Network, Build, and Learn at MongoDB.local Events — Now Free to Attend
Panel Discussion at MongoDB.local London, 2021 Every year, MongoDB hosts popular MongoDB.local events in major cities around the world. Packed with workshops, talks, and keynotes, these one-day, in-person gatherings bring together engineers, entrepreneurs, and executives from the surrounding area. This year, for the first time, admission to MongoDB.local events is free. (Note that admission is granted on a first-come, first-served basis, limited only by seating capacity.) Five upcoming events Five MongoDB.local events are scheduled for the remainder of 2022, and you can register for the .local event near you through the links below or through the MongoDB.local hub page . Frankfurt , September 27, 2022 San Francisco , October 20, 2022 Dallas , October 27, 2022 London , November 15, 2022 Toronto , December 15, 2022 From sessions on the future of serverless to demos of next-generation technology, here’s what to expect at a MongoDB.local event near you. Learn from the experts Whether you attend keynote presentations or participate in customer discussions, you can tap into a wealth of knowledge from people and organizations that are thoroughly familiar with today’s technology landscape. You’ll learn from MongoDB experts, who will share hard-earned knowledge, practical solutions, and technical insight based on firsthand experience with common issues. You can also attend talks from MongoDB customers, which are generally centered around a specific use case and solution — a sort of shared retrospective for the public. At .local Frankfurt, for example, an engineer from Bosch will discuss the company’s evolution from individual documents to time series data in an IoT environment. All MongoDB.locals include sessions for a wide array of skill levels and specialities, such as a deep dive into the new Queryable Encryption feature or an introduction to building a basic application using Atlas Device Sync and React. These workshops offer practical, actionable advice that you can implement immediately upon returning to your office. Expand your professional network MongoDB.local events also offer many opportunities to expand your personal and professional network. In particular, these gatherings are a great way to connect with members of your local MongoDB User Group, who are likely working with the same technologies (or facing similar challenges) that you are. Whether you’re searching for a new job or business opportunity, looking for tips and techniques to implement in your own environment, or just browsing for inspiration, you’ll likely find what you seek at MongoDB.local. Explore the latest products Product booths are another highlight of MongoDB.local events. Staffed by MongoDB product teams, these booths are where you can pick up limited edition stickers, discuss the latest developments with expert engineers, and see new MongoDB features in action. Every event also features booths where third-party partners, vendors, and allies demonstrate cutting-edge technology, show how their platforms and services work in tandem with MongoDB, and answer any questions you may have. Stop by these booths to explore the next big thing in data, see how MongoDB can provide new solutions for pressing problems, and come away with helpful, personalized advice for your own challenges. Enjoy a one-of-a-kind experience From Frankfurt’s Klassikstadt to London’s Tobacco Dock , MongoDB.locals are held at unique, memorable venues. Step inside refurbished historical sites, such as a former factory turned automobile museum or a shipping wharf converted into a top-tier event space. In addition to a full day of talks and tutorials, attendees can enjoy breakfast, lunch, snacks, and drinks served at MongoDB.locals. Join us for a day packed with learning and networking opportunities in a venue near you. Whether you’re a decision-maker or a developer, you’ll find something interesting, enlightening, or useful at MongoDB.local. Learn more about our upcoming MongoDB.local events in Frankfurt , San Francisco , Dallas , London , and Toronto , and register for your free ticket.
Free your data with the MongoDB Relational Migrator
Nothing is more frustrating than data that is just out of reach. Imagine wanting to combine customer behavior data from your CRM and usage data from your legacy product to trigger tailored promotions in your new mobile app, but not being able to locate the required data in the sea of tables in your relational database. As MongoDB CTO Mark Porter explains in his MongoDB World keynote , the data that can make a difference might be locked up “somewhere that you can’t use.” Relying on his own hard-earned experience with data, Porter adds that this information can be trapped “in a schema with hundreds or thousands of tables that have built up over decades.” “Schema is a huge part of this problem,” MongoDB product manager Tom Hollander explains during a presentation on MongoDB Relational Migrator at MongoDB World 2022. “So we’ve spent a lot of time building out the tools to enable you to map your tabular relational schema into a document schema and make use of the full power of the MongoDB document model.” To see MongoDB Relational Migrator in action, check out this introduction and demo from MongoDB World 2022, featuring MongoDB product manager Tom Hollander. What is MongoDB Relational Migrator? MongoDB Relational Migrator streamlines migrations from legacy data infrastructure to MongoDB by helping developers analyze relational database schemas, convert them into MongoDB schemas, and then migrate data from the source database to MongoDB. Currently, Relational Migrator is compatible with four of the most common relational databases: Oracle, SQL Server, MySQL, and PostgreSQL. Migrator not only moves data from your relational database to MongoDB, but it also transforms it according to your new schema. As Hollander and MongoDB product marketing director Eric Holzhauer point out , developers often use a mix of software and tools (e.g., extract-transform-load pipelines, change data capture (CDC), message queues, and streaming) to execute migrations, which can be complicated, risky, and error-prone. Relational Migrator provides a single tool that can streamline the process while simultaneously ensuring that your data lands in an organized, logical manner. By simplifying schema translations — one of the most complex, difficult parts of any relational migration — Relational Migrator grants developers and other technical teams a greater degree of control over (and increased visibility into) their new MongoDB schema. The result is to make data more accessible for analysis and decision making. “Now I can get at the data in my program without going through a translation layer,” Porter explains. A visual representation of how Migrator maps relational schema to document schema. Migration mode: Snapshot or ongoing? Migrator provides two modes of data transfer: a one-time snapshot or a continuous sync (which will be available later this year). To help decide which mode you should use, consider whether you can move over to MongoDB and immediately decommission your previous database or whether you need to keep your existing relational database up and running. Organizations may wish to keep their relational database for various reasons, such as testing the effectiveness of your proposed document schema, running out the contract or licensing agreement to avoid expensive fees, or keeping old databases available for audits. In this situation, you can keep your relational database running so that Relational Migrator will continue to push data from your source to your new MongoDB clusters. The limits of Relational Migrator As Hollander points out, Relational Migrator is only a tool — one intended to facilitate schema mapping, providing many abilities and options for effective schema design. “It’s not a silver bullet that will immediately modernize your application portfolio,” Hollander says. “It’s not going to do everything for you. You still have to do the planning.” Furthermore, because database schema is a tricky topic even for seasoned experts, Hollander recommends that developers would benefit from working with architects, consultants, and partners — especially if they’re not familiar with MongoDB or schema design best practices. Relational Migrator does not yet support continuous replication, which would enable your relational database and MongoDB clusters to coexist for an extended period of time. However, Hollander says that work on this feature is ongoing and it will be available in the future, along with additional capabilities like schema recommendations, an integration for the MongoDB Atlas developer data platform, and more. MongoDB Relational Migrator is currently in early access, for use on non-production workloads with assistance from our Product and Field Engineering teams. To learn more, get in touch with your MongoDB rep or contact us via our Migrator page to discuss your workload and next steps.
3 Reasons (and 2 Ways) to Use MongoDB’s Improved Time Series Collections
Time series data, which reflects measurements taken at regular time intervals, plays a critical role in a wide variety of use cases for a diverse range of industries. For example, park management agencies can use time series data to examine attendance at public parks to better understand peak times and schedule services accordingly. Retail companies, such as Walmart , depend on it to analyze consumer spending patterns down to the minute, to better predict demand and improve shift scheduling, hiring, warehousing, and other logistics. As more sensors and devices are added to networks, time series data and its associated tools have become more important . In this article, we’ll look at three reasons (and two ways) to use MongoDB time series collections in your stack. This in-depth introduction to time series data features MongoDB Product Manager Michael Gargiulo. Reason 1: Purpose-built for the challenges of time series data At first glance, time series collections resemble other collections within MongoDB, with similar functionalities and usage. Beneath the surface, however, they are specifically designed for storing, sorting, and working with time series data. For developers, query speed and data accessibility continue to be challenges associated with time series data. Because of how quickly time series data can accumulate, it must be organized and sorted in a logical way to ensure that queries and their associated operations can run smoothly and quickly. To address this issue, time series collections implement a key tenet of the MongoDB developer data platform: Data that is stored together is accessed together. Documents (the basic building block of MongoDB data) are grouped into buckets, which are organized by time. Each bucket contains time series data from a variety of sources — all of which were gathered from the same time period and all of which are likely to show up on the same queries. For example, if you are using time series collections to analyze the rise in summer temperatures of Valencia, Spain from 1980 to 2020, then one bucket will contain temperatures for August 1991. Relevant, but distinct buckets (such as temperatures for the months of June and July 1991) would also be stored on the same page for faster, easier access. MongoDB also lets you create compound indexes on any measurement field in the bucket (whether it’s timeField or metaField) for faster, more flexible queries. Because of the wide variety of indexing options, operations on time series data can be executed much more quickly than with competing products. For example, scan times are reduced by indexing buckets of documents (each of which has a unique identifier) rather than individual documents. In terms of the previous example, you could create an index on the minimum and maximum average summer temperatures in Valencia, Spain from 1980 to 2020 to more quickly surface necessary data. That way, MongoDB does not have to scan the entire dataset to find min and max values over a period of nearly four decades. Another concern for developers is finding the last metadata value, which in other solutions, requires users to scan the entire data set — a time-consuming process. Instead, time series collections use last point queries, where MongoDB simply retrieves the last measurement for each metadata value. As with other fields, users can also create indexes for last points in their data. In our example, you could create an index to identify the end of summer temperatures in Valencia from 1980 to 2020. By indexing the last values, time series collections can drastically reduce query times. Another recurring challenge for time series applications is data loss from Internet of Things (IoT) applications for industries such as manufacturing, meteorology, and more. As sensors go offline and gaps in your data appear, it becomes much more difficult to run analytics, which require a continuous, uninterrupted flow of data. As a solution, the MongoDB team created densification and gap filling. Densification, executed by the $densify command, creates blank, placeholder documents to fill in any missing timestamps. Users can then sort data by time and run the $fill command for gap filling. This process will estimate and add in any null or missing values in documents based on existing data. By using these two capabilities in tandem, you will get a steady flow of data to input into aggregation pipelines for insights. Reason 2: Keep everything in house, in one data platform Juggling different data tools and platforms can be exhausting. Cramming a bunch of separate products and technologies into a single infrastructure can create complex architectures and require significant operational overhead. Additionally, a third-party time series solution may not be compatible with your existing workflows and may necessitate more workarounds just to keep things running smoothly. The MongoDB developer data platform brings together several products and features into a single, intuitive ecosystem, so developers can use MongoDB to address many common needs — from time series data to change streams — while reducing time-consuming maintenance and overhead. As a result, users can take advantage of the full range of MongoDB features to collect, analyze, and transform time series data. You can query time series collections through the MongoDB Compass GUI or the MongoDB Shell , utilize familiar MongoDB capabilities like nesting data within documents, secondary indexes, and operators like $lookup or $merge, and process time series data through aggregation pipelines to extract insights and inform decision making. Reason 3: Logical ways to organize and access time series data Time series collections are designed to be efficient, effective, and easy to use. For example, these collections utilize a columnar storage format that is optimized for time series data. This approach ensures efficiency in all database operations, including queries, input/output, WiredTiger cache usage, and storage footprints for both data and secondary indexes. Let’s look, for example, at how querying time series data collections works. When a query is executed, two things happen behind the scenes: Bucket unpacking and query rewrites. To begin with, time series collections will automatically unpack buckets — similar to the $unwind command. MongoDB will unscroll compressed data, sort it, and return it to the format in which it was inserted, so that it is easier for users to read and parse. Query rewrites work alongside bucket unpacking to ensure efficiency. To avoid unpacking too many documents (which exacts a toll in time and resource usage), query rewrites use indexes on fields such as timestamps to automatically eliminate buckets that fall outside the desired range. For example, if you are searching for average winter temperatures in Valencia, Spain from 1980 to 2020, you can exclude all temperatures from the spring, summer, and fall months. Now that we’ve examined several reasons to consider MongoDB time series collections, we’ll look at two specific use cases. Use case 1: Algorithmic trading Algorithmic trading is a major use case for time series data, and this market is predicted to grow to $15 billion by 2028 . The strength of algorithms lies in their speed and automation; they reduce the possibility of mistakes stemming from human emotions or reaction time and allow for trading frequency beyond what a human can manage. Trading algorithms also generate vast volumes of time series data, which cannot necessarily be deleted, due to compliance and forecasting needs. MongoDB, however, lets you set archival parameters, to automatically move that data into cheaper cloud object storage after a preset interval of time. This approach preserves your valuable storage space for more recent data. Using MongoDB products such as Atlas, materialized views, time series collections, and triggers, it is also possible to build a basic trading algorithm. Basically, time series data will be fed into this algorithm, and when the conditions are ideal, the algorithm can buy or sell as needed, thus executing a series of individual trades with cumulative profits and losses (P&L). Although you’ll need a Java app to actually execute the trades, MongoDB can provide a strong foundation on which to build. The structure of such an algorithm is simple. Time series data is loaded from a live feed into MongoDB Atlas, which will then input it into a materialized view to calculate the averages that will serve as the basis of your trades. You can also add a scheduled trigger to execute when new data arrives, thereby refreshing your materialized views, keeping your algorithm up to date, and not losing out on any buying/selling opportunities. To learn more, watch Wojciech Witoszynski’s MongoDB World 2022 presentation on building a simple trading algorithm using MongoDB Atlas, “Algorithmic Trading Made Easy.” Use case 2: IoT Due to the nature of IoT data, such as frequent sensor readings at fixed times throughout a day, IoT applications are ideally suited for time series collections. For example, Confluent, a leading streaming data provider, uses its platform alongside MongoDB Atlas Device Sync , mobile development services, time series collections, and triggers to gather, organize, and analyze IoT data from edge devices. IoT apps often feature high volumes of data taken over time from a wide range of physical sensors, which makes it easy to fill in meta fields and take advantage of densification and gap filling features as described above. MongoDB’s developer data platform also addresses many of the challenges associated with IoT use cases. To begin with, MongoDB is highly scalable, which is an important advantage, given the huge volumes of data generated by IoT devices. Furthermore, MongoDB includes key features to enable you to make the most of your IoT data in real time. These include change streams for identifying database events as they occur, or functions, which can be pre-scheduled or configured to execute instantaneously to respond to database changes and other events. For users dealing with time-based data, real-time or otherwise, MongoDB’s time series collections offer a seamless, highly optimized way to accelerate operations, remove friction, and use tools, such as triggers, to further analyze and extract value from their data. Additionally, users no longer have to manually bucket, query, or otherwise troubleshoot time series data; instead, MongoDB does all that work for them. Try MongoDB time series collections for free in MongoDB Atlas .
MongoDB 6.0 Now Available!
MongoDB 6.0 is now available for download. This major release introduces improvements to existing features as well as new products to empower you to build faster, troubleshoot less, and cut out complexity from your workflows. Continuing with the theme of the developer data platform concept introduced at MongoDB World 2022 , MongoDB 6.0’s new and enhanced abilities help remove the need for outside platforms in your tech stacks or application architectures. This means less time managing fundamentally incompatible solutions and more time building applications and solutions. MongoDB 6.0 includes several feature upgrades, more integrations, support for a diverse range of scenarios, and much more. For instance, time series collections and change streams can now be used for additional use cases, such as geo-indexing or finding the before and after states of documents, respectively. Additionally, MongoDB 6.0 includes exciting new releases for security, analytics, search, and more. One innovative new capability is Queryable Encryption , a first-of-its-kind technology that allows you to efficiently query data even as it remains encrypted, only decrypting it when it’s made available to the user. To learn more about MongoDB 6.0, read “7 Big Reasons to Upgrade to MongoDB 6.0” and visit the MongoDB 6.0 homepage to learn more — and to upgrade now.
7 Big Reasons to Upgrade to MongoDB 6.0
First announced at MongoDB World 2022, MongoDB 6.0 is now generally available and ready for download now. MongoDB 6.0 includes the capabilities introduced with the previous 5.1–5.3 Rapid Releases and debuts new abilities to help you address more use cases, improve operational resilience at scale, and secure and protect your data. The common theme in MongoDB 6.0 is simplification: Rather than forcing you to turn to external software or third-party tools, these new MongoDB capabilities allow you to develop, iterate, test, and release applications more rapidly. The latest release helps developers avoid data silos, confusing architectures, wasted time on integrating external tech, missed SLAs and other opportunities, and the need for custom work (such as pipelines for exporting data). Here’s what to expect in MongoDB 6.0. 1. Even more support for working with time series data Used in everything from financial services to e-commerce, time series data is critical for modern applications. Properly collected, processed, and analyzed, time series data provide a gold mine of insights — from user growth to promising areas of revenue — helping you grow your business and improve your application. First introduced in MongoDB 5.0, time series collections provide a way to handle these workloads without resorting to adding a niche technology and the resulting complexity. In addition, it was critical to overcome obstacles unique to time series data, such as high volume, storage and cost considerations, and gaps in data continuity (caused by sensor outages). Since its introduction, time series collections have been continuously updated and improved with a string of rapid releases . We began by introducing sharding for time series collections (5.1) to better distribute data, before rolling out columnar compression (5.2) to improve storage footprints, and finally moving on to densification and gap-filling (5.3) for allowing teams to run time series analytics — even when there are missing data points. As of 6.0, time series collections now include secondary and compound indexes on measurements, improving read performance and opening up new use cases like geo-indexing. By attaching geographic information to time series data, developers can enrich and broaden analysis to include scenarios involving distance and location. This could take the form of tracking temperature fluctuations in refrigerated delivery vehicles during a hot summer day or monitoring the fuel consumption of cargo vessels on specific routes. We’ve also improved query performance and sort operations. For example, MongoDB can now easily return the last data point in a series — rather than scanning the whole collection — for faster reads. You can also use clustered and secondary indexes to efficiently perform sort operations on time and metadata fields. 2. A better way to build event-driven architectures With the advent of applications like Seamless or Uber, users have come to expect real-time, event-driven experiences, such as activity feeds, notifications, or recommendation engines. But moving at the speed of the real world is not easy, as your application must quickly identify and act on changes in your data. Introduced in MongoDB 3.6, change streams provide an API to stream any changes to a MongoDB database, cluster, or collection, without the high overhead that comes from having to poll your entire system. This way, your application can automatically react, generating an in-app message notifying you that your delivery has left the warehouse or creating a pipeline to index new logs as they are generated. The MongoDB 6.0 release enriches change streams, adding abilities that take change streams to the next level. Now, you can get the before and after state of a document that’s changed, enabling you to send updated versions of entire documents downstream, reference deleted documents, and more. Further, change streams now support data definition language (DDL) operations, such as creating or dropping collections and indexes. To learn more, check out our blog post on change streams updates . 3. Deeper insights from enriched queries MongoDB’s aggregation capabilities allow users to process multiple documents and return computed results. By combining individual operators into aggregation pipelines, you can build complex data processing pipelines to extract the insights you need. MongoDB 6.0 adds additional capabilities to two key operators, $lookup and $graphlookup , improving JOINS and graph traversals, respectively. Both $lookup and $graphlookup now provide full support for sharded deployments. The performance of $lookup has also been upgraded. For instance, if there is an index on the foreign key and a small number of documents have been matched, $lookup can get results between 5 and 10 times faster than before. If a larger number of documents are matched, $lookup will be twice as fast as previous iterations. If there are no indexes available (and the join is for exploratory or ad hoc queries), then $lookup will yield a hundredfold performance improvement. The introduction of read concern snapshot and the optional atClusterTime parameter enables your applications to execute complex analytical queries against a globally and transactionally consistent snapshot of your live, operational data. Even as data changes beneath you, MongoDB will preserve point-in-time consistency of the query results returned to your users. These point-in-time analytical queries can span multiple shards with large distributed datasets. By routing these queries to secondaries, you can isolate analytical workloads from transactional queries with both served by the same cluster, avoiding slow, brittle, and expensive ETL to data warehouses. To learn more, visit our documentation . 4. More operators, less work Boost your productivity with a slate of new operators, which will enable you to push more work to the database — while spending less time writing code or manipulating data manually. These new MongoDB operators will automate key commands and long sequences of code, freeing up more developer time to focus on other tasks. For instance, you can easily discover important values in your data set with operators like $maxN , $minN , or $lastN . Additionally, you can use an operator like $sortArray to sort elements in an array directly in your aggregation pipelines. 5. More resilient operations From the beginning, MongoDB’s replica set design allows users to withstand and overcome outages. Initial sync is how a replica set member in MongoDB loads a full copy of data from an existing member — critical for catching up nodes that have fallen behind, or when adding new nodes to improve resilience, read scalability, or query latency. MongoDB 6.0 introduces initial sync via file copy, which is up to four times faster than existing, current methods. This feature is available with MongoDB Enterprise Server. In addition to the work on initial sync, MongoDB 6.0 introduces major improvements to sharding, the mechanism that enables horizontal scalability. The default chunk size for sharded collections is now 128 MB, meaning fewer chunk migrations and higher efficiency from both a networking perspective and in internal overhead at the query routing layer. A new configureCollectionBalancing command also allows the defragmentation of a collection in order to reduce the impact of the sharding balancer. 6. Additional data security and operational efficiency MongoDB 6.0 includes new features that eliminate the need to choose between secure data or efficient operations. Since its GA in 2019, client-side field-level encryption (CSFLE) has helped many organizations manage sensitive information with confidence, especially as they migrate more of their application estate into the public cloud. With MongoDB 6.0, CSFLE will include support for any KMIP-compliant key management provider. As a leading industry standard, KMIP streamlines storage, manipulation, and handling for cryptographic objects like encryption keys, certificates, and more. MongoDB’s support for auditing allows administrators to track system activity for deployments with multiple users, ensuring accountability for actions taken across the database. While it is important that auditors can inspect audit logs to assess activities, the content of an audit log has to be protected from unauthorized parties as it may contain sensitive information. MongoDB 6.0 allows administrators to compress and encrypt audit events before they are written to disk, leveraging their own KMIP-compliant key management system. Encryption of the logs will protect the events' confidentiality and integrity. If the logs propagate through any central log management systems or SIEM, they stay encrypted. Additionally, Queryable Encryption is now available in preview. Announced at MongoDB World 2022, this pioneering technology enables you to run expressive queries against encrypted data — only decoding the data when it is made available to the user. This ensures that data remains encrypted throughout its lifecycle, and that rich queries can be run efficiently without having to decrypt the data first. For a deep dive into the inner workings of Queryable Encryption, check out this feature story in Wired . 7. A smoother search experience and seamless data sync Alongside the 6.0 Major Release, MongoDB will also make ancillary features generally available and available in preview. The first is Atlas Search facets , which enable fast filtering and counting of results, so that users can easily narrow their searches and navigate to the data they need. Released in preview at MongoDB World 2022 , facets will now include support for sharded collections. Another important new addition is Cluster-to-Cluster Sync , which enables you to effortlessly migrate data to the cloud, spin up dev, test, or analytics environments, and support compliance requirements and audits. Cluster-to-Cluster Sync provides continuous, unidirectional data synchronization of two MongoDB clusters across any environment, be it hybrid, Atlas, on-premises, or edge. You’ll also be able to control and monitor the synchronization process in real time, starting, stopping, resuming, or even reversing the synchronization as needed. Ultimately, MongoDB 6.0’s new abilities are intended to facilitate development and operations, remove data silos, and eliminate the complexity that accompanies the unnecessary use of separate niche technologies. That means less custom work, troubleshooting, and confusing architectures — and more time brainstorming and building. MongoDB 6.0 is not an automatic upgrade unless you are using Atlas serverless instances. If you are not an Atlas user, download MongoDB 6.0 directly from the download center . If you are already an Atlas user with a dedicated cluster, take advantage of the latest, most advanced version of MongoDB. Here’s how to upgrade your clusters to MongoDB 6.0 .
The Developer Data Platform: Highlights from MongoDB World 2022 Keynotes
MongoDB World 2022 is the first in-person MongoDB conference in nearly three years, offering us an opportunity to announce new releases and outline the future of MongoDB. During three World keynotes on June 7, the company’s leaders discussed our vision for the company and our products — and how they form a developer data platform, a family of tools and services built around a common API to help developers reduce complexity, improve their experience, achieve operational excellence, and run deep analytics. The inspiration for this concept originated from the desire to empower developers to build and scale applications faster, thus transforming their organizations and businesses. As Dev Ittycheria has discovered over the course of his eight years as CEO, “No customer has complained about innovating too quickly.” “What they have complained about — and what they struggle with — is increasing their pace of innovation,” Ittycheria says. “Invariably, the thing that holds them back is their legacy, brittle, inflexible architecture and infrastructure.” Why developers? From the beginning, MongoDB was built by — and for — developers, a category that includes anyone who creates or works with applications, as well as those who lead them. “Every product we build, every feature we develop — is all geared towards developer productivity,” Ittycheria says. “The obvious question,” Ittycheria continues, “is how do you make developers insanely fast and productive?” Given that developers spend so much time troubleshooting data, the answer lay in removing the friction inherent to this process. That’s why MongoDB was built on the document model, which maps data to objects in code — transforming the way developers organized and interacted with data. We believed in the potential of the document model so strongly that we built our entire product family around it, streamlining the developer data experience and facilitating all data-related tasks and products, from search to analytics. Additionally, the world continues to digitize, a trend that was only accelerated by the COVID-19 pandemic and the ensuing lockdowns. “There will be 750 million new digital apps by 2025,” Ittycheria says, citing a study from analyst firm IDC. CTO Mark Porter agrees. “There will be more applications built over the next four years than were built in the first 40 years,” he says. “The pace of innovation is increasing, and that means developer productivity is essential.” To get ahead of these trends, Ittycheria says, MongoDB is doubling down on research and development — as well as empowering innovators to create, transform, and disrupt industries by unleashing the power of software and data. The struggles of a developer The root causes of many developer difficulties can be summed up in two parts: an obsolete, decades-old technology (the relational data model) and the complications that arise from its fundamental mismatch with modern applications. “Relational databases were not scalable,” Porter says, recalling his time as a developer. “No matter how hard I tried, we couldn’t make them available, and no matter what we did, we couldn’t make SQL and RMS easy to use.” In essence, the limitations of relational databases are becoming very clear, Ittycheria adds. “They’re too rigid, too inflexible, too cumbersome, and just don’t scale.” As a result, “there’s been a proliferation of niche databases — which are focused on some small point solution — to compensate.” In fact, these narrow, specialized products (such as key-value or in-memory databases) often add cost and complexity. Combining these disparate products into a single architecture can impede innovation by siloing data, fragmenting application infrastructure, and further confusing workflows. This also creates a training gap — slowing down developers as they spend valuable time learning the ins and outs of each product. A typical data architecture, with a number of specialized databases adding complexity. A better way to work with data “We obsess about helping you get from an idea to a global reality,” says Sahir Azam, MongoDB’s chief product officer. The result of that obsession is MongoDB Atlas, our developer data platform, which reflects that obsession in three key ways. First, MongoDB offers an elegant developer experience. By getting the data, plumbing, and complexity out of the way, MongoDB enables users to “focus on innovating and building the differentiation for their companies and ideas,” Azam says. As a result, developers no longer have to create or run unwieldy, bespoke architectures for each new product or application. Next, Atlas enables broad workload support, providing, in Azam’s words, “most, if not all, of the capabilities you need for demanding modern applications” — whether they’re operational, analytical, or transactional. This includes abilities like application search, data lake, and aggregation pipelines, to name a few. Lastly, Atlas is resilient, scalable, stable, and secure, “so you can take an idea from a single geography to serving customers worldwide,” Azam says. When combined with the ease of use and versatility of the document model, the Atlas product family presents a uniquely valuable proposition for many developers. In order to build the future, developers need a mission-critical foundation. “Applications have always needed a solid foundation — from silicon to chips,” Porter says. If “someone at the lower level misses a configuration file, someone at the lower level messes something up, and everything comes crashing down.” Ultimately, the strength of MongoDB is that it frees up the developer to play to their strengths — building new products and applications, and not wrangling existing components. By providing documents and a flexible schema, high availability and scalability, and seamless partner integration, MongoDB helps become the mission-critical foundation for developers to build upon. “Just a database isn’t enough,” Porter says. For you to succeed, “there’s an actual, existential need to have this foundation. And we call it our developer data platform.” How far we've come Today, MongoDB is the world’s most popular data platform for building modern applications, Ittycheria says. The numbers back up this statement, with over 265 million downloads of MongoDB’s Community Edition, upwards of 150,000 new Atlas registrations per month, and more downloads in the past twelve months than in the first 12 years of MongoDB’s existence. Further, MongoDB has greatly expanded its global reach. From a humble beginning of four regions in AWS, MongoDB Atlas now runs in 95+ regions worldwide in AWS, Google Cloud, and Azure. MongoDB has also partnered with other cloud providers around the world. MongoDB’s core mission remains the same, even as our user base has expanded to 35,000+ customers across every industry and use case, as well as 100+ nations. MongoDB continues to simplify the developer experience, streamline the release process, speed up innovation, and help organizations ship faster. “Every week, we see new ideas spring up across the globe,” Azam says, many of which are powered by MongoDB. These organizations, which range from small startups to large corporations, include a digital-only challenger bank in Vietnam, a startup providing simulation training for Norwegian healthcare professionals, and a nonprofit that deals with surplus food from restaurants across Mexico. A serverless, mission-critical foundation MongoDB’s goal is to make Atlas the data platform for developers, empowering them to build the applications of the future. To achieve this objective, MongoDB is going serverless. “Modern development, in many ways, has been a constant search for higher levels of abstraction,” Azam points out, which removes complexity, and enables developers to move faster, differentiate, and pivot as needed. By going serverless, Atlas will minimize operational overhead down to almost zero, shifting the burden of servers, data centers, and provisioning away from developers. Further, Azam points out that many existing serverless databases “pose some significant limitations.” For instance, one popular type of serverless database is the key-value store, an ultra-simple database that cannot sustain complex workloads — and forces developers to add more databases in order to support additional application functionality. Instead, Atlas serverless combines all the best characteristics of serverless with the complete MongoDB experience — including the versatility of the rich document model, transactional guarantees, rich aggregations, and much more. This way, “we can support the full breadth of use cases you’re used to building on our platform,” Azam says. Unlike other serverless products, Atlas serverless instances also offer a competitive pricing model. Currently, “most serverless databases force a hard trade-off” when it comes to scaling, Azam says, requiring users to either deal with cold start delays when ramping up their serverless databases from zero, or pay extra (and pre-provision capacity) in order to scale quickly up from zero. In contrast, Atlas serverless enables users to “scale down to minimal usage and instantly scale up as your application needs — without any pre-committed capacity,” Azam says. Coupled with competitive pricing, flexibility for development and deployment, and instant scaling, Atlas serverless instances bring all of the advantages of serverless — without any of the downsides. What MongoDB can do for developers In essence, MongoDB will enable users to do their best work in four key ways. Reduce complexity Complicated application architectures, alongside an abundance of point solutions, force developers to spend more time and effort on operational “plumbing,” distracting them from their core mission of transformation through innovation. Using the MongoDB Atlas developer data platform, developers can, in Azam’s words, “remove complexity and the need for more niche databases in your architecture.”These features include MongoDB Atlas Search, for a purpose-built search solution, and Atlas Device Sync, for ensuring data consistency between edge, cloud, and backend.” Read our blog on reducing complexity to learn more . Provide a better developer experience “If you remove the friction from working with data,” Ittycheria says, “you make developers insanely productive.” An elegant developer experience “makes lives so much easier.” This is achieved through superior tooling and integration between MongoDB features, such as Atlas serverless instances, which abstract away considerations like provisioning and scaling, or the Atlas CLI, which packs the power and functionality of a GUI into the simplicity of a command line. Read our blog on the developer experience gap to learn more . Application analytics As businesses continue to digitize, their need to collect information for real-time analytics has only grown. To address this need, Atlas has added real-time application analytics abilities into its unified platform, Azam says. This means supporting analytical queries (and not just transactions), as well as making this data easily available for deep analysis and strategic decision making. This category includes Atlas Charts for rich data visualizations, and the Atlas SQL Interface for both connecting third party SQL-based analytics tools to Atlas. Read our blog on new analytics features to learn more . Operational excellence “We do this all with a strong foundation of resiliency, security, and scale,” Azam says. This means automating core operational processes to deploy and run global data infrastructure, plus simplifying complex procedures such as data secrecy, migrations, and cross-environment sync. Related features include the Atlas Operator for Kubernetes, which allows developers to deploy, scale, and manage Atlas clusters using Kubernetes, or our pioneering Queryable Encryption, a cryptographically secure, operationally efficient solution for working with sensitive data. Read our blog on new features to improve security and operations to learn more . Building the future — with MongoDB “But we’re not done yet — and neither are you,” Ittycheria says. “Tomorrow, we will help support newer and more inspiring applications. Just imagine what we’ll do tomorrow.” “We have 150,000 new ideas coming in every month,” Azam says. “I challenge you to think about how to transform your organization — how to take your next big idea to a global reality.” “What I’d like to challenge you to do is to grab your share of those 765 million apps,” Porter says. “Think about how you can change the world — and hopefully do it on our platform.... I am sure that the future is going to be built by you.”
Streamline, Simplify, Accelerate: New MongoDB Features Reduce Complexity
At MongoDB World 2022 , we announced several developer-centric features that provide more powerful analytics, streamline operations, and reduce complexity. In this post, we look at MongoDB Atlas Data Federation , MongoDB Atlas Search , MongoDB Atlas Device Sync and its Flexible Sync, and change streams. As consumer expectations of the applications they use grow, developers must continue to create richer experiences. To do that, many are adding a variety of data systems and components to their architectures, including single-purpose NoSQL datastores, dedicated search engines, and analytics systems. Piecing these disparate systems together adds complexity to workflows, schedules, and processes, however. For instance, one application could utilize a solution for database management, another solution for search functionality, and a third solution for mobile data sync. Even within an organization, teams often use different products to perform the same tasks, such as data analysis. This way of building modern applications often causes significant problems, such as data silos and overly complex architectures. Additionally, developers are forced to spend extra time and effort to learn how each of these components functions, to ensure they work together, and to maintain them over the long term. It should not be the developer’s job to rationalize all these different technologies in order to build rich application experiences. The developer data platform For developers and their teams, cobbling together a data infrastructure from disparate components is inefficient and time-consuming. Providers have little incentive to ensure that their solutions can function alongside the products of their competitors. Further, internal documentation, which is key to demystifying the custom code and shortcuts in a bespoke architecture, might not be available or current, and organizational knowledge gets lost over time. MongoDB Atlas, our developer data platform , was built to solve these issues. An ecosystem of intuitive, interlinked services, Atlas includes a full array of built-in data tools, all centered around the MongoDB Atlas database. Features are native to MongoDB, work with a common API, are designed for compatibility, and are intended to support any number of use cases or workloads, from transactional to operational, analytics to search, and anything in between. Equally important, Atlas removes the hidden, manual work of running a sprawling architecture, from scaling infrastructure to building integrations between two or more products. With these rote tasks automated or cleared away, developers are free to focus on what they do best: build, iterate, and release new products. MongoDB Atlas Data Federation MongoDB Atlas Data Federation allows you to write a single query to work with data across multiple sources, such as your Amazon S3, Atlas Data Lake , and MongoDB Atlas clusters. Atlas Data Federation is not a separate repository of data, but a service to combine, enrich, and transform data across multiple sources, regardless of origin, and output to your preferred location. With Atlas Data Federation, developers who want to aggregate data or federate queries do not need to use complex data pipelines or time-consuming transformations — a key advantage for those seeking to build real-time app features. Atlas Data Federation also makes it easier to quickly convert MongoDB data into columnar file formats, such as Parquet or CSV, so you can facilitate ingestion and processing by downstream teams that are using a variety of different analytics tools. MongoDB Atlas Search Rich, responsive search functionality has become table stakes for both consumer-facing and internal applications. But building high-quality search experiences isn’t always easy. Developers who use a third-party, bolt-on search engine to build search experiences have to deal with problems like the need to sync data between multiple systems; more operational overhead for scaling, securing, and provisioning; and using different query interfaces for database and search. Built on the industry-leading Apache Lucene search library, MongoDB Atlas Search is the easiest way to build rich, fast, and relevant search directly into your applications. It compresses three systems — database, search engine, and sync mechanism — into one, so developers don’t have to deal with the problems that bolt-on search engines introduce. It can be enabled with a few API calls or clicks and uses the same query language as the rest of the MongoDB product family. Atlas Search provides all of the features developers need for rich, personalized search experiences to users, like facets , now generally available, which offers users a way to quickly filter and navigate search results. With facets, developers can index data to map fields to categories like brand, size, or cost, and update query results based on relevance. This allows users to easily define multiple search criteria and see results updated in near real-time. MongoDB Atlas Device Sync With apps such as TikTok, Instagram, and Spotify, mobile users have come to expect features such as real-time updates, reactive UIs, and an always-on, always-available experience. While the user experience is effortless, building these abilities into a mobile app is anything but. Such features require lots of time and resources to develop, test, debug, and maintain. MongoDB Atlas Device Sync is designed to help developers address mobile app data challenges, including limited connectivity, dead zones, and multiple collaborators (all with varying internet speeds and access) by gathering, syncing, and resolving any sync conflicts between the mobile database and MongoDB Atlas — without the burden of learning, deploying, and managing separate data technologies. At World 2022, MongoDB announced Flexible Sync, a new way to sync data between devices and the cloud. Using Flexible Sync, developers can now define synced data using language-native queries and fine-grained permissioning, resulting in a faster, more seamless way of working — and one analogous to the way developers code and build. Previously, developers had to sync full partitions of data; Flexible Sync enables synchronization of only the data that’s relevant. With support for filter logic, asymmetric sync, and hierarchical permissioning, Flexible Sync can reduce the amount of required code by 20% or more, and speed up build times from months to weeks. Change Streams Data changes quickly, and your applications need to react just as quickly. When a customer’s order is shipped, for instance, they expect an in-app or email notification — and they expect it immediately. Yet building applications that can respond to events in real time is difficult and often requires the use of polling infrastructure or third-party tools, both of which add to developer overhead. Latency and long reaction times result in data that is outdated, and poor experiences for users of that data. Like Atlas’s Database Triggers, change streams enable developers to build event-driven applications and features that react to data changes as they happen. Along with reducing the complexity and cost of building this infrastructure from scratch, the new change stream enhancements (available in MongoDB 6.0) will enable you to determine the state of your database before and after an event occurs, so you can act on the changes and build business logic, analytics, and policies around it. That opens up new use cases, such as retrieving a copy of a document immediately after it is updated. All of these updates and new capabilities focus on the critical need to eliminate complexity in order to build, deploy, and secure modern applications in any environment. Together, MongoDB helps solve what MongoDB president and CEO Dev Ittycheria called a key developer challenge in his MongoDB World 2022 keynote: reducing the friction and cost of working with data. Learn more about MongoDB World 2022 announcements at mongodb.com/new and in these stories: 5 New Analytics Features to Accelerate Insights and Automate Decision-Making 4 New MongoDB Features to Improve Security and Operations Closing the Developer Experience Gap: MongoDB World Announcements
The Insider's Guide to MongoDB World 2022
Join us from June 7 to June 9 at MongoDB World 2022, which will be held at New York City’s Javits Center. Enjoy three packed days of keynotes, workshops, talks, technical panels, networking, community building, and more. Whether you’re eager to reconnect with your peers in person or are slightly overwhelmed by the choice of sessions and activities, you’ll find everything you need to know in this post. We will highlight special events at MongoDB World, preview what to expect and how to prepare, and provide tips on getting the most out of the conference. Plan your itinerary Space for workshops, talks, and other sessions are limited, so make sure to check out the World 2022 agenda and sign up for the activities that interest you. “Take time to create a list — and budget time between sessions,” advises Ben Flast, a MongoDB product management lead and featured World speaker . “There’s a lot going on, so have a plan to make sure to see the sessions that are most important to you.” Pick your learning path Whether they’re conference tracks, Chalk Talks, or keynotes, each event has a different audience, purpose, and skill level. The must-see keynotes from MongoDB CEO Dev Ittycheria, CTO Mark Porter, and chief product officer Sahir Azam will showcase announcements and new releases — and explain how they fit into the MongoDB ecosystem. Additionally, we are excited to announce that renowned technologist Ray Kurzweil has been confirmed as a keynote speaker. A distinguished thinker, inventor, and leader, Kurzweil has transformed multiple areas of technology, pioneering industry-leading products such as flatbed scanners, the first text-to-speech synthesizer, and much more. Don’t miss this exciting speech from a legend of the tech industry. Talks and workshops are divided into eight tracks, each of which includes a variety of sessions. The tracks include: Partner talks; the MongoDB Application Data Platform; Community Cafe; Governance, Compliance, and Security; Industry and Solutions Data Architecture; Modern Application Development; Make It Matter; Schema Design and Modeling; and the keynote speeches. Make It Matter, a track on inclusion, diversity, equity, and accessibility (IDEA), will be held in our dedicated IDEA Lounge. “People learn in all sorts of ways,” explains Karen Huaulme, a principal developer advocate at MongoDB. “That’s why we have hour-long sessions, 15-minute lightning talks, and everything in between. Feel free to mix and match so that you can learn in a way that works for you.” See the entire World 2022 agenda and mix and match your sessions For instance, Jumpstarts are high-level tutorials that introduce newcomers to basic (but important) MongoDB skills and best practices. This year, we’re running Jumpstarts on data and schema modeling, MongoDB Atlas, and Atlas Search, all of which will be moderated by seasoned MongoDB product managers and users. In contrast, Chalk Talks are highly interactive, small-group sessions for everyone from beginners to experts. Chalk Talks tend to be short (around 30 minutes), with plenty of audience participation, whiteboarding, and free-flowing discussion. For something more immersive, try a workshop — the long meal to the Chalk Talk’s snack break. Held only on Day 3, workshops are deep dives into highly technical topics. The first two hours will set the tone with onboarding, configuration, and lectures, and the second half will center on relevant real-world scenarios and attendee needs. If you want to practice using a specific technology and figure out how to make it work in your environment, sign up for a workshop . If you’re curious about the big picture, attend a Product Announcement or a Product Vision talk. Announcements will cover individual releases, how to use them, and how they fit into the MongoDB product family. Vision talks will marry new and existing products in order to explore different themes and workflows. Examples include " Serverless: The Future of Application Development " and " Going Real-Time With MongoDB Atlas ." More information can be found on the MongoDB World Agenda , which is updated regularly. Come prepared Speakers and facilitators will be in touch in advance to share all the necessary prerequisites, whether it’s downloadable modules, syllabi, or any other required materials. “Preparation will depend on the specific event,” says Jesse Hall, a senior developer advocate at MongoDB and workshop presenter. “For example, my workshop takes a serverless approach — setting up MongoDB in JAMstack — so be sure to bring a laptop with a basic development environment (like Node.js or VSCode).” Don't miss the hallway track Let serendipity take the wheel as you mix and mingle with other attendees, speakers, customers, partners, and other industry leaders between sessions, at the Community Cafe, and elsewhere. “Don’t be afraid to explore different events or exchange ideas with new people,” Huaulme suggests. “That’s where the magic happens. Don’t be intimidated by the idea of chatting with speakers or presenters. They’re very approachable, down-to-earth, and happy to hear from you.” “Keep an open mind and an open ear, and definitely reach out to anyone wearing MongoDB swag,” says Flast. “They’re working on something interesting.” Meet MongoDB partners Get to know our partners and learn how they build the future with MongoDB. Many of our top partners will be presenting talks at MongoDB World on topics from building operational data stores to working with edge devices , and they’ll also be running booths at the Partner Promenade. These organizations include major cloud companies, along with leaders in streaming data, real-time analytics, and much more. Matt Asay, MongoDB’s vice president of Partner Marketing , encourages visitors to see how these partners help enterprises of all sizes build the future. For his part, Asay looks forward to moderating a panel with leaders from Vercel, Prisma, and Apollo GraphQL, and to learning more about how these cutting-edge companies build for — and with — developers. Try something new Check out events that are off the beaten track, like the Builders’ Fest and the Community Cafe. At both venues, you’ll be able to unleash your creativity and pick up new skills. Check out the unique workshops at the Builders’ Pods, relaxed areas with lots of comfortable chairs, tables, and monitors. In the past, participants have learned to pick locks, create ice sculptures, construct machine learning algorithms, and develop games, among other things. For Huaulme, the Builders’ Fest sessions are a personal favorite. “The last time around, I learned to pick locks, while others learned to jump rope,” she recalls. “Builders’ Fest is a great place to learn new, fun skills — not all of which are related to tech.” Builders’ Fest will also include competition alongside discovery and exploration. Head to the nearby stages, where you can choose from coding challenges (like Code Golf) and play popular video games such as MarioKart, Donkey Kong, and more. Test your skills — whether it’s your mastery of code or your fast reflexes — against your peers. Stop by the Community Cafe to recharge. Lounge with coffee, thumb through the products at the swag store, and take a break from the action. Don’t forget to check out the silk-screen booth, where you can customize T-shirt designs and watch as they’re printed before your eyes. Register today for MongoDB World, and use code MDBW22BLOG to save 25% off your tickets. We hope to see you in NYC from June 7 to June 9!
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