scoring allows you to influence the order of results returned by manipulating the score of each result. In Atlas Search, that means you can use a numeric field in a document and apply a mathematical expression to it. For example, you might want to increase the score of restaurants that are sponsored or have higher star ratings. This can easily be accomplished within the same search query by simply adding the function option to the score parameter of your query. Learn more about how to use function scores in our developer tutorial . Show results for more search queries with synonyms Synonyms are often used to define terms that are semantically similar to each other to improve search results. For example, someone searching for “noodles” might want to find results for “spaghetti”, “chow mein”, or “pad thai”. Synonyms can also help with typos, especially on mobile and small keyboards. In Atlas Search, you can define collections of synonyms for a search index via the API. Synonyms can be explicit (one-way) or equivalent (two-way). Explicit synonyms are good for defining relationships between terms that are subsets of each other, like the noodle example above: “spaghetti”, “chow mein”, and “pad thai” are all explicit synonyms for “noodles”, but not each other (you don’t want results for “chow mein” in a search for “spaghetti”). Equivalent synonyms are often used for terms that have regional variations or are otherwise interchangeable both ways, like soda and pop, or Kleenex and tissues. What's next for Atlas Search Developers are increasingly turning to full-text search to make content more discoverable and relevant for application end users. With Atlas Search, we hope to not only make building full-text search easier, but also more powerful and expressive. Join our community to ask questions and find out what other developers are building with Atlas Search and let us know what you think we should build next in our feedback forums .
Introducing Serverless Instances on MongoDB Atlas, Now Available in Preview
Since we first launched MongoDB Atlas in June 2016, we’ve been working towards building a cloud database that not only delivers a first-class developer experience, but also simply just works: no setup, tuning, or maintenance required. Over the years, this has led to features like auto-scaling and click-to-create index suggestions , along with numerous optimizations to our automation engine. We’re excited to announce that we’re one more step closer to realizing this vision with the introduction of serverless databases on MongoDB Atlas . Think less about your database, and more about your data Serverless computing and NoOps have emerged as popular trends in modern application development. Cloud functions are commonly used to power business logic in applications, and many teams rely on completely automated IT operations. The appeal of serverless technology is hard to deny: elastic scaling eliminates the need for upfront resource provisioning and ongoing maintenance, and consumption-based pricing means paying only for resources that are used. It abstracts and automates away many of the lower-level infrastructure decisions that developers don’t want to have to learn or manage so they can focus on building differentiated features. When it comes to databases, compute and storage resources have traditionally been tightly coupled. Applying a serverless model to databases means decoupling them and changing the way engineering teams think about infrastructure. Rather than asking a developer to predict an application’s future workload patterns, break them down into individual resource requirements, and then map them to arbitrary units of database instance sizes, serverless databases offer a much simpler experience: define where your data lives, and get a database endpoint you can use. This not only streamlines the database deployment process, it also eliminates the need to monitor and adjust capacity on an ongoing basis. Developers are free to focus on thinking about their data rather than their databases, and leave the lower-level infrastructure decisions to intelligent, behind-the-scenes automation. Serverless instances on MongoDB Atlas All customers now have the ability to create a serverless database on MongoDB Atlas with the introduction of serverless instances , announced at MongoDB.live 2021 . It’s incredibly easy to get started: simply choose a cloud region and you’ll receive an on-demand database endpoint for your application. Serverless instances always run on the latest MongoDB version so you never have to worry about backwards compatibility or upgrades. You can view and manage them using the same UI and API as your existing database deployment on Atlas (i.e., clusters), and they come with end-to-end security, continuous uptime, metrics, alerts, and backups. Watch this demo of how to create a serverless instance on MongoDB Atlas This new deployment type will be available in preview, so it doesn’t yet support all of the features and capabilities available on clusters today. It’s ideal for infrequent or sparse workloads, or development and testing workloads in the cloud. If you’re running a high-throughput production workload, dedicated clusters are still the recommended deployment option. A hands-free database experience This is the first of many releases, and we have an ambitious roadmap ahead. We will continue to invest in making working with data ever more seamless and delightful for developers, from adding support for newer Atlas capabilities like full-text search and native visualizations , to even more intelligent automation and optimization. Create your own serverless instance on MongoDB Atlas. Try the Preview If you have feedback or questions, we’d love to hear them! Join our community forums to meet other MongoDB developers and see what they’re building with serverless instances. What's next for MongoDB Atlas Serverless instances are just one of many new additions to Atlas that we hope will make developers’ lives easier. Earlier this year, we added index removal suggestions to Performance Advisor and released a quick start for creating and managing clusters via the command line with the MongoDB CLI . We are also working on integrations with Vercel and Netlify , two popular serverless application platforms, to give developers an easy way to get started on MongoDB Atlas. What would make your development experience better on MongoDB Atlas? Share your feature requests in our feedback forums .
Launched Today: MongoDB 5.0, Serverless Atlas, and the Evolution of our Application Data Platform
Today we welcome you to our annual MongoDB .Live developer conference. Through our keynote and conference sessions we'll show you all the improvements, new features, and exciting things we've been working on since last year’s conference. What I want to do in this blog post is provide you with a summary of what we are announcing, and resources to help you learn more. While it's easy to focus on what we are announcing at this year's event, we actually started out on this journey 12 years ago by releasing the world’s most intuitive and productive database technology to develop with — MongoDB. And we believe the applications of the NEXT 10 YEARS will be built on data architectures that continue to optimize for the developer experience, allowing teams like yours to innovate at speed and scale. So how are we building on this vision? Today I am incredibly proud to announce three big things: The General Availability (GA) of MongoDB 5.0, the latest generation of our core database. It includes native support for time series workloads, new ways to future-proof your applications, multi-cloud privacy controls, along with a host of other improvements and new features. The preview release of serverless instances on MongoDB Atlas, which makes it even easier for development teams who don’t want to think about capacity management at all to get the database resources they need quickly and efficiently. Major enhancements to Atlas Data Lake, Atlas Search, and Realm Sync, which allow engineering teams to reduce architectural complexity and get more value out of their data by leveraging a unified application data platform. MongoDB 5.0 GA MongoDB 5.0 is the latest generation of the database most wanted by developers . Our new release makes it even easier to support a broader range of workloads, introduces new ways of future-proofing your apps, and further enhances privacy and security. This major jump in version number from MongoDB 4.4 – our prior GA version – to 5.0 reflects a new era for MongoDB's release cadence: We want to get new features and improvements into your hands faster. Starting with MongoDB 5.0, we will be publishing new Rapid Releases every quarter, which will roll up into Major Releases once a year for those of you that want to maintain the existing annual upgrade cadence. You can learn more about the new MongoDB release cadence from our blog post published last October. Digging into MongoDB 5.0, here is what’s new and improved: Native Time Series Designed for IoT and financial analytics, our new time series collections, clustered indexing, and window functions make it easier, faster, and lower cost to build and run time series applications, and to enrich your enterprise data with time series measurements. MongoDB automatically optimizes your schema for high storage efficiency, low latency queries, and real-time analytics against temporal data. Running your time series applications on our application data platform eliminates the time and the complexity of having to stitch together multiple technologies yourself. You can manage the entire time series data lifecycle in MongoDB – from ingestion, storage, querying, real-time analysis, and visualization through to online archiving or automatic expiration as data ages. Time series collections can sit right alongside regular collections in your MongoDB database, making it really easy to combine time series data with your enterprise data within a single versatile, flexible database – using a single query API to power almost any class of workload. Our new time-series collections blog post gives you everything you need to get started. Future-proof with the Versioned API and Live Resharding Starting with MongoDB 5.0, the Versioned API future-proofs your applications. You can fearlessly upgrade to the latest MongoDB releases without the risk of introducing backward-breaking changes that require application-side rework. Using the new versioned API decouples your app lifecycle from the database lifecycle, so you only need to update your application when you want to introduce new functionality, not when you upgrade the database. Future-proofing doesn’t end with the Versioned API. MongoDB 5.0 also introduces Live Resharding which allows you to easily change the shard key for your collections on demand – with no database downtime – as your workload grows and evolves. The way I like to think about this is that we’ve extended the flexibility the document model has always given you down to how you distribute your data. So as things change, MongoDB adapts without expensive schema or sharding migrations. Next-Gen Privacy & Security MongoDB’s unique Client-Side Field Level Encryption now extends some of the strongest data privacy controls available anywhere to multi-cloud databases. And with the ability in 5.0 to reconfigure your audit log filters and rotate x509 certificates without downtime you maintain a strict security posture with no interruption to your applications. Run MongoDB 5.0 Anywhere MongoDB 5.0 is available today as a fully-managed service in Atlas . You can of course also download and run MongoDB 5.0 on your own infrastructure, either with the community edition of MongoDB, or with MongoDB Enterprise Advanced . The Enterprise Advanced offering provides sophisticated operational tooling via Ops Manager, advanced security controls, proactive 24x7 support, and more. MongoDB Ops Manager 5.0 enhancements include: Support for the automation, monitoring, and backup/restore of MongoDB 5.0 deployments. Improved load performance with parallelized client-side restores. A quick start experience for deploying MongoDB in Kubernetes with Ops Manager. And lastly, a guided Atlas migration experience that walks users through provisioning a migration host to push data from their existing environment into the fully managed Atlas cloud service. You can learn more about MongoDB 5.0 from our What’s New guide . New to MongoDB Atlas — Serverless Instances (Preview) We want developers to be able to build MongoDB applications without having to think about database infrastructure or capacity management. With serverless instances on MongoDB Atlas, now available in Preview, you can automatically get the database resources you need based on your workload demand. It’s really simple: the only decision you need to make is the cloud region hosting your data. After that, you’ll get an on-demand database endpoint that dynamically adapts to your application traffic. Serverless instances will support the latest MongoDB 5.0 GA release, Versioned API, and upcoming Rapid Releases so you never have to worry about backwards compatibility or upgrades. Pay only for reads and writes your application performs and the storage resources you use (up to 1TB of storage in preview) and leave capacity management to MongoDB Atlas’s best-in-class automation. We invite you to try it out today with a new or existing Atlas account. And the Preview release is just the beginning – we will be working with partners such as Vercel and Netlify to deliver an integrated serverless development experience in the coming months. In the longer term, we will continue to evolve our cloud-native backend architecture to abstract and automate even more infrastructure decisions and optimizations to deliver the best database experience on the market. The New MongoDB Shell GA The new MongoDB Shell has been redesigned from the ground up to provide a modern command-line experience with enhanced usability features and powerful scripting environment. It makes it even easier for users to interact and manage their MongoDB data platform, from running simple queries to scripting admin operations. A great user experience, even on a command-line tool, should always be a major consideration. With the new MongoDB Shell we have introduced syntax highlighting, intelligent auto-complete, contextual help and useful error messages creating an intuitive, interactive experience for MongoDB users. Check out this blog post for more information. MongoDB Charts and Atlas Data Lake: Better Together MongoDB Charts intuitive UI and ability to quickly create & share charts and graphs of JSON data is now integrated with Atlas Data Lake . You can now easily visualize JSON data stored in Amazon AWS S3 without any data movement, duplication or transformation. Furthermore, you can run Atlas Data Lake’s federated query to blend data across multiple Atlas databases and AWS S3, and visualize the results with Charts. By adding Atlas Data Lake as a data source in Charts, you can discover deeper, more meaningful insights in real time. Check out this blog post for more information. Atlas Search — More Relevance Features It’s incredibly important for modern applications to deliver fast and relevant search functionality: it powers discoverability and personalization of content, which in turn drives user engagement and retention. Atlas Search , which delivers powerful full-text search functionality without the need for a separate search engine, has several new capabilities for building rich end user experiences. We’ve recently added support for function scoring, which allows teams to apply mathematical formulas on fields within documents to influence their relevance, such as popularity or distance — e.g. closer restaurants with more or better reviews will show up higher in a list of results. In addition, you can now define collections of synonyms for a particular search index. By associating semantically equivalent terms with each other, you can respond to a wider range of user-initiated queries in your applications. Realm Realm lets you have simple, powerful local persistence on mobile phones, tablets and IoT devices like Raspberry Pi. The Realm SDKs provide a set of APIs that let developers store and interact with native objects directly, reducing the amount of code required as there is no need for ORMs or learning cryptic database syntax. In addition, we made MongoDB Realm Sync generally available earlier this year, making it easy to synchronize data between local storage on your devices and MongoDB Atlas on the backend. No need to worry about networking code or dealing with conflict resolution as we handle all of that for you. Today, we’re excited to announce support for Unity. You can now use Realm to store your game data, like scores and player state, and sync it automatically across devices. Realm's support for Unity is now Generally Available and ready for production workloads. We're also investing in support for more cross-platform frameworks — the Kotlin Multiplatform and Flutter/Dart SDKs are now both available in Alpha. And finally, the team is working towards Realm Flexible Sync, a new way to synchronize data with more granular control. Flexible Sync will allow you to — Build applications that respond dynamically to user's needs. Let your end users decide what data they need, and when. Use more precise permissions that can adapt over time. Check out this dedicated blog on our upcoming plans for Flexible Sync to learn more. Getting Started With everything we announced today, you can imagine it was a packed keynote! And there is so much more that we didn’t cover. You can get all of the highlights from our new announcements page where you will also find all the resources you need to get started.
MongoDB Atlas for Government
We are pleased to announce the general availability of MongoDB Atlas for Government, which is an independent environment of our flagship cloud product MongoDB Atlas that’s built for US government needs. It will allow federal, state, and local governments as well as educational institutions to build and iterate faster using a modern database-as-a-service platform. The service is available in AWS GovCloud (US) and AWS US East/West regions. We are also pleased to announce that MongoDB Atlas for Government has been approved as FedRAMP Ready . FedRAMP Ready indicates that a third-party assessment organization has vouched for a cloud service provider’s security capabilities, and the FedRAMP PMO has reviewed and approved the Readiness Assessment Report. MongoDB Atlas for Government Highlights: Atlas for Government clusters can be created in AWS GovCloud East/West or AWS East/West regions. Atlas for Government clusters can span regions within AWS GovCloud or within AWS (but not across those two environments). Atlas core features such as automated backups, AWS PrivateLink, AWS KMS, federated authentication, Atlas Search, and more are fully supported Applications can use client-side field level encryption with AWS KMS in GovCloud or AWS East/West. Getting Started and Pricing: MongoDB Atlas for Government is available to Government customers or companies that sell to the US Government. You can buy Atlas for Government through AWS GovCloud or AWS marketplace . Of course, you can also work directly with MongoDB; please fill out this form and a representative will get in touch with you. To learn more about Atlas for Government, visit the product page , check out the documentation , or read the FedRAMP FAQ .
MongoDB Atlas Celebrates Five Years of Innovation in Data
Today we’re thrilled to celebrate the five-year anniversary of Atlas, MongoDB’s multi-cloud database platform. When we launched Atlas in 2016, we couldn’t have foreseen the impact it would have on both our customers and MongoDB as a company. MongoDB Atlas has allowed us to become an ever more trusted partner to our customers, playing a key role in their efforts to manage and mitigate risk. One of the insights that spurred the development of Atlas — that it should be easy to move your data into, out of, and between clouds — remains as groundbreaking today as it was five years ago. Thanks to our commitment to innovation, reliability, and security, Atlas’s customers include well-known companies such as Forbes , Toyota Material Handling , Pitney Bowes , and 7-Eleven . Sixty of the Fortune 100 and many of the world’s most innovative disruptors rely on Atlas to help them grow, become more efficient, gain insights from their data, and create superior customer experiences. Atlas has transformed MongoDB into a cloud-first company: Atlas’s revenue is growing at 73 percent a year and currently accounts for more than half of MongoDB’s revenue. We got a glimpse of the future in 2009 with the first production version of MongoDB. For years, developers struggled to build modern applications on top of decades-old relational databases. Using a JSON-based document model, MongoDB was exceptionally fast, scalable, flexible, and intuitive for developers. It was unusually proficient with both structured and unstructured data. The database’s very design pushed developers and engineers to think differently about how they worked with data. As our customers investigated new business models enabled by the cloud, we noticed two things about the way they were working with data. First, they were increasingly choosing to use MongoDB in the cloud rather than hosting it on premises. Second, our customers were gravitating toward managed services. If our customers were going to the cloud — and they were — we needed to be there too. Our goal was not only to become a cloud-native database, but also to provide developers with a superior platform so they could change the world with data — and employ all the cloud’s potential to do it. As a managed service, Atlas would free developers from the overhead of managing MongoDB themselves. By making data portable across the biggest public clouds, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, it would free data from vendor lock-in. Developers and engineering teams could match the right cloud to the right workload in a few clicks and a few minutes without writing or rewriting code. Since Atlas shipped in 2016, it has gained more than 25,000 customers. We’ve dramatically improved its functionality, turning it into an even more powerful tool to fulfill our mission of making data stunningly easy to use. At its 2016 launch, Atlas was available in four AWS regions. The next year, we introduced cross-region replication. Within a single cloud, a customer could now enable cross-region deployments for even better availability guarantees. We launched global clusters in 2018, which made it easier to place data closer to the user and enabled our customers’ applications to run faster all over the world. MongoDB Atlas is now available in about 80 regions across AWS, Microsoft Azure, and GCP. Atlas customers can switch clouds as business requirements change to take advantage of pricing changes and make the best use of each cloud’s capabilities. Our broad reach also helps our customers comply with increasingly complex data sovereignty and localization requirements. We’ve pushed Atlas well beyond the ability to serve larger numbers of regions. In 2019 we acquired Realm , which makes data accessible no matter where your user is or how lousy their connectivity may be. The MongoDB Realm Mobile Database enables simple, powerful persistence on mobile devices so apps can work offline as well as they do online. Two years later, we released MongoDB Realm Sync, making it even easier to keep data in sync across users and devices and connect to the Atlas database on the backend. We also released MongoDB Atlas Search, which enables developers to create rich, relevance-based search without moving their data into a separate search engine. That was accompanied by MongoDB Atlas Data Lake, enabling developers to use federated queries to analyze data across tiers. In 2019 we introduced client-side field-level encryption, an industry-leading approach to security. It’s relatively common to encrypt data at rest or in transit, but client-side field-level encryption encrypts data while it’s in use. There’s no additional code for developers to write and no significant impact on performance, and applications can still query data. Client-side field-level encryption enables our clients to use managed services in the cloud with more confidence, because even those who support the underlying cloud infrastructure cannot decrypt the data. It also makes it easier to comply with increasingly common “right to be forgotten” mandates in contemporary privacy legislation. A user can be forgotten simply by destroying the associated encryption key, making their data unreadable and irrecoverable. In 2020, our development teams accomplished what had been their mission since the inception of Atlas: multi-cloud clusters. With multi-cloud clusters, MongoDB Atlas goes well beyond its promise — fulfilled years earlier — to work equally well in any of the public clouds. Multi-cloud clusters enable a single cluster to be in multiple clouds simultaneously, making it trivial to move data between them. We have big plans for the next five years, and we’re already getting started. Soon we’ll preview serverless instances on MongoDB Atlas, making it even easier for development teams to get the capacity they need, when they need it. You choose the region that hosts your data and we’ll do the rest, with an on-demand database endpoint that dynamically adapts to your application traffic. We’re also making it easier to support a broader range of workloads, offering new ways to future-proof apps, and continuing to improve security and privacy capabilities. We’ll be making major enhancements to Atlas Data Lake, Atlas Search, and Realm Sync, all of which reduce architectural complexity and allow our customers to get more value from their data with a unified application data platform. And we’ll be doing it all on an accelerated cadence. Starting with MongoDB 5.0, we’ll publish new releases every quarter for those who want to be on the fast track, and then rolling those up into annual Major Releases for those who want to stay on the current cycle. We expect the next five years to be just as exciting and innovative as the past five — if not more. We can’t wait to see you there. For more on Atlas's Five Year Anniversary, check out this video
Competitive Advantage Cannot Be Bought: Accelerating Digital Transformation With MongoDB's Application Data Platform
Business disruption isn’t new. But it is accelerating to a new, ever-more-disruptive level. That acceleration is being amplified by the forces of our era, from global health crises to political shifts to climate change, among others. These disruptive forces are creating huge changes in corporate behavior and consumer expectations. When considering changing business conditions, whether from adaptation or innovation, it’s worth asking, “Is this something that is going to default back to the way it was before?” In most cases, the answer is "probably not.” Disruption is here to stay. While that can be scary for incumbent enterprises, we believe that disruption is the opposite of stagnation. It is an opportunity for change, adaptation, and movement. In 2020, many companies had to completely rethink how they accomplished their business goals and were pushed to implement new strategies at an accelerated pace. Years of digital transformation were condensed into months. Companies developed new structures and flexible models — internal muscles that will continue serving them as they move forward. The pace at which digital transformation happens is only accelerating. With an increasingly digital economy, we’re seeing: No more barriers to entry. Obstacles to entering the digital economy are essentially gone. For example, a bank considering fintech disruption is no longer just competing with interregional banks. It’s now competing with every mobile-first challenger bank anywhere in the world. Competition is more high stakes than ever. A need to enable sustainable innovation. The most competitive businesses are maniacally focused on removing every obstacle so that their teams can be agile and move quickly in a scalable and sustainable way. An emphasis on data security. As more of the IT landscape moves into the cloud, and as organizations increasingly go global, data security and privacy need to be at the forefront. It’s critical that a company’s customers trust them to be the stewards of their data now and into the future. However, not everyone’s digital transformation is successful. Many companies can’t keep up with new competitors and sudden market shifts. Your competitive advantage Competitive advantage is now directly tied to how well companies are able to build software around their most important asset: data. Companies have been using commercial software since the early 1970s. What is different now is that their differentiation as a business is tied to the software they build internally. No one is expecting to win in their industry because they use an off-the-shelf CRM product, for example. That is to say: Competitive advantage cannot be bought, it must be built. This is not a new idea. Even the most basic software cannot work without proper storage, retrieval, and use of data. Otherwise, every application would have a beautiful front end but nothing functional behind them. The true art and skill of modern application development is to tap into data and wield it to create true innovation. Customer experience and expectations Almost 15 years into the smartphone and smart device era, consumer and B2B expectations for their digital interactions and experiences are extremely high and exceptionally demanding. Customers expect their digital experiences to: Be highly responsive: Digital experiences must be quick to react to events, actions, and consumer behavior. Deliver relevant information: Modern digital experiences present the most relevant information intelligently, sometimes even predicting what a consumer is searching for before they complete their thought. Embrace mobile first: Mobile is becoming the primary way customers interact with companies. They expect to be able to do everything they would’ve done on a desktop from their mobile devices. Uphold data privacy: Customers expect complete data privacy — and for companies to allow them to take control of their data when requested. Be powered by real-time analytics: Customers expect applications to be smart. In addition to all of the above, consumers expect apps to guide them, assure them, and delight them with rich experiences powered by analytics and delivered in real time. Continuously improve: Customer expectations demand improvements at a faster rate than ever before. Legacy infrastructure: A challenge in digital transformation Companies' ability to deliver on customer expectations is almost entirely reliant on their underlying data infrastructure — it’s the foundation of their entire tech stack. Modern digital experiences demand a modern data infrastructure that addresses how data is stored, processed, and used. Despite companies’ best efforts — and significant spending — it’s estimated that more than 70% of enterprises fail in their digital transformation initiatives . This alarming number can make it appear as though digital improvement is a gamble not worth taking, but this is most definitely not the case. The truth is that though the way companies leverage data to build modern applications has changed, the typical data infrastructure has not kept up with the demands, making working with data the hardest part of building and evolving applications. One key factor is that typical data infrastructures are still built around legacy relational databases. These outdated infrastructures mean: Rigidity. The rigidity of the rows and columns that define relational databases make experimenting and iterating on applications difficult. Anytime a data model or schema needs to be changed as an application evolves or incorporates new types of data, developers must consider dependencies at the data layer, and the brittle nature of relational databases makes such change difficult. Data structure clashes. Relational tabular data structures are at odds with how most developers think and code, which is through object-oriented programming languages. To put it simply, developers do not think in rows and columns, which clash with modern data and the objects developers work with. Automatic failover and horizontal scaling are not natively supported. The essentials that modern applications need, such as automatic failover and support for massive scale on demand, are not natively built into legacy relational databases; these become more obstacles to overcome. With a relational data infrastructure, it’s typical for an organization to have hundreds or thousands of tables built up over years. Having to update or unwind them as it is trying to build or iterate on its applications brings innovation to a crawl — or puts it on pause altogether. As an example, Travelers , a Fortune 500 U.S.-based insurance company, recently attempted to modernize an underwriting application. Their most profitable unit, business insurance, required much faster software delivery and response times. The company attempted to solve this with standard solutions, such as implementing agile development, breaking down monoliths with microservices, and rolling out continuous delivery. Despite their best efforts, however, legacy relational databases held them back. Travelers’ senior director of architecture at the time, Jeff Needham, in reference to their attempts at transformation, said, “At the end, it was still the legacy database. We implemented all these things, but we still had to wait days for a database change to be implemented.” Both Travelers’ failing result and eventual frustration are shared by many organizations that get ensnared in their own data infrastructures. What about NoSQL? For teams that need to deliver more modern application experiences or operate at faster speeds, it might appear that the most obvious path is to add NoSQL datastores as a bandage to address relational shortcomings. But doing so requires ETL (extract, transform, and load data from one source to another), adding more complexity to data management. These teams quickly realize that non-relational or NoSQL databases only excel at a few select objectives, with otherwise limited capabilities (including limited query capabilities and the lack of data consistency guarantees). The truth is that NoSQL databases can never really replace relational databases because they’re suitable only for niche use cases. In the end, it’s not just one database being added and requiring management but several — one for graph data and traversals, one for time series, one for key values, and so on. The ever-increasing need to address diversified data workloads means a new managed database for each type of data, creating even more silos. The bottom line is that adding NoSQL to cover what relational databases can’t makes the data environment even more complex than it was before. Beyond operational databases Today, an organization’s application data infrastructure is made up of more than just operational databases. To deliver rich search capabilities, companies often add separate search engine technologies to their environments, putting the onus on their teams to manage the movement of data between systems. To enable low-latency and offline-first application experiences on mobile devices, they often add separate local data storage technologies. Syncing data from devices to the backend becomes another spinning plate for developers to keep up with since it involves complexities such as networking code and conflict resolution. Finally, to create rich analytics-backed application experiences, more often than not organizations use ETL for their data, reformatting it along the way for an entirely separate analytics database. Every step of the way, more time, people, and money goes toward what is now a growing data infrastructure problem — an increasing sprawl of complexity — and eating away at development cycles that could otherwise be spent innovating their products. Spaghetti architecture is a tax on innovation As they try to solve data issues by adding new components, services, or technologies, many companies find themselves trapped in “spaghetti architecture,” meaning overly complex and siloed architectures piled on top of already heavy infrastructures. Each bit of technology has its quirks from operational, security, and performance standpoints, demanding expertise and attention — and making data integration difficult. Moving data between systems requires dedicated people, teams, and money. Massive resources go into dealing with the incredible amount of data duplication. But beyond just cost, development resources must go toward dealing with multiple operational and security models when data is distributed across so many different systems. This makes it incredibly difficult to innovate in a scalable, sustainable way. In fact, this is why many digital transformations fail: inadequate data infrastructures, burning through resources, and “solutions” creating more complexity. And all while, they are falling behind their competitors. We think of all of this as a recurring tax on innovation tied to an ever-growing data infrastructure problem that we call DIRT (data and innovation recurring tax). DIRT is recurring because it never goes away by itself. It’s a 2,000-pound boulder strapped to a team’s back today, tomorrow, and five years from now. It will continue to weigh down teams until they address it head on. Eliminate DIRT DIRT is a real problem, but there are equally real, and realistic, solutions. The most successful and advanced organizations avoid such complexities altogether by building data infrastructures focused on four key guidelines: Doubling developer productivity. Companies’ success depends on their developers’ ability to create industry-leading applications, so these businesses prioritize removing any obstacles to productivity, including rigid data structures, fragmented developer experiences, and backend maintenance. Prioritizing elegant, repeatable architectures. The companies that will win the race toward data integrity understand the cost of bespoke data infrastructures and technologies that only make their production environments more complex. These companies use niche technologies only when absolutely necessary. Intentionally addressing security and data privacy. Successful businesses don’t let data security and privacy become a separate and massive project. They’re able to satisfy sophisticated requirements without compromising simplicity or the developer experience. Leveraging the power of multi-cloud. These companies don’t compromise on deployment flexibility. They’re ahead of data gravity and can deploy a single application globally across multiple regions and multiple clouds without having to rewrite code or spend months in planning. How MongoDB helps MongoDB provides companies with an application data platform that allows them to move fast and simplify how they build with data for any application. This allows organizations to spend less effort rationalizing their data infrastructure and focus more on innovation and building out their unique differentiation, eliminating DIRT. The document model is flexible and maps to how developers think and code. The flexible document data model makes it easy to model and remodel data as application requirements change, increasing developer productivity by eliminating the headache of rows and columns. Instead, documents map exactly to the objects that developers work with in their code. This is the core insight that MongoDB’s founders had at least a decade ago: Data systems fundamentally need a different data model to be able to match modern development. This is also why MongoDB has become so popular with developers, with 75 million+ downloads. MongoDB documents are a superset of all other data models. The MongoDB document model upholds the superset of legacy functions, allowing users to store and work with data of various types and shapes. In contrast to niche databases, it covers the needs of relational data, objects, cache formats, and specialized data such as GIS data types or time series data. Document databases are not just one of many other databases to be used simultaneously. Advanced organizations realize that the document model underpins a broad spectrum of use cases. For example, the simplest documents serve as key-value pairs. Documents can be modeled as the nodes and edges of a graph. Documents are actually more intuitive for modeling relationships with support for embedding and nested structures. The ability to work with diverse varieties of data fits neatly within the document data model, giving MongoDB a concrete foundation to build from. MongoDB features a powerful, expressive, and unified interface. This provides for improved productivity because developers do not need to research, learn, and stay up-to-date on multiple ways to work with data across their different workloads. It’s also much more natural to use than SQL because the developer experience is one that feels like an extension of programming languages. The experience is idiomatic to each programming language and paradigm; developers can view MongoDB documents in their native format and work directly with the data without the need for abstraction layers such as object relational mappers (ORMs), data abstraction layers (DALs), and more — they can simply be removed or retired. Furthermore, multiple different teams working in different programming environments — from C# to Java to C++ — can access the same data at their leisure, allowing simplification and integration of data domains. MongoDB: The application data platform MongoDB is more than just a database. It is a multi-purpose, multi-faceted application data platform. This means that MongoDB recognizes data comes in a wide variety of formats, needs to be processed, stored, trained (and so on) in a broad variety of ways, and needs to be regulated, audited, encrypted, and secured in a similarly diverse set of ways. Data is one of the most valuable yet complex assets companies have. MongoDB simplifies many different use cases to wield this important asset in an intelligent, beautiful way by offering a unified interface to work with data generated by modern applications. MongoDB brings together two foundational concepts — the document model and a unified query API — in the form of an operational and transactional database. MongoDB’s application data platform offers: A transactional database: MongoDB has the transactional guarantees and data governance capabilities required to not just supplement but actually replace relational databases. Distributed multi-document transactions are fully ACID compliant, making it the transactional database behind core mission-critical applications. Search capabilities: Fully integrated full-text search eliminates the need for separate search engines. The MongoDB platform includes integrated search capabilities, including an extended query API, so developers are not forced to stand up a dedicated search engine to enable application search. All of the operations, including data movement, is handled natively within the MongoDB platform. Mobile readiness: MongoDB Realm’s flexible local datastore includes seamless edge-to-cloud sync for mobile and computing at the edge. MongoDB Realm enables agility for mobile developers through a natural development experience that syncs data from the backend to the front end with minimal code required. Things like conflict resolution, networking code, and plumbing are all handled automatically. Real-time analytics: MongoDB offers real-time analytics with workload isolation and native data visualization. As more organizations design smarter applications with MongoDB, they can call on real-time analytics tied to either machine learning or direct application experiences. Data lake: With MongoDB, developers can run federated queries across operational databases, transactional databases, and cloud object storage. Queries can also be extended across multiple clusters, or even to data sitting outside of MongoDB. MongoDB’s architecture is able to federate and aggregate data for analytical purposes as needed. A sustainable platform: In real-world applications, no capabilities matter if the platform is not secure, resilient, and scalable. Only sustainable frameworks can evolve with changes in the market and demand for the product. Scalability and compliance: Everything at MongoDB is built on a distributed systems architecture, with turnkey global data distribution for data sovereignty and fast access. This is not just for horizontal scaling and linear cost economics as workloads get larger, but it also helps organizations handle data distribution for their global applications, keeping relevant data close to the user but, for example, distributed across different geographic regions as needed to deliver a low-latency experience. MongoDB can also be used to isolate data by country to address data sovereignty regulations. Security: MongoDB holds industry-leading data privacy controls with client-side field level encryption, having built security into the core of the database, whether it's with encrypted storage, role-based access controls, or enterprise-grade auditing. In a world where there are often third-party providers involved, this gives more control to the end customer so they can definitively say that no third-party provider can access sensitive data, preventing full breaches of security. Multi-cloud: With MongoDB, developers have the flexibility to deploy across clouds in nearly 80 regions. Extending their databases across multiple clouds allows developers to leverage the innovative services that may be associated with another provider, build cross-cloud resiliency, and get additional reach without having to stand up separate databases in different regions. This, in turn, allows for a unified developer experience across data workloads, a simpler operational and security model, an automated and transparent data movement between services, and reduction of the dreaded data duplication. Interested in getting started with MongoDB Atlas for your digital transformation? Start for free here or contact us directly.
MongoDB.live Returns this Summer
MongoDB.live is returning in July! Join us for the all-virtual, all-encompassing conference on the future of data. As the signature convention of the world’s most wanted database , MongoDB.live has something for everyone. Explore current trends and upcoming technologies, build key skills, and connect with other data enthusiasts of all levels and interests. What's in Store? This free event will run from July 13-14, and will include engaging presentations, immersive tutorials, interactive discussions, and much more. Whether you’re a DevOps engineer or a solutions architect, you’ll find plenty of useful, engaging, and enlightening content. On Day 1, kick off MongoDB.live with an exciting keynote headlined by MongoDB executives, experts, and users, who will introduce MongoDB 5.0, and walk you through the latest product announcements — and how these new features will transform how you work with data. Day 2 will commence with a roundtable panel of MongoDB power users, who will share helpful tips stemming from their own experiences, and discuss how MongoDB solutions have helped their progression to the Cloud and to SaaS. Both days will feature dozens of educational breakout sessions, deep-dive tutorials for everything from serverless to sharding, live “Ask Me Anything” panels with MongoDB experts and executives, and much more. What Happened Last Year? In 2020, MongoDB.live went online for the first time. During the keynote , Sahir Azam, our Chief Product Officer, explained how the MongoDB team integrated every feature into a seamless, holistic platform that addresses any data need a developer may have. To demonstrate the MongoDB platform in action, Sahir invited the marine program team from WildAid, a leading global conservation nonprofit, onto the (virtual) stage. WildAid spoke about their mission of protecting vulnerable aquatic ecosystems, gathering vital data, and ensuring compliance with a dizzying array of regulations — and how they used every part of the MongoDB application data platform, from Realm to Atlas Search, to support their operations. Participants also attended breakouts, talks, and tutorials, including practical advice for building a non-linear career from Capitol Hill to coding; a discussion on open sourcing mentorship; and a walkthrough on building a serverless app with MongoDB Atlas, Realm, and AWS; to name a few. Lastly, we recognized our 2020 Innovation Award winners, and highlighted MongoDB users and customers who were doing groundbreaking work in their fields. The list of recipients boasts household names like Toyota and Pizza Hut alongside smaller, equally savvy organizations such as Zinc and dacadoo. How to Join Please register today to reserve your spot. As we get closer to the event, you’ll receive a confirmation email with instructions to create your profile — which you can use to bookmark sessions and tracks, and fill in your personal details for networking. During the week before the event, you’ll also be able to access our digital Partner Pavilion and its resources, including entry-level talks, downloadable materials, sponsor booths, and more. On the days of the conference, you can access MongoDB.live from your desktop or mobile device. As we get closer to MongoDB.live, keep checking the event website , which will be regularly updated with information on workshops, speakers, and what to expect. We can’t wait to see you (virtually)!
Why I Wrote the New MongoDB Aggregations Book
In early May 2021, I published my book, Practical MongoDB Aggregations, which I released electronically and free for anyone to read . I love the MongoDB database and the uniqueness and power of its aggregation framework to analyse and manipulate massive amounts of data intuitively and efficiently. The opportunity to share this passion with others spurred me to write the book, with which I aim to support developers, architects, data analysts, data engineers, and data scientists to better understand how to maximise their productivity and effectiveness when building aggregation pipelines, as well as how to optimise these pipelines. Like many people over the past year during the pandemic, I’ve struggled to keep myself occupied when not busy doing my day job. Hence, my book was born not just from a desire to improve people’s knowledge but as my pandemic project, written over many weekends, to stave off the boredom. I believe aggregation pipelines provide a powerful domain-specific language for data processing in a way I’ve not seen before in other data-oriented tools, languages, or standards. SQL is a good data query language that caters to some analytical use cases via “group-by/having” statements. However, it typically has to be paired with a procedural language (e.g., Oracle’s PL/SQL ) to encompass an ordered set of complex data transformation rules. In the big data world of Hadoop , I find the MapReduce approach is too complex to develop with efficiently. Higher-level tools like Spark help alleviate some of this. However, by the necessity of still having to be general-purpose and versatile, the amount of Spark code required to process data sitting in any type of database is still too high for my liking. Many ETL tools provide proprietary data transformation capabilities, but these have to cater to the lowest common denominator capabilities across all the different types of databases they interact with. For these reasons and from experience, I consider MongoDB Aggregations to be the best tool for processing large data sets because it combines performance with productivity. Nevertheless, I sense the aggregation framework is shrouded in mystery for many people, hence my desire to demystify it with this book. I believe I identified a knowledge gap that many users wanted to be filled. MongoDB Inc. provides excellent reference documentation about aggregations in the MongoDB Manual , and MongoDB University provides a tremendous free online training course on aggregations . What I felt was still to be addressed was an opinionated yet informed perspective on how best to assemble aggregation pipelines from the well-documented parts—something that points the way to achieve optimal productivity and performance, accompanied by fully formed example pipelines to help put these approaches into practice. I hope readers of my book will learn some new things of value and enjoy reading it. A good test of the relevance of my book, in time, will be if people come back to it repeatedly as they continue with their journey of developing aggregations. Read the book for free now!