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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.
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!
Introducing: Atlas Operator for Kubernetes
The MongoDB Enterprise Operator serves to automate and manage MongoDB clusters on self-managed infrastructure. While this integration has provided complete control over self-managed MongoDB deployments from a single Kubernetes control plane, we’re taking it a step further by extending this functionality to our fully-managed database—MongoDB Atlas. We’re excited to introduce the trial version of the Atlas Operator for Kubernetes. The Atlas Operator will allow you to manage all your MongoDB Atlas clusters without ever having to leave Kubernetes. Keep your workflow as seamless and optimized as possible by managing the lifecycle of your cloud-native applications from where you want most. With the trial version of this Atlas Operator, you can provision and deploy fully-managed MongoDB Atlas clusters on the cloud provider of your choice through Kubernetes. This provider is especially important for those seeking to unlock the power of multi-cloud with unique tools and services native to AWS, Google Cloud, and Azure without any added complexity to the data management experience. With this new Atlas Operator, you get the best of all clouds with multi-cloud clusters on Atlas , coupled with the freedom to run your entire stack anywhere, all while managed in one central location. The “trial version” simply means it has all the core functionality to provision fully-managed Atlas clusters, but the bells and whistles are yet to come. In addition to encapsulating core Atlas functionality, it ensures Kubernetes Secrets are created for each database user which allows for easier management of sensitive data. The Atlas Operator also allows you to create IP Bindings so your applications can securely access clusters. If you’re interested in using the trial version of the Atlas Operator today, follow our quickstart guide below to get started! Quickstart Below you’ll find the steps to create your first cluster in Atlas using the Atlas Operator. Note that you need to have a running Kubernetes cluster before deploying the Atlas Operator. Register/Login to Atlas and create API Keys for your Organization. This information together with the Organization ID will be used to configure the Atlas Operator access to Atlas. Deploy the Atlas Operator kubectl apply -f \ https://raw.githubusercontent.com/mongodb/mongodb-atlas-kubernetes/main/deploy/all-in-one.yaml Create a Secret containing connection information from step one. This Secret will be used by the Atlas Operator to connect to Atlas: kubectl create secret generic mongodb-atlas-operator-api-key \ --from-literal="orgId=<the_atlas_organization_id>" \ --from-literal="publicApiKey=<the_atlas_api_public_key>" \ --from-literal="privateApiKey=<the_atlas_api_private_key>" \ -n mongodb-atlas-system Create AtlasProject Custom Resource: cat <<EOF | kubectl apply -f - apiVersion: atlas.mongodb.com/v1 kind: AtlasProject metadata: name: my-project spec: name: Test Atlas Operator Project projectIpAccessList: - ipAddress: "0.0.0.0/0" comment: "Allowing access to database from everywhere (only for Demo!)" EOF Create AtlasCluster Custom Resource cat <<EOF | kubectl apply -f - apiVersion: atlas.mongodb.com/v1 kind: AtlasCluster metadata: name: my-atlas-cluster spec: name: "Test-cluster" projectRef: name: my-project providerSettings: instanceSizeName: M10 providerName: AWS regionName: US_EAST_1 EOF (You'll have to wait until the cluster is ready - "status" field shows "ready:true":) kubectl get atlasclusters my-atlas-cluster -o=jsonpath='{.status.conditions[?(@.type=="Ready")].status}' True Create a Secret for the password that will be used to log into Atlas Cluster Database kubectl create secret generic the-user-password \ --from-literal="password=P@@sword%" Create AtlasDatabaseUser Custom Resource (references the password Secret) cat <<EOF | kubectl apply -f - apiVersion: atlas.mongodb.com/v1 kind: AtlasDatabaseUser metadata: name: my-database-user spec: roles: - roleName: "readWriteAnyDatabase" databaseName: "admin" projectRef: name: my-project username: theuser passwordSecretRef: name: the-user-password EOF Shortly the Secret will be created by the Atlas Operator containing the data necessary to connect to the Atlas Cluster. You can mount it into your application Pod and read the connection strings from the file or from the environment variable. kubectl get secrets/test-atlas-operator-project-test-cluster-theuser \ -o=jsonpath="{.data.connectionString.standardSrv}} | base64 -d mongodb+srv://theuser:P%40%40sword%25@test-cluster.peqtm.mongodb.net Stay Tuned for More Be on the lookout for updates in future blog posts! The trial version of the MongoDB Atlas Operator is currently available on multiple marketplaces, but we’ll be looking to make enhancements in the near future. For more information, check out our MongoDB Atlas & Kubernetes GitHub page and our documentation .
Global, Multi-Cloud Security at Scale with MongoDB Atlas
In October 2020, we announced the general availability of multi-cloud clusters on MongoDB Atlas . Since then, we’ve made several key improvements that allow customers to take advantage of the full breadth of MongoDB Atlas ’ best-in-class data security and privacy capabilities across clouds on a global scale. Cross-Cloud Security with MongoDB Atlas A common question we get from customers about multi-cloud clusters is how security works. Each cloud provider offers protocols and controls to ensure that data within its ecosystem is securely stored and accessed. But what happens when your data is distributed across different clouds? Don’t worry–we have you covered. MongoDB Atlas is designed to ensure that our built-in best practices are enforced regardless of which cloud providers you choose to use, from dedicated network peering connections to customer-managed keys for data encryption-at-rest and client-side field-level encryption. Private Networking to Multiple Clouds You can now create multiple network peering connections and/or private endpoints for a multi-cloud cluster to access data securely within each cloud provider. For example, say your operational workload runs on Azure, but you want to set up analytics nodes in Google Cloud and AWS so you can compare the performance of Datalab and SageMaker for machine learning. You can set up network peering connections for all three cloud providers in Atlas to allow each of your cloud environments to access cluster data in their respective nodes using private networks. For more details, take a look at our documentation on network peering architecture . Integrate with Cloud KMS for Additional Control Over Encryption Any data stored in Atlas can be encrypted with an external key from AWS KMS, Google Cloud KMS, or Azure Key Vault for an extra layer of encryption on top of MongoDB’s built-in encrypted storage engine . You can also configure client-side field level encryption (client-side FLE) with any of the three cloud key management services to further protect sensitive data by encrypting document fields before it even leaves your application ( support for Azure Key Vault and Google Cloud KMS is available in beta with select drivers ). This means data remains encrypted even while it is in memory and in-use within your live database. Even though the data is encrypted, it remains queryable by the application but is inaccessible to any administrators running the database or underlying cloud infrastructure for you. Beyond security, client-side FLE is also a great way to comply with right to erasure requests that are part of modern privacy regulations such as the GDPR or the CCPA. You simply destroy the user’s encryption key and their PII is unreadable and irrecoverable in memory, on disk, in logs, and in backups. For multi-cloud clusters, this means you can take advantage of multiple layers of encryption that use keys from different clouds. For example, you can have PII data encrypted client-side with AWS KMS keys, then stored in both an AWS and Google Cloud region on Atlas and further encrypted at rest with a key managed via Azure Key Vault. Global, Multi-Cloud Clusters on MongoDB Atlas For workloads that reach users across continents, our customers leverage Global Clusters . This gives you the unique ability to shard clusters across geographic zones and pin documents to a specific zone. Now that Atlas is multi-cloud, you can now choose from the nearly 80 available regions across all three providers, expanding the potential reach of your client applications while making it easy to comply with data residency regulations. Consider a sample scenario where you’re based in the US and want to expand to reach audiences in Europe. To comply with GPDR , you must store EU customer data within that region. With Global Clusters, you can configure a multi-cloud cluster with a US zone and an EU zone. In the US, you choose to run on AWS, but in Europe, you decide to go with Azure because it has more available regions. All of this can be configured in minutes using the Atlas UI: simply define your zones and ensure that your documents contain a location field that dictates which zone they should be stored in. For more details, follow our tutorial for how to configure a multi-cloud Global Cluster on Atlas . Future-Proof Your Applications with Multi-Cloud Clusters There are many reasons why companies are considering a multi-cloud strategy , from cross-cloud resiliency to geographical reach to being able to leverage the latest tools and services on the market. With MongoDB Atlas, you get best-in-class data security and operations and intuitive admin controls, regardless of how many cloud providers you want to use. To learn more about how to deploy a multi-cloud cluster on MongoDB Atlas, check out our step-by-step tutorial , which includes best practices for node distribution, instructions for how to test failing over to another cloud, and more. Safe Harbor The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.
MongoDB Launches Sales Academy
We’re thrilled to announce our inaugural MongoDB Sales Academy! This program will prepare emerging professionals with the training and experience they need to jumpstart a career in sales. We’re looking for recent college graduates with an interest in technology to join our rapidly growing sales team. “The creation of a program designed to develop recent college graduates into sales professionals is a natural extension of MongoDB’s culture of talent development. We have best in breed sales enablement and onboarding programs, and a “BDR to CRO” program focused on accelerating sales careers. We have an opportunity to bring these world-class training programs to those who are starting their careers, and to turn emerging professionals into future leaders at MongoDB.” - Meghan Gill, VP Sales Operations & SDR The Sales Academy will be a full time, paid 8-week training program based in Austin, TX. It will focus on training and developing future MongoDB Sales Development Representatives as, upon completion, these recent college graduates will move into a full time SDR position. Those who are part of the Sales Academy will have direct one-on-one support from their sales mentors, MongoDB’s leadership team, the Campus Team, and each other. These New Grads will complete a best-in-class training program, which includes both technical concepts and sales processes. Through regular coaching and professional development training, our Sales Academy New Grads will graduate from the program and become full-time members of the Sales team at MongoDB. “Life at MongoDB is ever-evolving and a great start for anyone looking to take their career to the next level. You can expect to constantly learn new things about technology and your customers, work alongside some of the best sales professionals in the industry, and to be on the forefront of innovation. If you want to understand technology like never before, work with customers modernizing today’s world, and get consistent feedback from peers and leadership, this is the right place for you.” - Maya Monico, SDR Manager This isn’t the first time that MongoDB has hired students into our sales organization. Hannah Branfman was part of our SDR Internship program and, upon graduating from her school, joined us full-time. When asked about what sales at MongoDB is like, Hannah says: “If you have ambition, are coachable and have a strong desire to learn, MongoDB will be a great fit for you. You have to be willing to make mistakes and remain naturally curious — don’t stop asking questions! If you have the perseverance to not only get here, but to then set the bar high for yourself and surpass it, you will fit in great. Get ready to make an impact!” - Hannah Branfman, SDR We’re eager to find recent college graduates who are ambitious and excited to learn. If you’re interested in kickstarting your sales career at MongoDB in our Austin office, this could be the perfect fit for you! The job post is now up and we look forward to reviewing your application and getting to know you!
MongoDB Realm Sync is GA
Every mobile developer wants to build an app that users will love - meaning you want to build apps that work for users regardless of signal strength, that react to changes in data in real time, and that won’t drain your user’s battery life or use excessive amounts of data. In June, we released MongoDB Realm , a set of integrated application development services that makes it possible for anyone to build a great app - whether you’re a solo developer working to stand up your idea, or part of a larger team shipping your latest release. As part of this, we announced a public beta for MongoDB Realm Sync , which makes it easier for you to keep data in sync across users, devices, and your back end, even when devices aren’t always online. We’re excited to share that as of today, Realm Sync is now Generally Available (GA). We believe Realm Sync offers a best-in-class solution for offline-first app developers, who need to move data between a local client and the cloud. With the Realm Sync service, we’ve significantly reduced the code you need to write, while also reducing the complexity of your app architecture. Crucially, we’ve done it while making sure everything is built to optimize for battery power, CPU, and bandwidth. As a developer, you no longer need to write (or maintain) thousands of lines of complex conflict resolution and networking code. Realm Sync handles that for you, making it simple to move data between the local Realm Mobile Database and MongoDB Atlas on the back end. You can build features faster, reduce bugs, deliver a better user experience – and do it all without having to worry about standing up or scaling servers. Download the MongoDB Realm Whitepaper Building for an Offline-First Environment To many development teams, synchronizing data between the client and your back end sounds simple. But when connectivity isn’t guaranteed, it becomes time-consuming and complex to achieve. MongoDB Realm simplifies data sync. Synchronization works bi-directionally, moving data between the Realm Mobile Database on the client-side and MongoDB Atlas on the back end. Automatic conflict resolution resolves any data conflicts that may emerge across multiple devices, users, and your back end, and ensures data is consistent whenever mobile devices come online. Because data is synced to Atlas, applications can easily scale up or down infrastructure as app usage changes. MongoDB Realm Sync also: Speeds feature innovation. Realm’s Mobile Database - used to store data locally on device, and MongoDB Realm Sync - both reduce the code developers need to write, and free up time to focus on building new features that provide unique business value. Works across platforms. Realm Mobile Database and MongoDB Realm Sync work on any platform, for any mobile device. Is secure and stable. MongoDB Realm lets you encrypt data in-flight or at-rest, both in the cloud and on-device. MongoDB Realm Sync in Action Fortune 500 businesses and cutting-edge start-ups are already using the Realm Mobile Database and MongoDB Realm Sync to build their apps today. Srikanth Gandra, Director of Digital Technology for 7-Eleven, built a mobile app on MongoDB Realm that’s been successfully rolled out for use across the United States and Canada. “What we’ve created is really innovative. Since rolling this out to all 8,500 stores in North America, we’ve been able to sync data across more than 20,000 devices on a nearly real-time basis," he said. "[Managers] can start using devices immediately, rather than waiting 2-3 minutes to download the data on initial startup, like they used to. Data accuracy - especially around inventory when sales happen or shipments arrive - has really improved.” “We’re evaluating using Realm and Realm Sync to assist with inbound and outbound parcel shipping use cases,” said James Fairweather, Chief Innovation Officer, Pitney Bowes. “As an example, we are exploring building an app on Realm for our front-line workers to scan a package that would automatically sync the data back to MongoDB Atlas providing consistent reporting and up-to-date logistics throughout the shipping journey.” With MongoDB Realm Sync, mobile developers have the tools to make data sync simple, making sure they both build apps fast, while still making sure that even complex components like real-time data sync are built right. Try MongoDB Realm Sync, and get started building your offline-first app. Try MongoDB Realm Sync Today
Learn How to Scale Your Startup with this Educational Series
As a startup, you're probably interested in spending more time on shipping features than managing your database. Upskill in the new year with Scaling Your Startup with MongoDB Atlas: A Series . Join us for a three-part educational monthly webinar series kicking off January 26, completely focused on how to do less with more. When? The last Tuesday of every month, January through March, at 9 am PT/noon ET What? Our technical experts will take you through NoSQL, the MongoDB Query Language, MongoDB Atlas (our global cloud database service), and show you how to take the heavy lifting out of database management. View Scaling Your Startup with MongoDB Altas: A Series Why NoSQL for Startups | Lauren Schaefer | January 26 Scale Your Startup with MongoDB Atlas | Mike Lynn | February 23 Reaching Scalability with MongoDB Atlas | Adrienne Tacke | March 30 Where? Zoom How much? Free! Who should attend? Attend if you are a MongoDB beginner, in need of a technical refresher, building a product or service, or part of a startup. Will it be recorded? Of course. Each webinar will be recorded and posted to the website for you to watch anytime, at your convenience. Why? Just check out the takeaways below. Plus at the end of each presentation, you’ll have time to submit queries for Q&A with the technical expert. Key takeaways will include: Mapping terms & concepts from tables to documents Discovering the 4 major advantages of documents for your startup Changing your mindset in 3 key ways Deploying a free tier cluster Adding users & managing user access Connecting to MongoDB Compass, the GUI for MongoDB What's Realm? Third-party integration What is and why Multi-Cloud? How to set-up Multi-Cloud Cluster And much more! Save Your Spot for Scaling Your Startup with MongoDB Atlas: A Series
MongoDB Atlas Online Archive for Data Tiering is now GA
We’re thrilled to announce that MongoDB Atlas Online Archive is now Generally Available. Online Archive allows you to seamlessly tier your data across Atlas clusters and fully managed cloud object stores, while retaining the ability to query it through a single endpoint. Reduce storage costs. Set the perfect price to performance ratio on your data. Automate data tiering. Eliminate the need to manually migrate or delete valuable data. Queryable archives. Easily federate queries across live and archival data using a unified connection string. With Online Archive, you can bring new use cases to MongoDB Atlas that were previously cost-prohibitive such as high volume time-series workloads, data archival for auditing purposes, historical log keeping and more. Manage your entire data lifecycle on MongoDB Atlas without replicating or migrating it across multiple systems. What is Atlas Online Archive? Online Archive is a fully managed data tiering solution that allows you to tier data across your "hot" database storage layer and "colder" cloud object storage to maintain queryability while optimizing on cost and performance. Online Archive is a good fit for many different use cases, including: Insert heavy workloads, where data is immutable and has lower performance requirements as it ages Historical log keeping and time-series datasets Storing valuable data that would have otherwise been deleted using TTL indexes We’ve received amazing feedback from the community over the past few months while the feature was in beta and we’re now confident in supporting your production workloads. Our users have put the feature through a variety of use cases in production and development workloads which has enabled us to make a wide range of improvements. Online Archive gives me the flexibility to store all of my data without incurring high costs, and feel safe that I won't lose it. It's the perfect solution. Ran Landau, CTO, Splitit Autonomous Archival Management It's easy to get started with Online Archive and it requires no ongoing maintenance once it’s been set up. In order to activate the feature, you can follow these simple steps: Navigate to the “Online Archive” tab on your cluster card and begin the setup flow. Set an archiving rule by selecting a date field, with dot-notation if it’s nested, or creating a custom filter. Choose commonly queried fields that you want your archival queries to be optimized for, with a few things in mind: Your data will always be “partitioned” by the date field in your archive, but can be partitioned by up to two additional fields as well. The fields that you most commonly query should be towards the top of the list (date can be moved to the top or bottom). Query fields should be chosen carefully as they cannot be changed after the fact and will have a large impact on query performance. Avoid choosing a field that has unique values as it will have negative performance impacts for queries that need to scan lots of data. And you’re done! MongoDB Atlas will automatically move data off of your cluster and into a more cost-effective storage layer that can still be queried with a single connection string that combines cluster and archive data, powered by Atlas Data Lake . What's Next? Along with announcing Online Archive as Generally Available, we’re excited to share a few additional product enhancements which should be available in the coming months: Private Link support when querying your archive Incremental deletes of data from your archive Support for BYO key encryption on your archival data Improved performance and stability Try Atlas Online Archive Online Archive allows you to right-size your Atlas clusters by storing hot data that is regularly accessed in live storage and moving colder data to a cheaper storage tier. Billing for this feature will include the cost to store data in our fully managed cloud object storage and usage based pricing for querying archive data. We can’t wait to see what new workloads you’ll bring onto MongoDB Atlas with the new flexibility provided by Online Archive! To get started, sign up for an Atlas account and deploy any dedicated cluster (M10 or higher). Have questions? Check out the documentation or head over to our community forums to get answers from fellow developers. And if we’re missing a feature you’d like to see, please let us know ! Safe Harbor Statement The development, release, and timing of any features or functionality described for MongoDB products remains at MongoDB's sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality. Except as required by law, we undertake no obligation to update any forward-looking statements to reflect events or circumstances after the date of such statements.
Re-Imagining What A Cloud-Native Database Can Be
COVID-19 has compelled companies of all sizes and industries to reinvent themselves. From the way they work to the way they interact with customers, the pandemic has forced an urgent shift to a digital-by-default customer experience and, as a result, has accelerated the move to the cloud. But as companies make the move, many are finding that the same data silos and operational complexity that thwarted innovation for decades is simply following them into the cloud. Developers responsible for building today’s apps have to work with a patchwork of technologies, data models, APIs, and languages across disparate systems to deliver the right data at the right time to power critical applications and services. To better serve these developers, we’ve expanded our capabilities outside of the core database into a robust data platform we call MongoDB Cloud . At its core is MongoDB Atlas, our fully managed global cloud database, which enables your developer teams to spend less time on undifferentiated work and more time writing code that adds business value. By adding capabilities such as Atlas Search , Atlas Data Lake , MongoDB Charts and MongoDB Realm , which provide a consistent experience for working with data in different ways, you’re drastically reducing the cognitive burden on development teams. Simply put, MongoDB Cloud allows you to easily deploy, manage, and scale data architectures designed to support the converging requirements of transactional and analytical systems within a single elegant platform. Any cloud, anywhere, anytime The pandemic has put a spotlight on resilience and agility and showcased the importance a data platform can have for your business. This has not only accelerated the migration to the public cloud, but also the move to multi-cloud environments. Many of our customers rely on more than one cloud provider, and 55 percent of organizations currently report using multiple public clouds . That’s why we designed our offerings to have the same great developer experience regardless of which cloud provider or providers you use. Since launching in 2016, MongoDB Atlas has always pushed the boundaries of what’s possible in cloud data management, with customers able to deploy their data from more than 75 regions worldwide across AWS, Azure, and Google Cloud. And with the recent launch of multi-cloud clusters on MongoDB Atlas, we’ve recast the cloud and development experience again. With this first-of-a-kind capability, companies gain the ability to distribute their data in a single cluster across multiple public clouds simultaneously, or move workloads seamlessly between them. This is true data portability, enabling the freedom and flexibility to use best-of-breed services across multiple platforms, and ensuring cross-cloud resiliency. But we’re not standing still. Introducing Online Archive on MongoDB Atlas Today, we are announcing more innovations that unleash the full potential of business data, beginning with the general availability (GA) of Online Archive for MongoDB Atlas. With Online Archive, you can seamlessly tier your data across fully managed databases and cloud object storage, all while retaining the ability to query it through a single endpoint. Users can create a rule to automatically archive infrequently accessed data in their MongoDB Atlas clusters onto their object store, eliminating operational complexity and transactional data storage costs. All users on dedicated clusters (M10+) can use Online Archive regardless of which cloud provider they are using to run Atlas. If you want to learn more or see if Online Archive could help your organization, watch our deep-dive technical session , which is available on demand and features a live Q&A on Wednesday, Dec. 2 from 3:30-4pm ET. Online Archive gives me the flexibility to store all of my data without inucrring high costs, and feel safe that I won't lose it all. It's the perfect solution. Ran Landau, CTO, Splitit The power of choice Empowered developer teams around the world turn to MongoDB Atlas on their preferred cloud to deliver mission-critical services to their businesses faster. Here are a few customer success stories that are near and dear to our hearts: Ludo King : The small but mighty team of developers behind India’s favorite mobile game, Ludo King, turned to MongoDB Atlas and MongoDB Realm on AWS. The results? They’ve been able to keep building new, revenue-generating features for the game’s half a billion players, while efficiently managing near instantaneous 1000% growth. Toyota Materials Handling Europe : While building the connected warehouses of the future, Toyota Material Handling needed a database as flexible and powerful as MongoDB Atlas, running on Azure, to break down their monolith and transition to a microservices architecture. Boxed : Grocery delivery wholesaler Boxed built its platform on MongoDB Atlas on Google Cloud to accommodate the soaring demand for goods and services due to the pandemic. As brick-and-mortar retailers struggled to keep up with demand, Boxed saw a 30x spike in demand, which they were able to handle because of MongoDB’s powerful data platform. Get started with MongoDB Atlas today. And make sure you take advantage of all the opportunities to explore MongoDB at AWS re:Invent 2020 .