4 Ways MongoDB Enhances Your Google BigQuery Experience
MongoDB and Google Cloud continue to build on their partnership, with MongoDB enhancing Google Cloud with pay-as-you-go abilities, unified billing, and integrations with multiple different GC features, including BigQuery . And, when it comes to data architecture, BigQuery and MongoDB are two products that are better together. Google BigQuery and MongoDB are better together Google’s serverless data warehouse, BigQuery, was launched in 2011 with an aim to enhance business agility as their cloud-native data warehouse. BigQuery allows for fast queries that can uncover insights using familiar SQL. When MongoDB is added to the database technology stack as a complementary technology, it enhances the breadth of capabilities for the developer across a variety of use cases, including the following four examples. Combined impact of the Enterprise Data Warehouse and the Operational Data Store BigQuery is best suited as an Enterprise Data Warehouse (EDW), meaning it is designed to optimize long-running analytics. MongoDB Atlas , on the other hand, is best suited as an Operational Data Store (ODS), designed to optimally support high throughput and highly concurrent real-time operational applications that demand random access to an entity’s data in native JSON. This combination means that BigQuery and MongoDB are complementary technologies that can jointly deliver more value — each delivering on their strongest qualities. BigQuery excels at long-running queries, while Atlas handles the real-time operational application needs with thousands of concurrent sessions and millisecond response times. Enriched end-customer experiences BigQuery enables data scientists and analysts with machine learning (ML) models and BI tools for structured and semi-structured data at scale. For roles that need results with a turnaround time of a day or more, BigQuery is a strong tool for big data queries. With MongoDB Atlas, engineers and development teams can build applications faster and handle highly diverse schema, query, and update patterns, adapting to demanding user needs and competition. Atlas can also deliver the real-time or less than 24-hour queries that are necessary to keep your business operational. Additionally, data can easily move back and forth between the two platforms, creating a prime combination for running analytics on operational data. Being able to unlock the full potential of your data across your organization means that everyone has the insight into the business metrics they need, when they need it. This allows quicker decision making, as well as stronger and more accurate reporting. Extensibility to MongoDB Atlas features On top of the value and synergy that can be realized by a BigQuery+Atlas combination, other Atlas features can help enhance the usefulness and sophistication of a data architecture, such as: Atlas Charts can be leveraged to create rich visualizations of any data stored within Atlas. Atlas Triggers and Alerts can apply database logic in response to events or on a predefined schedule. Atlas Search brings full-text search at scale to all data across MongoDB and BigQuery alike. Atlas Data Federation enables aggregating data across multiple data sources, such as Atlas clusters and HTTPS endpoints, and transforming it into analytical formats (e.g., Parquet). This means you can not only access data in real-time, but you can also analyze it in a visual, user-friendly way. This functionality makes your data more actionable, allowing you not only to answer questions about your business data but also make better predictions and future adjustments based on it. Furthermore, being alerted to certain data-based events and triggering new actions based on that information means you can have your data working more efficiently for you, freeing up time to innovate and focus on core business competencies. Lastly, this approach simplifies your data lifecycle, so JSON data from various applications and endpoints can easily be transformed and consumed for rich analytics. Deeper understanding of your customer Businesses can use fully managed MongoDB Atlas to store customer 360 profiles. A 360-degree view of a customer allows businesses to track an individual customer’s journey across multiple channels, devices, purchases, and interactions, and improves customer satisfaction. With the combination of Atlas and BigQuery, businesses can also use compiled data — such as, transactional data, behavioral data, user profile and segmentations, and business analytics — to match user profiles with products and services using Artificial Intelligence (AI). Vertex AI , a managed machine learning platform, provides all the Google cloud services in one place to deploy and maintain AI models. Being able to easily access a 360 view for each customer and have automation around their customer journey helps with customer engagement and loyalty by improving customer satisfaction and retention through personalization and targeted marketing communications. It also enables retailers to aggregate customer interactions across all channels and identify valuable new customers. Google BigQuery and MongoDB Atlas in the real world Current , a leading U.S. challenger bank, uses innovative approaches, services, and technologies to serve people overlooked by traditional banks, regardless of age or income level, to help improve their financial outcomes. To help create customer experiences that cannot exist in traditional systems, Current chose to leverage Google Cloud, including BigQuery, with MongoDB layering the platform to achieve their goals. Read Full Current Story Are you a Google BiqQuery customer that is curious about how MongoDB Atlas can amplify your existing data warehouse or data lake architecture? Try MongoDB Atlas for free today and spin up your first workload in minutes. Try pay-as-you-go Atlas on GC Marketplace
Achieving Industrial Connectivity at Scale with Wimera and MongoDB
Industry 4.0 (I4.0) represents the beginning of the Fourth Industrial Revolution. It includes the current trend of automation technologies in the manufacturing industry as well as disruptive technologies and concepts, such as cyber-physical systems (CPS), Industrial Internet of Things (IIoT), cloud computing, and immersive visualization. Through Industry 4.0, embedded systems, semantic machine-to-machine communication, IIoT, and CPS technologies are integrating the virtual space with the physical world. These technologies are enabling a new generation of industrial systems, such as smart factories, to deal with the complexity of fast-paced and hyper-personalized production. In this article, we’ll explore Wimera’s unique solutions to the challenges of I4.0 and IIoT, built with MongoDB. Information and insights With IIoT, existing industrial systems will be modernized to drive digital transformation and unlock tomorrow's smart enterprise. IIoT has been finding its way into products and sensors while revolutionizing existing manufacturing systems; thus, it is considered a key enabler for the next generation of advanced manufacturing. Industry 4.0 generally comprises many complex components and has broad applications in all manufacturing sectors. The first challenge faced by manufacturing companies when embarking on the I4.0 journey is to sensorize and connect their manufacturing equipment in order to collect, store, and analyze data for information and insights. Wimera Systems is solving this challenge as an I4.0 enablement company offering IIoT solutions using their unique hardware, software application, and AI/ML-based analytics engine. Wimera’s Smart Factory Suite has seen tremendous growth, with 2500+ global installations across 50+ customers. MongoDB has been pivotal to that growth, acting as the core component of the IIoT suite and enabling the company to offer its services at scale without having to worry about managing the complexity of an IIoT database. Bringing AI-powered IIoT to the manufacturing shop floor Manufacturing companies are emerging from the pandemic with a renewed focus on digital transformation and smart factories investment. COVID-19 has heightened the need for IIoT technology and innovation, forcing manufacturers to compete in a digitalized business environment. Many manufacturers still operate using legacy technologies and systems; on most shop floors, equipment and operator efficiency are manually calculated and tracked using spreadsheets. The machines are maintained using time-based rather than condition-based maintenance strategies. And, no real-time visibility exists on consumables and tools usage. All these practices result in increased maintenance costs, suboptimal production, and ultimately, customer dissatisfaction. Wimera understands these challenges all too well, which is why they created the Smart Factory Suite supporting both on-premise and cloud deployments. The Smart Factory Suite provides insights for managing the entire production landscape through interconnected devices and machines, operations, and facilities. It can predict and make real-time adjustments for increased production efficiency and less downtime. The suite is primarily utilized for empowering manufacturing operations, equipment maintenance, warehouse operations, and inventory management. With Smart Factory Suite, Wimera serves a wide range of manufacturing industry sectors including, but not limited to, automotive, electronics, chemical, and food processing companies. Deploy and run anywhere with MongoDB MongoDB, with its freedom to run anywhere, lets Wimera offer both on-premises and cloud deployment options for its customers. In both cases, the suite is directly connected with machine controllers using Wimera libraries for all popular Programmable Logic Controller (PLC) brands. The suite is also connected to legacy machines through external sensors installed by the Wimera team. Data is extracted via the Wimera ReMON Data Acquisition (DAQ) device (Figure 1) that utilizes the MongoDB database as the persistent data storage. MongoDB’s flexible data model makes it easy to combine and enrich this data and enables live dashboards and instant alerts for factory personnel. The data collected and optimized by ReMON DAQ is further fed to ReMON AI , an advanced analytics engine. ReMON AI provides advanced analytics through AI/ML models and leverages MongoDB to deliver application-driven analytics in real time. Figure 1: ReMON DAQ and ReMON AI (source: Wimera ReMON ). Whether through on-premises or cloud deployment (Figures 2 and 3), Wimera’s customers have benefited from MongoDB’s capabilities that are critical for IIoT applications, such as time series collections and the flexible, intuitive document data model. Figure 2: Wimera IoT architecture on premises. Figure 3: Wimera IoT architecture on cloud (using MongoDB on AWS). In one customer example, while deploying IIoT at a multinational CNC machine shop, the customer preferred to use their existing production monitoring application enriched with IoT data coming from Wimera’s Smart Factory Suite. In this case, MongoDB enabled easy and seamless integration of the IoT application with the customer's application via a simple API. Additionally, high-speed data coming from a vibration sensor was handled effectively by MongoDB time series collections, resulting in real-time alerts sent to maintenance teams for instant corrective actions on the shop floor. In another example, a multinational automotive manufacturer wanted a single platform that could collect and combine data coming from vendors in different formats and contexts. MongoDB's flexible document model helped manage the varied data types easily, allowing the customer to benefit from a single application capable of managing multiple vendors in parallel. This flexibility offered by MongoDB enables the customer to keep adding new vendors instantly without changing the underlying cloud infrastructure or tweaking schemas. Interested readers can check out additional case studies on Wimera’s website. Building better together Wimera and MongoDB’s partnership gives customers confidence with validated architectures to ensure successful, optimized, and scalable deployments at their facilities. Wimera’s continued partnership with MongoDB also helps guide the company’s product roadmap as we expand in the IIoT, Smart Factory market together. MongoDB is the only enterprise grade database chosen by the Wimera development team due to easy handling of the large volume of data generated from machines and sensors while maintaining a high performance… If we want to insert thousands of records in a second, then MongoDB is the best choice for that given our solutions are for Industrial IoT. Also, horizontal scaling (adding new columns) is not an easy process in any RDBMS system. But in the case of MongoDB, it is very easy Nagarajan Narayanasamy, CEO, Wimera Systems Private Limited A bright future ahead Since 2019, Wimera has been an early adopter of MongoDB for their Industrial IoT application for discrete manufacturing industries and process industries on multiple domains. “Currently, Narayanasamy says, “Wimera’s Industrial IoT solutions are matured, and we are focused on scaling globally.” Wimera now targets expansion in India, APAC, EU, and USA for the discrete manufacturing and process industries and also for select OEMs and machine builders. “As MongoDB continues to scale itself globally through its multi-cloud data distribution strategy, we see a good synergy partnering with MongoDB for the mutual benefit of both companies and the community as a whole. We also would like to work with MongoDB on the technology roadmap and solve some of the real-life challenges faced by manufacturing industries,” Narayanasamy says. Wimera has recently started their MongoDB Atlas journey, and the adoption will grow as their customers demand more cloud solutions compared to current on-premises deployments. MongoDB will continue to help IoT companies like Wimera take their product offering to the next level and enable their customers to digitally transform their manufacturing operations. To learn more about MongoDB’s role in industrial connectivity and IIoT, please visit our Manufacturing and Industrial IoT page.
Choosing the Right Tool for the Job: Understanding the Analytics Spectrum
Data-driven organizations share a common desire to get more value out of the data they're generating. To maximize that value, many of them are asking the same or similar questions: How long does it take to get analytics and insights from our application data? What would be the business impact if we could make that process faster? What new experiences could we create by having analytics integrated directly within our customer-facing apps? How do our developers access the tools and APIs they need to build sophisticated analytics queries directly into their application code? How do we make sense of voluminous streams of time-series data? We believe the answer to these questions in today's digital economy is application-driven analytics. What is Application-Driven Analytics? Traditionally, there's been a separation at organizations between analytics that run the business and analytics that manage the business. They're built by different teams, they serve different audiences, and the data itself is replicated and stored in different systems. There are benefits to the traditional way of doing things and it's not going away. However, in today's digital economy, where the need to create competitive advantage and reduce costs and risk are paramount, organizations will continue to innovate upon the traditional model. Today, those needs manifest themselves in the demand for smarter applications that drive better customer experiences and surface insights to initiate intelligent actions automatically. This all happens within the flow of the application on live, operational data in real time. Alongside those applications, the business also wants faster insights so it can see what's happening, when it's happening. This is known as business visibility, and the goal of it is to increase efficiency by enabling faster decisions on fresher data. In-app analytics and real-time visibility are enabled by what we call application-driven analytics. Find out why the MongoDB Atlas developer data platform was recently named a Leader in Forrester Wave: Translytical Data Platforms, Q4 2022 You can find examples of application-driven analytics in multiple real-world industry use cases including: Hyper-personalization in retail Fraud prevention in financial services Preventative maintenance in manufacturing Single subscriber view in telecommunications Fitness tracking in healthcare A/B testing in gaming Where Application-Driven Analytics fits in the Analytics Ecosystem Application-driven analytics complements existing analytics processes where data is moved out of operational systems into centralized data warehouses and data lakes. In no way does it replace them. However, a broader spectrum of capabilities are now required to meet more demanding business requirements. Contrasting the two approaches, application-driven analytics is designed to continuously query data in your operational systems. The freshest data comes in from the application serving many concurrent users at very low latency. It involves working on much smaller subsets of data compared to centralized analytics systems. Application-driven analytics is typically working with hundreds to possibly a few thousand records at a time. And it's running less complex queries against that data. At the other end of the spectrum is centralized analytics. These systems are running much more complex queries across massive data sets — hundreds of thousands or maybe millions of records, and maybe at petabyte scale — that have been ingested from many different operational data sources across the organization. Table 1 below identifies the required capabilities across the spectrum of different classes of analytics. These are designed to help MongoDB’s customers match appropriate technologies and skill sets to each business use case they are building for. By mapping required capabilities to use cases, you can see how these different classes of analytics serve different purposes. If, for example, we're dealing with recommendations in an e-commerce platform, the centralized data warehouse or data lake will regularly analyze vast troves of first- and third-party customer data. This analysis is then blended with available inventory to create a set of potential customer offers. These offers are then loaded back into operational systems where application-driven analytics is used to decide which offers are most relevant to the customer based on a set of real-time criteria, such as actual stock availability and which items a shopper might already have in their basket. This real-time decision-making is important because you wouldn't want to serve an offer on a product that can no longer be fulfilled or on an item a customer has already decided to buy. This example demonstrates why it is essential to choose the right tool for the job. Specifically, in order to build a portfolio of potential offers, the centralized data warehouse or data lake is an ideal fit. Such technologies can process hundreds of TBs of customer records and order data in a single query. The same technologies, however, are completely inappropriate when it comes to serving those offers to customers in real time. Centralized analytics systems are not designed to serve thousands of concurrent user sessions. Nor can they access real-time inventory or basket data in order to make low latency decisions in milliseconds. Instead, for these scenarios, application-driven analytics served from an operational system is the right technology fit. As we can see, application-driven analytics is complementary to traditional centralized analytics, and in no way competitive to it. The benefits to organizations of using these complementary classes of analytics include: Maximizing competitive advantage through smarter and more intelligent applications Out-innovating and differentiating in the market Improving customer experience and loyalty Reducing cost by improving business visibility and efficiency Through its design, MongoDB Atlas unifies the essential data services needed to deliver on application-driven analytics. It gives developers the tools, tech, and skills they need to infuse analytics into their apps. At the same time, Atlas provides business analysts, data scientists, and data engineers direct access to live data using their regular tools without impacting the app. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics: Defining the Next Wave of Modern Apps .
MACH Aligned for Retail: Headless
The MACH Alliance is a non-profit organization fostering the adoption of composable architecture principles, namely Microservices , API-First , Cloud-Native SaaS , and Headless. MongoDB, among many other technology companies, is a member of this Alliance, enabling developers to adopt these principles in their applications. In this article, we’ll focus on the fourth principle championed by the MACH Alliance: Headless. Let’s dive in. What is headless? A headless architecture is one where the layers or components of the architecture are decoupled. The “heads” (i.e., frontends) operate independently from the backend logic or “core body” microservices and share data via API. This concept is key to a successful shift toward microservices — without decoupling the architectural layers, you’re running on a modern monolith. Looser coupling also leads to an increase in frontend change and flexibility, reusability of core features, less downtime because there’s no single point of failure, and promotes reusability of key features. Headless applied to retail Retail was one of the first industries to embrace headless architectures, with the term coined in 2012 by Dirk Hoerig, founder of commercetools . These concepts were originally applied to building modern ecommerce solutions and are now being expanded to any application in the IT stack. In this model, the head can be an ecommerce web frontend, or mobile app, or an internal frontend system for stock management. The core body components support the heads (Figure 1). They can be a payment system, a checkout solution, a product catalog, or a warehouse management application. Figure 1: The “head” and “core body” components, sharing data as part of APIs. Customers and their experiences are at the heart of retail. Adopting headless principles can greatly help companies meet rapidly changing customer requirements and stand out from the competition. Customers require a seamless journey between mobile, web applications, and in-store with data and logic consistent across channels. New channels might also need to be added such as integration with social media, to reach a younger customer base. Retailers might need to be able to sell in multiple regions or across product lines, requiring them to adopt multiple frontends to serve different customer groups without having to rewrite or duplicate the whole IT stack. New features might need to be added quickly to reflect competitors’ moves without tracing changes back through every component of the stack or experiencing downtime. Internal workforce systems can follow similar principles. The common denominators of these example use cases include speed of change and frontend flexibility, avoiding downtime, and reusability of the backend components. Headless solutions enable developers to avoid duplicating efforts by reusing the core capabilities of applications and adapting them to various target systems and use cases. Those principles save developers’ time and can be leveraged to provide a seamless experience to customers, as the underlying data layer and workflows are shared across multiple services offering similar functionalities. Headless architectures also come with the following advantages. Bring new features to market faster New features and MVPs can be introduced with minimal impact on other application components. Release cycles can be managed efficiently via a microservice architecture relying on different squads, and new releases can be pushed to production when ready, independently of the work of other squads. For example, a retailer can expand into a new country quickly by developing a country-specific frontend that reuses existing core components and requires no backend downtime. Scale to meet seasonal demand Companies can independently scale application components where and when required. For example, increased user traffic might require more resources to support frontend components, leaving the backend untouched and vice versa. In an ecommerce scenario, this can take the form of expected deviations from a seasonality standpoint (e.g., end-of-month transactions following salary distribution, holiday shopping) or unplanned variations (e.g., influencer marketing). Thus, this model can result in: Cost savings: Achieve cost reductions as a headless architecture running on the cloud enables to further decouple its pay-as-you-go model, by only paying for the infrastructure required by each front/backend component. Improved customer experience: Develop highly available and responsive applications so that customer experience is not affected by computing resources. Leverage best-of-breed technologies Headless architectures can help companies gain greater flexibility in deploying and managing the IT stack, allowing them to: Focus on value-add development: A composable headless architecture enables companies to choose to build or buy individual components in the stack. As the components are decoupled, it becomes easier to unpick than if the stack is fully integrated — as the APIs can be redirected to the new solution more easily. This approach lets companies put their development activity into value-added functionality should a best-of-breed vendor solution arrive on the market delivering core functionality. Avoid vendor lock-in: This also allows for more seamless technology switches should companies decide to bring development back in-house or switch vendors. Improve talent acquisition and retention: Deploying in a flexible and composable manner lets development teams choose the programming languages and tools they feel best match the requirements, allowing companies to attract and retain top talent. Less downtime with faster troubleshooting A headless architecture also makes it easier to pinpoint which single layer/component is the root cause of issues, as opposed to troubleshooting in monolithic applications where dependencies can be difficult to map. Fewer dependencies mean less downtime; when a change or failure occurs to one component, it doesn't affect the whole stack. For ecommerce retailers, any downtime can have a direct impact on revenue, so an architecture that supports a move towards 24/7 uptime is ideal. Removing data silos and sharing data across multiple journeys also enables companies to implement truly omnichannel experiences and leverage the datasets for other downstream processes, such as user personalization and analytics. Learn how Boots is using MongoDB Atlas to standardize their infrastructure via an API and microservice-driven approach . How can MongoDB help? Headless architectures require a strong data layer to reap all the above-mentioned benefits. MongoDB includes several key features that enable developers to speed up the pace of delivery of new features and bug fixes, scale with minimal effort, and leverage APIs to share data with the different components of the stack. Deliver faster with no downtime MongoDB provides a flexible document model that easily adapts to the needs of different microservices and supports adding new features and data fields without having to rethink the underlying data schema or experience downtime. Let’s consider a product catalog microservice that uses a particular API to read data from certain fields. A second microservice can be developed requiring the same set of fields as the first along with a few new ones connecting via a new API. MongoDB allows the change to be made with no downtime of the product catalog microservice and related API. Scale effortlessly Adding new features and services will likely require scaling the data layer to cater to higher storage and workload. MongoDB, through its sharding capabilities , enables a distributed architecture by horizontally scaling the data layer and by distributing data across multiple servers. This approach can provide better efficiency than a single high-speed, high-capacity server (vertical scaling), to build highly responsive retail solutions. Support composable architectures MongoDB also possesses strong API capabilities to support a microservice-based backend architecture and make data accessible and shareable across components (Figure 2). These capabilities include APIs and drivers supporting a dozen programming languages on the market, such as C, Python, Node.js, and Scala. The MongoDB Unified Query API allows working with data of any type, including time series, arrays, and geospatial. MongoDB Atlas, MongoDB’s Developer Data Platform, comes with the Atlas Data API allowing to programmatically create, read, update, and delete data stored on Atlas clusters as part of standard HTTPS requests. The Atlas GraphQL API allows fine-tuning of API requests by returning only the required data (e.g., information about a particular customer or product). Figure 2: MongoDB supports a headless architecture via APIs. Data availability and resiliency should also be considered when adopting headless architectures. MongoDB Atlas clusters are highly available and backed by an industry-leading uptime SLA of 99.995% across all cloud providers. If a primary node becomes unavailable, MongoDB Atlas will automatically failover in seconds. Clusters can be also deployed across multiple cloud regions to weather the unlikely event of a total region outage, or in multiple cloud platforms together. Summary Adopting a headless architecture is paramount for retailers wanting to enhance customer experience and build more resilient applications. MongoDB, with its leading database offering, API layer, and high availability is strongly suited to meet the requirements of modern applications. Read our previous blog posts in the MACH series covering Microservices , API-First , and Cloud-Native SaaS .
MongoDB Named as a Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2022
In The Forrester Wave™: Translytical Data Platforms, Q4 2022, translytical data platforms are described by Forrester as being “designed to support transactional, operational, and analytical workloads without sacrificing data integrity, performance, and analytics scale.” Characterized as next-generation data platforms, the Forrester report further notes that “Adoption of these platforms continues to grow strongly to support new and emerging business cases, including real-time integrated insights, scalable microservices, machine learning (ML), streaming analytics, and extreme transaction processing.” To help users understand this emerging technology landscape, Forrester published its previous Translytical Data Platforms Wave back in 2019. Three years on, Forrester has named MongoDB as a Leader in its latest Translytical Data Platforms Wave. We believe MongoDB was named a Leader in this report due to the R&D investments made in further building out capabilities in MongoDB Atlas , our multi-cloud developer data platform. These investments were driven by the demands of the developer communities we work with day-in, day-out. You told us how you struggle to bring together all of the data infrastructure needed to power modern digital experiences – from transactional databases to analytics processing, full-text search, and streaming. This is exactly what our developer data platform offers. It provides an elegant, integrated, and fully-managed data architecture accessed via a unified set of APIs. With MongoDB Atlas, developers are more productive, they ship code faster and improve it more frequently. Translytics and the Rise of Application-Driven Analytics Translytics is part of an important shift that we at MongoDB call application-driven analytics . By building smarter apps and increasing the speed of business insights, application-driven analytics gives you the opportunity to out-innovate your competitors and improve efficiency. To do this you can no longer rely only on copying data out of operational systems into separate analytics stores. Moving data takes time and creates too much separation between application events and actions. Instead, analytics processing has to be “shifted left” to the source of your data – to the applications themselves. This is the shift MongoDB calls application-driven analytics . It’s a shift that impacts both the skills and the technologies developers and analytics teams use every day. This is why understanding the technology landscape is so important. Overall, MongoDB is good for customers that are driving their strategy around developers who are tasked with building analytics into their applications. The Forrester Wave™: Translytical Data Platforms, Q4 2022 Evaluating the top vendors in the Translytic Data Platforms Wave Forrester evaluated 15 of the most significant translytical data platform vendors against 26 criteria. These criteria span current offering and strategy through to market presence. Forrester gave MongoDB the highest possible scores across eleven criteria, including: Number of customers Performance Scalability Dev Tools/API Multi-model Streaming Cloud / On-prem / distributed architecture Commercial model The report cites that “MongoDB ramps up its translytical offering aggressively”, and that “Organizations use MongoDB to support real-time analytics, systems of insight, customer 360, internet of things (IoT), and mobile applications.” Access your complimentary copy of the report here . Customer Momentum Many development teams start out using MongoDB as an operational database for both new cloud-native services as well as modernized legacy apps. More and more of these teams are now improving customer experience and speeding business insight by adopting application-driven analytics. Examples include: Bosch for predictive maintenance using IoT sensor data. Keller Williams for relevance-based property search and sales dashboarding. Iron Mountain for AI-based information discovery and intelligence. Volvo Connect for fleet management. Getting started on your Translytics Journey The MongoDB Atlas developer data platform is engineered to help you make the shift to Translytics and application-driven analytics – leading to smarter apps and increased business visibility. The best way to get started is to sign up for an account on MongoDB Atlas . Then create a free database cluster, load your own data or our sample data sets, and explore what’s possible within the platform. The MongoDB Developer Center hosts an array of resources including tutorials, sample code, videos, and documentation organized by programming language and product. Whether you are a developer or a member of an analytics team, it's never been easier to get started enriching your transactional workloads with analytics!
MongoDB and AWS: How a decade-old collaboration got even better in 2022
Developers select MongoDB because it makes building with data for almost any class of application easy and fast for them. They select Amazon Web Services (AWS) because it offers a comprehensive and broadly adopted cloud platform, offering more than 200 fully featured services. Bringing together MongoDB Atlas on AWS helps developers build and ship higher quality applications faster and scale them further. MongoDB has collaborated with AWS for close to a decade now, but 2022 has seen dramatic growth in both the quantity and quality of our joint activities, resulting in a strategic collaboration agreement announced earlier this year. Our collaboration spans joint product engineering and integration so MongoDB Atlas is a first-party service on AWS, and also extends to making it easy for customers to procure MongoDB Atlas on AWS. In 2022, we have worked more closely together than ever before. In this post, we'll cover what we've achieved, and how our customers benefit. If at any point you want to stop reading about the partnership and experience it in action, we invite you to get started for free with MongoDB's fully managed, pay-as-you-go listing on the AWS Marketplace . Delivering an outstanding customer experience Since re:Invent 2021, MongoDB and AWS have jointly seen an explosion in customer success, with MongoDB for Startups becoming one of the most widely used offerings in the AWS Activate program after we launched in July. And, since launching in the AWS Marketplace with pay-as-you-go pricing in December 2021, MongoDB Atlas has become one of the most popular self-service listings, with well over 1,000 customers. More broadly, we've seen our AWS Marketplace business show triple-digit growth through significant, mutual investments across engineering, sales, and marketing. We've also found great success working with AWS' Workload Migration and Proof of Concept programs, helping many new customers accelerate their migration to MongoDB Atlas on AWS over the past 12 months. Additionally, while MongoDB works closely with AWS across the globe, we devoted increased attention to Europe this past year, resulting in a considerable increase in customer adoption. As a result, AWS named us their AWS Marketplace Partner of the Year - EMEA in November 2022. One way that we've helped to accelerate such customer success is by making it easier to procure MongoDB Atlas on AWS. Over the past year, MongoDB and AWS have significantly simplified the purchasing experience for customers. We did this across a few key areas. One thing customers love about buying through AWS Marketplace is how seamless it makes the purchasing experience. However, historically this has been slowed somewhat for MongoDB customers by the need to agree to separate legal terms. Starting in November 2022, however, all Atlas on AWS customers purchasing through the AWS Marketplace Self Service listing use AWS Marketplace’s Standard Contract for Marketplace (SCMP) terms and conditions rather than MongoDB Cloud Terms of Service, thereby further reducing friction to getting productive, faster, with MongoDB. Close product collaboration Behind these improvements to our joint purchasing experience were significant improvements to how MongoDB Atlas integrates with key AWS services. MongoDB has long worked seamlessly with core AWS services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3), and more recently has collaborated with AWS to ensure tight integration with AWS container services like Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS), AWS serverless technologies like AWS Lambda, Amazon Eventbridge, and AWS Fargate; and edge computing services like AWS Wavelength . Over the past year, however, we've delved more deeply into AWS machine learning services (Amazon Comprehend, Amazon Kendra, Amazon Lex, etc.), AWS AppSync, Amazon Forecast, AWS Elastic Beanstalk, and more. In addition to direct integrations with AWS services, we made it simpler for customers to use MongoDB with important joint partners such as Datadog, Databricks, and Confluent. For Datadog, we improved MongoDB Atlas App Service to support forwarding logs on AWS to Datadog, thereby improving observability through real-time log analytics. With Databricks, we announced MongoDB as a data source within a Databricks notebook, thereby offering data practitioners an easier, more curated experience for connecting Databricks to MongoDB Atlas data. And with Confluent, we strengthened our integrations to help developers easily build robust, reactive data pipelines that stream events between applications and services in real time. Through innovations to the purchasing process and the product experience, we've helped make thousands of customers successful running MongoDB on AWS. Some joint customers, like Unqork , are upending entire industries with innovative approaches to technology and business. Others, like Volvo's Connected Solutions business , rely on MongoDB and AWS to scale their fleet management solution from tens of millions to billions of daily events. Other recent customers include Verizon , Marsello , GLS , and Shopline . Get started with MongoDB Atlas on AWS You needn't take our word for it, however. With just a few clicks — and no risk — you can get started for free with MongoDB Atlas on AWS . There's no upfront commitment, and if you choose to continue to build with MongoDB on AWS, you only pay for what you use.
3 Key Characteristics of Modernization
Analyst and research firm TDWI released its latest report on IT modernization: Maximizing the Business Value of Data: Platforms, Integration, and Management . The report reveals the modernization strategies, objectives, and experiences of more than 300 IT executives, data analysts, data scientists, developers, and enterprise architects. Within the survey itself lies the deeper, fundamental question of what is IT modernization in today's digital economy? It's an important question because it gets at the heart of why organizations want and need to modernize in the first place. Considering the effort, expense, and risks of modernizing, there needs to be a compelling purpose guiding the process in order to keep it on track and ensure its success. By dissecting the TDWI survey questions and responses, we can deduce what the three key characteristics of modernization are. #1: Modernization capabilities If we were to examine the elements and components that comprise modernized architecture, we would get a sense of what modernization looks like but not the purpose behind its deployment. So instead, let's start by looking at the capabilities modern architecture enables so we can get a clearer view of its characteristics and why they matter. Seventy-three percent of survey respondents reported that data democratization and self-service functionality are either extremely or very important. We've heard from numerous organizations that the task of managing data access at companies is slowing down innovation. Ben Herzberg, chief data scientist for data access company, Satori, recently told us , "The majority of organizations are still managing access to data in a manual way. Everyone is feeling the bottleneck. The data analyst who wants to do their job in a meaningful way just wants to understand what data sets they can use and get access to it fast." Getting access to data can be challenging without some sort of self-service data access capability. "Sometimes you have to go through three or four different teams to get access to data," Herzberg says. "It can take a week or two." The TDWI report also indicated a long-standing trend toward easier, more intuitive experiences extending to data integration, data pipelines, data catalog interaction, and monitoring. Survey respondents' top priorities over the next 12 months support this trend. In addition to migrating and consolidating data in the cloud, they intend to prioritize the following key capabilities: Enabling better data management for data science, AI, and ML Supporting development and deployment of data driven applications Supporting expansion in self service Business intelligence (BI) and analytics users Unifying management of data across distributed systems BI and analytics platforms remain one of the fastest growing software markets. The capabilities necessary to power these systems are in high demand: self-service analytics, faster discovery, predictions based on real-time operational data, and integration of rich and streaming data sets. The survey responses also showed that handling an increase in data volume and the number of concurrent users are modernization priorities. And there's pressure to reduce data latency and increase the frequency of updates. The survey showed that one of the most challenging capabilities organizations are dealing with is enabling low latency querying, search, and analytics. Giving users the right data at the right time to answer business questions, solve problems, and innovate with data is critical today and it depends on these capabilities. #2: Modernization outcomes The capabilities organizations seek only serve their modernization goals as far as they enable specific outcomes. And it's outcomes that are ultimately driving modernization initiatives. According to the survey, the number one outcome organizations seek to bring about is gaining fuller value from the data they store and capture. Forty-six percent of respondents cited it as their top challenge. Automating decision-making is another outcome organizations are seeking. Thirty-two percent of respondents rated automating decisions in operations in processes as very important. But it relies on the timely flow of insights into apps, one of the key capabilities identified earlier. Other key modernization outcomes cited in the survey include: Increase efficiency and effectiveness Generate new business strategies and models using analytics Make faster decisions Strengthen relationships via data sharing Improve trust and data quality Increase reuse and flexibility Reduce costs Provide authorized access to live data sets Consolidate data silos Developers in the survey said they were seeking to embed richer, personalized application experiences, with 52% saying they wanted seamless access to diverse data sets and sources. But first, they'll have to overcome several challenges that so far have proved difficult to solve. Sixty-eight percent of respondents said they face challenges processing streaming data and change data capture updates, and 64% struggle to integrate streaming with fast, high volume queries, and the same percentage said they struggle with combining historical and real-time analytics. #3: Modernization platform Modernized problems require modernized solutions. And the one most most commonly cited by respondents was a data platform , which they believe is the key to maximizing value from data. A data platform solves the issue of consolidating unnecessary data silos and ensuring access to data without the hassle of manual intervention or the risk of unauthorized access. Flexibility in the data platform is critical since data environments will continue to evolve, even after modernization milestones have been met. A data platform is one of the key elements that comprise modernized architecture. The TDWI survey cited several other advantages of unifying distributed data within a data platform: Simplifying and accelerating access Discovering data relationships easier and faster Creating a logical layer for single point of access Unifying data governance Reducing unnecessary data movement Modernized architecture Fifty-four percent of respondents said they were in the process of modernizing, and 29% were planning on doing so. The most frequently cited architectural feature by those modernizing or planning to was cloud migration from on-premises systems, with the goal being to change the dimensions of what was possible. But it wasn't just shifting to the cloud that respondents mentioned. The survey also indicated the prevalence of hybrid multi-cloud architectures as well, with data integration and management that span distributed data environments. Distributed architectures can lead to higher performance by putting data closest to where it's being used. It also solves data sovereignty issues by putting data where it's required to be due to regulatory jurisdiction. The report also mentions serverless architecture due to its pay-as-you-go computing model and improved business alignment. With serverless architecture , developers can build applications without thinking about infrastructure or traditional server management. Read the full TDWI report, Maximizing the Business Value of Data: Platforms, Integration, and Management .
10 years of MongoDB customers at AWS re:Invent
MongoDB has attended AWS re:Invent since its inception in 2012. A key reason for this is, of course, to help strengthen our partnership with AWS, which really began in 2015 and significantly expanded in March 2022 with a global, strategic collaboration agreement. But an even more fundamental reason for MongoDB's continued presence at AWS re:Invent over the years is the opportunity to engage with our many joint customers. Several MongoDB customers have been featured in re:Invent keynotes over the years. In fact, looking back at the customers AWS chose to feature in its keynotes, it's hard to find examples that are not MongoDB customers. Earlier this year, AWS celebrated 10 years of re:Invent by showcasing an equal number of "memorable customer moments" from the re:Invent mainstage. It was a great way to reaffirm AWS Leadership Principle #1 (Customer Obsession). It was also a great way to shine a light on the great things MongoDB's customers are doing. Rather than rewind on the many MongoDB customers spotlighted at re:Invent, let's look at those AWS called "most memorable" in its blog. All in on cloud Back in 2015, Capital One CIO Rob Alexander took to the re:Invent stage to discuss Capital One's "all in" approach to cloud. "We’re either using or experimenting with nearly every AWS service," Alexander said. What he didn't say, but which the company has been quite public about over the years, was how Capital One uses MongoDB in tandem with AWS services. A few months after Alexander's re:Invent comments, Capital One's Oron Gill Haus spoke at MongoDB World on Hygieia , the company's open source DevOps dashboard. Hygieia, built on MongoDB, provides the foundation for the company's attempts to reimagine banking. Haus detailed why MongoDB is so critical to Capital One's need to innovate quickly on customers' behalf, stressing how the variety and velocity of data makes MongoDB an ideal solution: We get data in from all different kinds of sources and formats, and we get it at different times. Now, what we have to do is predict the future and how you're planning on using the data. That's where traditional databases fall down. That's where you'll see MongoDB. We want to have the ability to find insights and be able to react quickly to those insights. Years later, Capital One advertises hundreds of jobs for those with MongoDB experience. (Hint: You may need to know how to roll back a MongoDB query for some of those jobs.) Capital One is doing impressive work with MongoDB, but it's not alone in its use of MongoDB for financial services. Goldman Sachs, Citi, Barclays, BBVA, Charles Schwab, FICO, HSBC, and Intuit are just a few MongoDB customers that have spoken publicly of how and why they use MongoDB. And, yes, some of these companies you may remember from the re:Invent main stage over the years. MongoDB to the Moon! Years before NASA Jet Propulsion Laboratory (JPL) took to the re:Invent stage (2016), the U.S. public agency was running MongoDB throughout NASA . By 2018, MongoDB was involved in the hugely interesting NASA Deep Space Network (DSN), a primary resource for communications and navigation for NASA's and partner agencies' interplanetary space missions. NASA had recently upgraded its decades-old infrastructure to base its modern Loading Analysis and Planning Software (LAPS) on Linux and MongoDB. LAPS, as a scientific paper details , "is responsible for long-term planning and forecasting, including studies and analysis of new missions, changed mission requirements, downtime, and new or changed antenna capabilities." Around the same time, and a key part of DSN operations, NASA was also looking for ways to improve the efficiency of operating antennas across the globe. The heart of this initiative was NASA's Link Complexity and Maintenance software (LCM), which stores all pertinent data in MongoDB. Hence, while it might not be accurate to say that MongoDB runs on the Moon, it would be true to say MongoDB helps NASA manage space missions to the Moon—and beyond. Can you hear me now? "[I]t’s just a massive moment for us at Verizon,” declared Hans Vestberg, chairman and CEO of Verizon, at re:Invent in 2019. He was talking about the company's partnership with AWS to deliver 5G network edge computing using AWS Wavelength. What wasn't said in the keynote, but that Robert Belson, Principal Engineer, Corporate Strategy, Verizon, explained , is that the vision was incomplete without MongoDB. “Verizon 5G Edge is a mobile edge computing platform, which embeds popular hyperscaler compute and storage, such as AWS Wavelength, at the edge of our 4G and 5G networks so application builders can extend existing workloads using the same popular services they know and love," he explained. “However, certain services, such as databases, are not natively supported, which is where [MongoDB] Atlas and Realm come into play by creating unprecedented flexibility for the developer and the end customer.” As we've described, Verizon decided that a comprehensive data platform was needed to make its 5G edge computing dream a reality. So Verizon integrated Atlas Functions with the Verizon edge discovery service to help direct 5G mobile clients to the topologically closest database instance across a customer’s edge deployment. In tandem, Verizon has overlaid a data persistence layer using MongoDB Realm, thereby enabling personalized experiences to extend to the network edge. Verizon is also using Atlas Device Sync and Realm to ensure the seamless synchronization of data between devices, the cloud and edge-of-network, online and offline. Customers love MongoDB + AWS Beginning to see a pattern here? While not every customer highlighted by AWS at re:Invent is a MongoDB customer, many are, including the few for which we've been able to provide some detail. Others include Epic Games, which runs its wildly popular game Fortnite on MongoDB ; or Volkswagen, which uses MongoDB throughout its web applications and in its Car Net service ; or Siemens, which runs MongoDB at the heart of its Monet system to provide monitoring, controlling, and remote management of field devices for advanced energy management services. This year while watching the various customers take the stage in re:Invent keynotes, keep in mind that they're also very likely a MongoDB customer, because customers that seek the agility and performance of AWS also tend to like how MongoDB's flexible data model enables them to do much more with their data. Interested in learning more? The best way might be to try fully managed MongoDB Atlas for free. You can get started now .
Modernize your GraphQL APIs with MongoDB Atlas and AWS AppSync
Modern applications typically need data from a variety of data sources, which are frequently backed by different databases and fronted by a multitude of REST APIs. Consolidating the data into a single coherent API presents a significant challenge for application developers. GraphQL emerged as a leading data query and manipulation language to simplify consolidating various APIs. GraphQL provides a complete and understandable description of the data in your API, giving clients the power to ask for exactly what they need — while making it easier to evolve APIs over time. It complements popular development stacks like MEAN and MERN , aggregating data from multiple origins into a single source that applications can then easily interact with. MongoDB Atlas: A modern developer data platform MongoDB Atlas is a modern developer data platform with a fully managed cloud database at its core. It provides rich features like native time series collections, geospatial data, multi-level indexing, search, isolated workloads, and many more — all built on top of the flexible MongoDB document data model. MongoDB Atlas App Services help developers build apps, integrate services, and connect to their data by reducing operational overhead through features such as hosted Data API and GraphQL API. The Atlas Data API allows developers to easily integrate Atlas data into their cloud apps and services over HTTPS with a flexible, REST-like API layer. The Atlas GraphQL API lets developers access Atlas data from any standard GraphQL client with an API that generates based on your data’s schema. AWS AppSync: Serverless GrapghQL and pub/sub APIs AWS AppSync is an AWS managed service that allows developers to build GraphQL and Pub/Sub APIs. With AWS AppSync, developers can create APIs that access data from one or many sources and enable real-time interactions in their applications. The resulting APIs are serverless, automatically scale to meet the throughput and latency requirements of the most demanding applications, and charge only for requests to the API and by real-time messages delivered. Exposing your MongoDB Data over a scalable GraphQL API with AWS AppSync Together, AWS AppSync and MongoDB Atlas help developers create GraphQL APIs by integrating multiple REST APIs and data sources on AWS. This gives frontend developers a single GraphQL API data source to drive their applications. Compared to REST APIs, developers get flexibility in defining the structure of the data while reducing the payload size by bringing only the attributes that are required. Additionally, developers are able to take advantage of other AWS services such as Amazon Cognito, AWS Amplify, Amazon API Gateway, and AWS Lambda when building modern applications. This allows for a severless end-to-end architecture, which is backed by MongoDB Atlas serverless instances and available in pay-as-you-go mode from the AWS Marketplace . Paths to integration AWS AppSync uses data sources and resolvers to translate GraphQL requests and to retrieve data; for example, users can fetch MongoDB Atlas data using AppSync Direct Lambda Resolvers. Below, we explore two approaches to implementing Lambda Resolvers: using the Atlas Data API or connecting directly via MongoDB drivers . Using the Atlas Data API in a Direct Lambda Resolver With this approach, developers leverage the pre-created Atlas Data API when building a Direct Lambda Resolver. This ready-made API acts as a data source in the resolver, and supports popular authentication mechanisms based on API Keys, JWT, or email-password. This enables seamless integration with Amazon Cognito to manage customer identity and access. The Atlas Data API lets you read and write data in Atlas using standard HTTPS requests and comes with managed networking and connections, replacing your typical app server. Any runtime capable of making HTTPS calls is compatible with the API. Figure 1: Architecture details of Direct Lambda Resolver with Data API Figure 1 shows how AWS AppSync leverages the AWS Lambda Direct Resolver to connect to the MongoDB Atlas Data API. The Atlas Data API then interacts with your Atlas Cluster to retrieve and store the data. MongoDB driver-based Direct Lambda Resolver With this option, the Lambda Resolver connects to MongoDB Atlas directly via drivers , which are available in multiple programming languages and provide idiomatic access to MongoDB. MongoDB drivers support a rich set of functionality and options , including the MongoDB Query Language, write and read concerns, and more. Figure 2: Details the architecture of Direct Lambda Resolvers through native MongoDB drivers Figure 2 shows how the AWS AppSync endpoint leverages Lambda Resolvers to connect to MongoDB Atlas. The Lambda function uses a MongoDB driver to make a direct connection to the Atlas cluster, and to retrieve and store data. The table below summarizes the different resolver implementation approaches. Table 1: Feature comparison of resolver implementations Setup Atlas Cluster Set up a free cluster in MongoDB Atlas. Configure the database for network security and access. Set up the Data API. Secrect Manager Create the AWS Secret Manager to securely store database credentials. Lambda Function Create Lambda functions with the MongoDB Data APIs or MongoDB drivers as shown in this Github tutorial . AWS AppSync setup Set up AWS Appsync to configure the data source and query. Test API Test the AWS AppSync APIs using the AWS Console or Postman . Figure 3: Test results for the AWS AppSync query Conclusion To learn more, refer to the AppSync Atlas Integration GitHub repository for step-by-step instructions and sample code. This solution can be extended to AWS Amplify for building mobile applications. For further information, please contact firstname.lastname@example.org .
MongoDB Joins Auth0 to Help Startups Combat Security Risks
We are excited to announce that MongoDB for Startups is collaborating with Auth0 for Startups to provide top security for applications by the most innovative startups. Why should a startup be part of the MongoDB and Auth0 startup programs? Customers, investors, and stakeholders expect many different things from a company, but one common requirement is responsibly managing their data. Companies choose MongoDB because it accelerates application development and makes it easier for developers to work with data. Developers mindful of security, compliance, and privacy when it comes to data use the robust Auth0 platform to create great customer experiences with features like single sign-on and multi-factor authentication. “Auth0 and MongoDB are very complementary in nature. While MongoDB provides a strong, secure data platform to store sensitive workloads, Auth0 provides secure access for anyone with the proper authorization," says Soumyarka Mondal, Co-founder of Sybill.ai. "We are safely using Auth0 as one of the data stores for the encryption piece, as well as using those keys to encrypt all of our users’ confidential information inside MongoDB.” What is the Auth0 for Startups Program? Auth0, powered by Okta, takes a modern approach to identity and enables startups to provide secure access to any application, for any user. Through Auth0 for Startups, we are bringing the convenience, privacy, and security of Auth0 to early-stage ventures, allowing them to focus on growing their business quickly. The Auth0 for Startups program is free for one year and supports: 100,000 monthly active users Five enterprise connections Passwordless authentication Breached password detection 50+ integrations, 60+ SDKs, and 50+ social & IdP connections What is the MongoDB for Startups Program? MongoDB for Startups is focused on enabling the success of high-growth startups from ideation to IPO. The program is designed to give startups access to the best technical database for their rapidly scaling ventures. Apply to our program and program participants will receive: $500 in credits for all MongoDB cloud products (valid for 12 months) A dedicated technical advisor for a two-hour, one-to-one consultation to help you with your data migration and optimization Co-marketing opportunities Access to the MongoDB developer ecosystem and access to our VC partners. Apply to Auth0 For Startups and the MongoDB for Startups Program today.
MongoDB and AWS: Simplifying OSDU Metadata Management
In this decade of the 2020s, the energy sector is experiencing two major changes at the same time: The transition from fossil to renewables, and the digital transformation that changes the way businesses operate through better applications and tools that help streamline and automate processes. To support both of these challenges, the Open Group OSDU Forum has created a new data platform standard for the energy industry that seeks to reduce data silos and enable transformational workflows via an open, standards-based API set and supporting ecosystem. OSDU (Open Subsurface Data Universe) is an industry-defining initiative that provides a unified approach to store and retrieve data in a standardized way in order to allow reductions in infrastructure cost, simplify the integration of separate business areas, and adopt new energy verticals within the same architectural principles. Amazon Web Services (AWS) — as an early supporter of OSDU — provides a premier, cloud-first offering available across more than 87 availability zones and 27 regions. MongoDB — an OSDU member since 2019 — and AWS are collaborating to leverage MongoDB as part of the AWS OSDU platform for added flexibility and to provide a robust multi-region OSDU offering to major customers. Why MongoDB for OSDU? OSDU provides a unique challenge, as its architecture is set to support a varied data set originating from the oil and gas industry, while also being extensible enough to support the expanding requirements of new energy and renewables. It must be able to support single-use on a laptop for beginning practitioners, yet scale to the needs of experts with varying deployment scenarios — from on-premises, in-field, and cloud — and from single tenant on one region to multi-region and multi-tenant applications. Furthermore, OSDU architectural principles separate raw object data from the metadata that describes it, which puts an additional burden on the flexibility needed to manage OSDU metadata, while supporting all the above requirements. Enter MongoDB Since 2008, MongoDB has championed the use of the document model as the data store that supports a flexible JSON-type structure, which can be considered a superset of different existing data types — from tabular, key-value, and text to geo-spatial, graph, and time series. Thus, MongoDB has the flexibility not only to support just the main metadata services in OSDU but also to adapt to the needs of domain-specific services as OSDU evolves. The flexibility of MongoDB allows users to model and query the data in a variety of ways within the same architecture without the need to proliferate disparate databases for each specific data type, which incurs overhead both in terms of deployment, cost and scale, and the ability to query. The schema flexibility inherent in this document model allows developers to adapt and make changes quickly, without the operational burden that comes with schema changes with traditional tabular databases. MongoDB can also scale from the smallest environment to massive, multi-region deployments, with cross-regional data replication support that is available today across more than 90 regions with MongoDB Atlas . With the addition of MongoDB’s cluster-to-cluster sync , MongoDB can easily support hybrid deployments bridging on-premises or edge to the cloud, a requirement that is increasingly important for energy supermajors or for regions where data sovereignty is paramount. Example: LegalTag An example of the benefit of MongoDB’s document model is OSDU’s LegalTag Compliance Service , which governs the legal status of data in the OSDU data ecosystem. It is a collection of JSON properties that governs how the data can be consumed and ingested. With MongoDB, the properties are directly stored, indexed, and made available to be queried — even via full-text search for more advanced use cases. The schema flexibility simplifies integrating additional derived data from ingested data sources, which is utilized for the further enrichment of the LegalTag metadata. Here the JSON document can accommodate more nodes to integrate this data without the need for new tables and data structures that need to be created and managed. AWS OSDU with MongoDB MongoDB and AWS collaborated to provide a MongoDB-based metadata implementation (Figure 1), which is available for all main OSDU services: Partition, Entitlements, Legal, Schema, Storage. The AWS default ODSU Partition service leverages MongoDB due to its simple replication capabilities (auto-deployable via CloudFormation, Terraform, and Kubernetes), which simplify identifying the correct connection information at runtime to the correct OSDU partition in a multi-region and multi-cluster deployment. The OSDU Entitlements service manages authorization and permissions for access to OSDU services and its data-using groups. The most recent OSDU reference implementation for Entitlements leverages a graph model to manage the relationship between groups, members, and owners. Thus, AWS again chose MongoDB with its inherent graph capabilities through the document model to simplify the implementation without the need to integrate a further dedicated database technology into the architecture. Figure 1: MongoDB metadata service options with AWS OSDU. Other potential benefits for OSDU MongoDB also offers workload isolation , which provides the ability to dedicate instances only for reporting workloads against the operational dataset. This provides the ability to create real-time observability of the system based on the activity on metadata. Triggers and aggregation pipelines allow the creation of an alternate view of activity in real-time, which can easily be visualized via MongoDB Charts (part of Atlas) without the need for a dedicated visualization system. Flexibility and consistency A major use case for both the energy industry and the direction of OSDU is the ability to capture and preprocess data closest to where it originated. For remote locations where direct connections to the cloud are prohibitive, this approach is often the only option — think Arctic or off-shore locations. Additionally, certain countries have data sovereignty laws that require an alternative deployment option outside of the public cloud. A MongoDB-based OSDU implementation can provide a distinct advantage, as MongoDB as a data platform itself supports deployment in the field (e.g., off-shore), on-premises, in private cloud (e.g., Kubernetes, Terraform), public cloud (e.g., AWS) and as a SaaS implementation (e.g., Atlas). Adoption of MongoDB for OSDU provides consistency across different deployment/cloud scenarios, thereby reducing the overhead for managing and operating a disparate set of technologies where multiple scenarios are required. Conclusion OSDU has been created to change the way data is collected and shared across the oil and gas and energy industry. Its intent is to accelerate digital transformation within the industry. The range of use cases and deployment scenarios requires a solution that provides flexibility in the supported datasets, flexibility for the developer to innovate without additional schema and operational burden, as well as flexibility to be deployable in various environments. Through the collaboration of AWS and MongoDB, there is an additional metadata storage option available for OSDU that provides a modern technology stack with the performance and scalability for the most demanding scenario in the energy industry. 1. MongoDB Atlas 2. MongoDB Edge Computing 3. OSDU Data platform on AWS
Manage and Store Data Where You Want with MongoDB
Increasingly, data is stored in a public cloud as companies realize the agility and cost benefits of running on cloud infrastructure. At any given time, however, organizations must know where their data is located, replicated, and stored — as well as how it is collected and processed to constantly ensure personal data privacy. Creating a proper structure for storing your data just where you want it can be complex, especially with the shift towards geographically dispersed data and the need to comply with local and regional privacy and data security requirements. Organizations without a strong handle on where their data is stored potentially risk millions of dollars in regulatory fines for mishandling data, loss of brand credibility, and distrust from customers. Geographically dispersed data and various compliance regulations also impact how organizations design their applications, and many see these challenges as an opportunity to transform how they engage with data. For example, organizations get the benefits of a multi-cloud strategy and avoid vendor lock-in, knowing that they can still run on-premises or on a different cloud provider. However, a flexible data model is needed to keep data within the confines of the country or region where the data originates. MongoDB runs where you want your data to be — on-premises, in the cloud, or as an on-demand, fully managed global cloud database. In this article, we’ll look at ways MongoDB can help you keep your data exactly where you need it. Major considerations for managing data When managing data, organizations must answer questions in several key areas, including: Process: How is your company going to scale security practices and automate compliance for the most prevalent data security and privacy regulatory frameworks? Penalties: Are your business leaders fully aware of the costs associated with not adhering to regulations when storing and managing your data? Scalability: Do you have an application that you anticipate will grow in the future and can scale automatically as demand requires? Infrastructure: Is legacy infrastructure keeping you from being able to easily comply with data regulations? Flexibility: Is your data architecture agile enough to meet regulations quickly as they grow in breadth and complexity? Cost: Are you wasting time and money with manual processes when adhering to regulations and risking hefty fines related to noncompliance? How companies use MongoDB to store data where they want and need it When storing and managing data in different regions and countries, organizations must also understand the rules and regulations that apply. MongoDB is uniquely positioned to support organizations to meet their data goals with intuitive security features and privacy controls, as well as the ability to geographically deploy data clusters and backups in one or several regions. Zones in sharded clusters MongoDB uses sharding to support deployments with very large data sets and high-throughput operations. In sharded clusters, you can create zones of sharded data based on the shard key, which helps improve the locality of data. Network isolation and access Each MongoDB Atlas project is provisioned into its own virtual private cloud (VPC), thereby isolating your data and underlying systems from other MongoDB Atlas users. This approach allows businesses to meet data requirements while staying highly available within each region. Each shard of data will have multiple nodes that automatically and transparently fail over for zero downtime, all within the same region. Multi-cloud clusters MongoDB Atlas is the only globally distributed, multi-cloud database. It lets you deploy a single cluster across AWS, Microsoft Azure, and Google Cloud without the operational complexity of managing data replication and migration across clouds. With the ability to define a geographic location for each document, your teams can also keep relevant data close to end users for regulatory compliance. IP whitelists IP whitelists allow you to specify a specific range of IP addresses against which access will be granted, delivering granular control over data. Queryable encryption Queryable encryption enables encryption of sensitive data from the client side, stored as fully randomized, encrypted data on the database server side. This feature delivers the utmost in security without sacrificing performance and is available on both MongoDB Atlas and Enterprise Advanced. MongoDB Atlas global clusters Atlas global clusters allow organizations with distributed applications to geographically partition a fully managed deployment in a few clicks and control the distribution and placement of their data with sophisticated policies that can be easily generated and changed. Thus, your organization can not only achieve compliance with local data protection regulations more easily but also reduce overhead. Client-Side Field Level Encryption MongoDB’s Client-Side Field Level Encryption (FLE) dramatically reduces the risk of unauthorized access or disclosure of sensitive data. Fields are encrypted before they leave your application, protecting them everywhere — in motion over the network, in database memory, at rest in storage and backups, and in system logs. Segmenting data by location with sharded clusters As your application gets more popular, you may reach a point where your servers will reach their maximum load. Before you reach that point, you must plan for scaling your database to adjust resources to meet demand. Scaling can occur temporarily, with a sudden burst of traffic, or permanently with a constant increase in the popularity of your services. Increased usage of your application brings three main challenges to your database server: The CPU and/or memory becomes overloaded, and the database server either cannot respond to all the request throughput, or do so in a reasonable amount of time. Your database server runs out of storage and thus cannot store all the data. Your network interface is overloaded, so it cannot support all the network traffic received. When your system resource limits are reached, you will want to consider scaling your database. Horizontal scaling refers to bringing on additional nodes to share the load. This process is difficult with relational databases because of the difficulty in spreading out related data across nodes. With non-relational databases, this is made simpler because collections are self-contained and not coupled relationally. This approach allows them to be distributed across nodes more simply, as queries do not have to “join” them together across nodes. Horizontal scaling with MongoDB Atlas is achieved through sharding. With sharded clusters, you can create zones of sharded data based on the shard key . You can associate each zone with one or more shards in the cluster. A shard can be associated with any number of zones. In a balanced cluster, MongoDB migrates chunks covered by a zone only to those shards associated with the zone: If one of the data centers goes down, the data is still available for reads unlike a single data center distribution. If the data center with a minority of the members goes down, the replica set can still serve write operations as well as read operations. However, if the data center with the majority of the members goes down, the replica set becomes read-only. Figure 1 illustrates a sharded cluster that uses geographic zones to manage and satisfy data segmentation requirements. Figure 1: Sharded cluster Other benefits of MongoDB Atlas MongoDB Atlas also provides organizations with an intuitive UI or administration API to efficiently perform tasks that would otherwise be very difficult. Upgrading your servers or setting up sharding without having to shut down your servers can be a challenge, but MongoDB Atlas removes this layer of difficulty through the features described here. With MongoDB, scaling your databases can be done with a couple of clicks. Meeting your data goals with MongoDB Organizations are uniquely positioned to store and manage data where they want it with MongoDB’s range of features discussed above. With the shift towards geographically dispersed data, organizations must make sure they are aware of – and fully understand – the local and regional rules and requirements that apply for storing and managing data. To learn more about how MongoDB can help you meet your data goals, check out the following resources: MongoDB Atlas security, with built-in security controls for all your data Entrust MongoDB Cloud Services with sensitive application and user data Scalability with MongoDB Atlas