How Edenlab Built a High-Load, Low-Code FHIR Server to Deliver Healthcare for 40 Million Plus Patients
The Kodjin FHIR server has speed and scale in its DNA. Edenlab, the Ukrainian company behind Kodjin , built our original FHIR solution to digitize and service the entire Ukrainian national health system. The learnings and technologies from that project informed our development of the Kodjin FHIR server. At Edenlab, we have always been driven by our passion for building solutions that excel in speed and scale. With Kodjin, we have embraced a modern tech stack to deliver unparalleled performance that can handle the demands of large-scale healthcare systems, providing efficient data management and seamless interoperability. Eugene Yesakov, Solution Architect, Author of Kodjin Built for speed and scale While most healthcare projects involve handling large volumes of data, including patient records, medical images, and sensor data, the Kodjin FHIR server is based on a system developed to handle tens of millions of patient records and thousands of requests per second, to ensure timely access and efficient decision-making for a population of over 40 million people. And all of this information had to be processed and exchanged in real-time or near real-time, without delays or bottlenecks. This article will explore some of the architectural decisions the Edenlab team took when building Kodjin, specifically the role MongoDB played in enhancing performance and ensuring scalability. We will examine the benefits of leveraging MongoDB's scalability, flexibility, and robust querying capabilities, as well as its ability to handle the increasing velocity and volume of healthcare data without compromising performance. About Kodjin FHIR server Kodjin is an ONC-certified and HIPAA-compliant FHIR Server that offers hassle-free healthcare data management. It has been designed to meet the growing demands of healthcare projects, allowing for the efficient handling of increasing data volumes and concurrent requests. Its architecture, built on a horizontally scalable microservices approach, utilizes cutting-edge technologies such as the Rust programming language, MongoDB, ElasticSearch, Kafka, and Kubernetes. These technologies enable Kodjin to provide users with a low-code approach while harnessing the full potential of the FHIR specification. A deeper dive into the architecture approach - the role of MongoDB in Kodjin When deciding on the technology stack for the Kodjin FHIR Server, the Edenlab team knew that a document database would be required to serve as a transactional data store. In an FHIR Server, a transactional data store ensures that data operations occur in an atomic and consistent manner, allowing for the integrity and reliability of the data. Document databases are well-suited for this purpose as they provide a flexible schema and allow for storing complex data structures, such as those found in FHIR data. FHIR resources are represented in a hierarchical structure and can be quite intricate, with nested elements and relationships. Document databases, like MongoDB, excel at handling such complex and hierarchical data structures, making them an ideal choice for storing FHIR data. In addition to supporting document storage, the Edenlab team needed the chosen database to provide transactional capabilities for FHIR data operations. FHIR transactions, which encompass a set of related data operations that should either succeed or fail as a whole, are essential for maintaining data consistency and integrity. They can also be used to roll back changes if any part of the transaction fails. MongoDB provides support for multi-document transactions , enabling atomic operations across multiple documents within a single transaction. This aligns well with the transactional requirements of FHIR data and ensures data consistency in Kodjin. Implementation of GridFS as a storage for the terminologies in Terminology service Terminology service plays a vital role in FHIR projects, requiring a reliable and efficient storage solution for terminologies used. Kodjin employs GridFS , a file system within MongoDB designed for storing large files, which makes it ideal to handle terminologies. GridFS offers a convenient way to store and manage terminology files, ensuring easy accessibility and seamless integration within the FHIR ecosystem. By utilizing MongoDB's GridFS, Kodjin ensures efficient storage and retrieval of terminologies, enhancing the overall functionality of the terminology service. Kodjin FHIR server performance To evaluate the efficiency and responsiveness of the Kodjin FHIR server in various scenarios we conducted multiple performance tests using Locust, an open-source load testing tool. One of the performance metrics measured was the retrieval of resources by their unique ids using the GET by ID operation. Kodjin with MongoDB achieved a performance of 1721.8 requests per second (RPS) for this operation. This indicates that the server can efficiently retrieve specific resources, enabling quick access to desired data. The search operation, which involves querying ElasticSearch to obtain the ids of the searched resources and retrieving them from MongoDB, exhibited a performance of 1896.4 RPS. This highlights the effectiveness of polyglot persistence in Kodjin, leveraging ElasticSearch for fast and efficient search queries and MongoDB for resource retrieval. The system demonstrated its ability to process search queries and retrieve relevant results promptly. In terms of resource creation, Kodjin with MongoDB showed a performance of 1405.6 RPS for POST resource operations. This signifies that the system can effectively handle numerous resource-creation requests. The efficient processing and insertion of new resources into the MongoDB database ensure seamless data persistence and scalability. Overall, the performance tests confirm that Kodjin with MongoDB delivers efficient and responsive performance across various FHIR operations. The high RPS values obtained demonstrate the system's capability to handle significant workloads and provide timely access to resources through GET by ID, search, and POST operations. Conclusion Kodjin leverages a modern tech stack including Rust, Kafka, and Kubernetes to deliver the highest levels of performance. At the heart of Kodjin is MongoDB, which serves as a transactional data store. MongoDB's capabilities, such as multi-document transactions and flexible schema, ensure the integrity and consistency of FHIR data operations. The utilization of GridFS within MongoDB ensures efficient storage and retrieval of terminologies, optimizing the functionality of the Terminology service. To experience the power and potential of the Kodjin FHIR server firsthand, we invite you to contact the Edenlab team for a demo. For more information On MongoDB’s work in healthcare, and to understand why the world’s largest healthcare companies trust MongoDB, read our whitepaper on radical interoperability .
Accelerating to T+1 - Have You Got the Speed and Agility Required to Meet the Deadline?
Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post. On May 28, 2024, the Securities and Exchange Commission (SEC) will implement a move to a T+1 settlement for standard securities trades , shortening the settlement period from 2 business days after the trade date to one business day. The change aims to address market volatility and reduce credit and settlement risk. The shortened T+1 settlement cycle can potentially decrease market risks, but most firms' current back-office operations cannot handle this change. This is due to several challenges with existing systems, including: Manual processes will be under pressure due to the shortened settlement cycle Batch data processing will not be feasible To prepare for T+1, firms should take urgent action to address these challenges: Automate manual processes to streamline them and improve operational efficiency Event-based real-time processing should replace batch processing for faster settlement In this blog, we will explore how MongoDB can be leveraged to accelerate manual process automation and replace batch processes to enable faster settlement. What is a T+1 and T+2 settlement? T+1 settlement refers to the practice of settling transactions executed before 4:30pm on the following trading day. For example, if a transaction is executed on Monday before 4:30 pm, the settlement will occur on Tuesday. This settlement process involves the transfer of securities and/or funds from the seller's account to the buyer's account. This contrasts with the T+2 settlement, where trades are settled two trading days after the trade date. According to SEC Chair Gary Gensler , “T+1 is designed to benefit investors and reduce the credit, market, and liquidity risks in securities transactions faced by market participants.” Overcoming T+1 transition challenges with MongoDB: Two unique solutions 1. The multi-cloud developer data platform accelerates manual process automation Legacy settlement systems may involve manual intervention for various tasks, including manual matching of trades, manual input of settlement instructions, allocation emails to brokers, reconciliation of trade and settlement details, and manual processing of paper-based documents. These manual processes can be time-consuming and prone to errors. MongoDB (Figure 1 below) can help accelerate developer productivity in several ways: Easy to use: MongoDB is designed to be easy to use, which can reduce the learning curve for developers who are new to the database. Flexible data model: Allows developers to store data in a way that makes sense for their application. This can help accelerate development by reducing the need for complex data transformations or ORM mapping. Scalability: MongoDB is highly scalable , which means it can handle large volumes of trade data and support high levels of concurrency. Rich query language: Allows developers to perform complex queries without writing much code. MongoDB's Apache Lucene-based search can also help screen large volumes of data against sanctions and watch lists in real-time. Figure 1: MongoDB's developer data platform Discover the developer productivity calculator . Developers spend 42% of their work week on maintenance and technical debt. How much does this cost your organization? Calculate how much you can save by working with MongoDB. 2. An operational trade store to replace slow batch processing Back-office technology teams face numerous challenges when consolidating transaction data due to the complexity of legacy batch ETL and integration jobs. Legacy databases have long been the industry standard but are not optimal for post-trade management due to limitations such as rigid schema, difficulty in horizontal scaling, and slow performance. For T+1 settlement, it is crucial to have real-time availability of consolidated positions across assets, geographies, and business lines. It is important to note that the end of the batch cycle will not meet this requirement. As a solution, MongoDB customers use an operational trade data store (ODS) to overcome these challenges for real-time data sharing. By using an ODS, financial firms can improve their operational efficiency by consolidating transaction data in real-time. This allows them to streamline their back-office operations, reduce the complexity of ETL and integration processes, and avoid the limitations of relational databases. As a result, firms can make faster, more informed decisions and gain a competitive edge in the market. Using MongoDB (Figure 2 below), trade desk data is copied into an ODS in real-time through change data capture (CDC), creating a centralized trade store that acts as a live source for downstream trade settlement and compliance systems. This enables faster settlement times, improves data quality and accuracy, and supports full transactionality. As the ODS evolves, it becomes a "system of record/golden source" for many back office and middle office applications, and powers AI/ML-based real-time fraud prevention applications and settlement risk failure systems. Figure 2: Centralized Trade Data Store (ODS) Managing trade settlement risk failure is critical in driving efficiency across the entire securities market ecosystem. Luckily, MongoDB integration capabilities (Figure 3 below) with modern AI and ML platforms enable banks to develop AI/ML models that make managing potential trade settlement fails much more efficient from a cost, time, and quality perspective. Additionally, predictive analytics allow firms to project availability and demand and optimize inventories for lending and borrowing. Figure 3: Event-driven application for real time monitoring Summary Financial institutions face significant challenges in reducing settlement duration from two business days (T+2) to one (T+1), particularly when it comes to addressing the existing back-office issues. However, it's crucial for them to achieve this goal within a year as required by the SEC. This blog highlights how MongoDB's developer data platform can help financial institutions automate manual processes and adopt a best practice approach to replace batch processes with a real-time data store repository (ODS). With the help of MongoDB's developer data platform and best practices, financial institutions can achieve operational excellence and meet the SEC's T+1 settlement deadline on May 28, 2024. In the event of T+0 settlement cycles becoming a reality, institutions with the most flexible data platform will be better equipped to adjust. Top banks in the industry are already adopting MongoDB's developer data platform to modernize their infrastructure, leading to reduced time-to-market, lower total cost of ownership, and improved developer productivity. Looking to learn more about how you can modernize or what MongoDB can do for you? Zero downtime migrations using MongoDB’s flexible schema Accelerate your digital transformation with these 5 Phases of Banking Modernization Reduce time-to-market for your customer lifecycle management applications MongoDB’s financial services hub
Introducing the Certified MongoDB Atlas Connector for Power BI
This is a collaborative post from MongoDB and Microsoft. We thank Alexi Antonino, Natacha Bagnard, Jad Jarouche from MongoDB, and Bob Zhang, Mahesh Prakriya, and Rajeev Jain from Microsoft for their contributions. Introducing MongoDB Atlas Connector for Power BI, the certified solution that facilitates real-time insights on your Atlas data directly in the Power BI interfaces that analysts know and love! Supporting Microsoft’s Intelligent Data Platform , this integration bridges the gap between Developers and Analytics teams, allowing analysts who rely on Power BI for insights to natively transform, analyze, and share dashboards that incorporate live MongoDB Atlas data. Available in June , the Atlas Power BI Connector empowers companies to harness the full power of their data like never before. Let’s take a deeper look into how the Atlas Power BI Connector can unlock comprehensive, real-time insights on live application data that will help take your business to the next level. Effortlessly model document data with Power Query The Atlas Power BI Connector makes it easy to model document data with native Power BI features and data modeling capabilities. With its SQL-92 compatible dialect, mongosql, you can tailor your data to fit any requirements by transforming heavily nested document data to fit your exact needs, all from your Power Query dashboard. Gain real-time insights on live application data By using the Power BI Connector to connect directly to MongoDB Atlas, you can build up-to-date dashboards in Power BI Desktop and scale insights to your organization through Power BI Service with ease. With no delays caused by data duplication, you can stay ahead of the curve by unlocking real-time insights on Atlas data that are relevant to your business. Empower cross-source data analysis The Power BI Connector's integration with MongoDB Atlas enables you to seamlessly model, analyze, and share insightful dashboards that are built from multiple data sources. By combining Atlas's powerful Data Federation capabilities with Power BI's advanced analytics and visualization tools, you can easily create comprehensive dashboards that offer valuable insights into your data, regardless of where it is stored. See it in action Log in and activate the Atlas SQL Interface to try out the Atlas Power BI Connector ! If you are new to Atlas or Power BI, get started for free today on Azure Marketplace or Power BI Desktop .
The MongoDB for VS Code Extension Is Now Generally Available
4 Ways MongoDB Solves Healthcare's Interoperability Puzzle
Picture this: You're on a road trip, driving across the country, taking in the beautiful scenery, and enjoying the freedom of the open road. But suddenly, the journey comes to a screeching halt as you fall seriously ill and need emergency surgery. The local hospital rushes you into the operating room, but how will they know what medications you're allergic to, or what conditions you've been treated for in the past? Figure 1: Before and after interoperability In a perfect world, the hospital staff would have access to all of your medical records, seamlessly integrated into one interoperable electronic health record (EHR) system. This would enable them to quickly and accurately treat you as seen in Figure 1. Unfortunately, the reality is that data is often siloed, fragmented, and difficult to access, making it nearly impossible for healthcare providers to get a complete picture of their patients' health. That’s where interoperability comes in, enabling seamless integration of data from different sources and formats, allowing healthcare providers with easy access to the information they need, even between different health providers. And at the heart of solving the interoperability challenge is MongoDB, the ideal solution for building a truly interoperable data repository. In this blog post, we'll explore four ways why MongoDB stands out from all others in the interoperability software space. We'll show you how our unique capabilities make us the fundamental missing piece in the interoperability puzzle for healthcare. Let’s get started! 1. Document flexibility MongoDB's document data model is perfect for managing healthcare data. It allows you to work with the data in JSON format, eliminating the need to flatten or transform it into a string. This simplifies the implementation of common interoperability standards for clinical and terminology data, such as HL7 FHIR and openEHR, as well as SNOMED and LOINC - because all of these standards also support JSON. The document model also supports nested and hierarchical data structures, making it easier to represent complex clinical data with varying levels of detail and granularity. MongoDB's document model also provides flexibility in managing healthcare data, allowing for dynamic and self-describing schemas. With no need to pre-define the schema, fields can vary from document to document and can be modified at any time without requiring disruptive schema migrations. This makes it easy for healthcare providers to add or update information to clinical documents, such as when new interoperability standards are released, ensuring that healthcare data is kept accurate and up-to-date without requiring database reconfiguration or downtime. 2. Scalability Dealing with large healthcare datasets can be challenging for traditional relational database systems, but MongoDB's horizontal scaling feature offers a solution. With horizontal scaling, healthcare providers can easily distribute their data across multiple servers and cloud providers (AWS, GCP, and Azure), resulting in increased processing power and faster query times. It also results in more cost-efficient storage as growing vertically is more expensive than growing horizontally. This feature allows healthcare providers to scale their systems seamlessly as their data volumes grow while maintaining performance and reliability. While MongoDB’s reliability is ensured through its replication architecture, where each database replica set consists of three nodes that provide fault tolerance and automatic failover in the event of node failure. Horizontal scaling also improves reliability by adding more servers or nodes to the system, reducing the risk of a single point of failure. 3. Performance When it comes to healthcare data, query performance can make all the difference in delivering timely and accurate care. And that’s another aspect where MongoDB shines. MongoDB holds data in a format that is optimized for storage and retrieval, allowing it to quickly and efficiently read and write data. MongoDB’s advanced querying capabilities, backed by compound and wildcard indexes, make it a standout solution for healthcare applications. MongoDB Atlas’ Search, using Apache Lucene indexing, also enables efficient querying across vast data sets, handling complex queries with multiple fields. This is especially useful for Clinical Data Repositories (CDRs), which permit almost unlimited querying flexibility. Atlas Search indexing also allows for advanced search features enabling medical professionals to quickly and accurately access the information they need from any device. 4. Security Figure 2: Fine-grained access control The security of sensitive clinical data is paramount in the healthcare industry. That’s why MongoDB provides an array of robust security features, including fine-grained access control and auditing as seen in Figure 2. With Client-Side-Field-Level Encryption (CS-FLE) and Queryable Encryption, MongoDB is the only data platform that allows the processing of randomly encrypted patient data, providing the highest level of data security, with minimal impact on performance. Additionally, MongoDB Atlas supports VPC peering and private links that permit secure connections to healthcare applications, wherever they are hosted. By implementing strong security measures from the start, organizations can ensure privacy by design. Partner ecosystem MongoDB is the only non-relational database and modern data platform that directly collaborates with clinical data repository (CDR) vendors like Smile, Exafluence, Better, Firely, and others. While some vendors offer MongoDB as an alternative to a relational database, others have built their solutions exclusively on MongoDB, one for example is Kodjin FHIR server. MongoDB has extended its capabilities to integrate with AWS FHIR Works, enabling healthcare providers and payers to deploy a FHIR server with MongoDB Atlas through the AWS Marketplace. With MongoDB's unique approach to data storage and retrieval and its ability to work with CDR vendors, millions of patients worldwide are already benefiting from its use. Beyond interoperability with MongoDB Access to complete medical records is often limited by data silos and fragmentation, leaving healthcare providers with an incomplete picture of their patients' health. That's where MongoDB's interoperability solution comes in as the missing puzzle piece the healthcare industry needs. With MongoDB's unmatched document flexibility, scalability, performance, and security features, healthcare providers can access accurate and up-to-date patient information in real-time. But MongoDB's solution goes beyond that. Radical interoperability with MongoDB means that healthcare providers own the data layer and are thus able to leverage any usages from the stored data, and connect to any existing applications or APIs. They're free to work with any healthcare data standard, including custom schemas, and leverage the data for use cases beyond storage and interoperability. The future of healthcare is here, and with MongoDB leading the way, we can expect to see more innovative solutions that put patients first. If you're interested in learning more about radical interoperability with MongoDB, check out our brochure .
Aerofiler Brings Breakthrough Automation to the Legal Profession
Don Nguyen is the perfect person to solve a technology problem in the legal space. Don spent several years in software engineering before eventually becoming a lawyer, where he discovered just how much manual, administrative work legal professionals have to do. The company he co-founded, Aerofiler, takes the different parts of the contract lifecycle and digitises them to eliminate manual work, allowing lawyers to focus on things that require their expertise. Don says the legal profession has always been behind industries like accounting, marketing, and finance when it comes to leveraging technology to increase productivity. Both Don and his co-founder, Stuart Loh, thought they could automate a lot of manual tasks for legal professionals through an AI-powered contract lifecycle management solution. Turning mountains into automation Law firms generate mountains of paperwork that must be digitised and filed. Searching contracts post-execution can be an arduous task using the legacy systems most firms are running on today. Initially, Don, Stuart, and Jarrod Mirabito (co-founder and CTO) set out to make searching contracts and tracking obligations easier. As the service became more popular, customers started asking for more capabilities, like digitising and automating the approval process. Aerofiler's solution now manages the entire contract lifecycle, from drafting and negotiations to approvals, signing, and filing. Don says the difficulty with running AI to extract data is you can't usually see where the data is coming from, and you can't train your models, for example, to extract a concept that might be specific to your industry. Aerofiler supports custom extraction so firms can crawl for and find exactly the results they're looking for, and it highlights exactly where in the contract the data is found. Aerofiler is unique as a modern, cloud-based Contract Lifecycle Management solution that streamlines contract management processes and enhances workflow efficiency. It features AI-powered analytics, smart templates, and real-time collaboration tools, and is highly configurable to fit the unique needs of different companies. Aerofiler's user interface is also highly intuitive and user-friendly, leading to greater user adoption and overall efficiency. The startup stack Don has over 10 years of experience working with MongoDB and describes it as very robust. When it was time to choose a database for their startup, MongoDB Atlas was an easy choice. One of the big reasons Don chose Atlas is so they don't have to manage their own infrastructure. Atlas provides the functionality for text search, storage, and metadata retrieval, making it easy to hit the ground running. On top of MongoDB, the system runs Express.js, VueJS, and Node.js, also known as a MEVN stack. In choosing a database, Don points out that every assumption you make will have exceptions to it, and no matter what your requirements are now, they will inevitably change. So one of the key factors in making a decision is how that database will handle those changes when they come. In his experience, NoSQL databases like MongoDB are easy to deploy and maintain. And, with MongoDB offering ACID transactions , they get a lot of the functionality that they would otherwise look for in a relational database stack. How startups grow up Aerofiler is part of the MongoDB for Startups program, which helps early-stage, high-growth startups build faster and scale further. MongoDB for Startups offers access to a wide range of resources, including free credits to our best-in-class developer data platform, MongoDB Atlas, personalized technical advice, co-marketing opportunities, and access to our robust developer community. Don says the free credits helped the startup at a time when resources were tight. The key to their success, Don says, is in solving problems their customers have. In terms of the road ahead, Don is excited about ChatGPT and says there are some very interesting applications for generative AI in the legal space. If anyone would like to talk about what generative AI is and how it could work in the legal space, he's happy to take those calls and emails . Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now .
MongoDB Goes (Leafy) Green: Our Net Zero Commitment
At MongoDB, we have a deep commitment to sustainability and taking ownership of our environmental impact. In 2021, we internally announced our pledge to have net zero emissions (CO 2 e) by 2030, and have since benchmarked our emissions and have worked on developing a strategy to achieve this goal. Through this process, we discovered that over our last fiscal year we produced the same amount of carbon as driving a gas-powered vehicle around the globe more than 6,000 times. This amount may seem high because we calculated not only our Scope 1 (direct; e.g., offices) and Scope 2 (indirect; e.g., purchased electricity) emissions as is standard under current reporting requirements, but also our Scope 3 emissions (indirect; e.g., supply/value chain). These Scope 3 emissions account for ~97.5% of our total footprint. We have chosen to disclose this full amount because we are committed to reducing our entire carbon footprint as much as possible and want to be transparent on this journey. While 2030 may seem far away, we are committed to reducing emissions and already have taken immediate action. Last year, we invested in hiring a Sustainability Manager to help us tackle how we are going to achieve our goals and engage teams across the company to develop carbon reduction strategies. This included adding an interim target to be 100% powered by renewables by 2026. We can’t do this alone. A large part of our indirect emissions is from our cloud partners, so we have partnered with them to see what we can achieve for our customers together. In addition to reducing our direct footprint, we are also focused on cleaner energy sources. By 2025 our major partners—AWS, Google Cloud (GCP), and Microsoft Azure—will be 100% powered by renewables. Following their example, we have entered into our first virtual power purchase agreement to support the construction of a new 10MW solar plant in Texas and add renewable energy to the grid. This is unique for a company our size and is evidence of our commitment to sustainability. By reducing our emissions, we can help our customers reduce their own carbon impact through MongoDB and the cloud. Enabling our customers to make greener choices We have heard from our customers that sustainability is important to them and influences their purchasing decisions. We looked at our own technology and have re-engineered MongoDB Atlas to reduce power consumption by ~30%. While moving to the cloud can have a positive impact on reducing carbon emissions, the actual amount depends on various factors, including the cloud provider, the type and amount of workloads, and the location of data centers. That's why we've introduced a new level of transparency to help our customers make more sustainable choices, including our Green Leaf icon in MongoDB Atlas that highlights low-carbon AWS and GCP cloud regions and encourages customers to consider the carbon impact of their choices. Additionally, MongoDB Atlas Serverless takes this one step further. Serverless infrastructure can help customers reduce their carbon footprint by reducing their infrastructure overhead and only using computing resources only when needed rather than constantly running. This means less energy is consumed overall, and less waste is created. Additionally, with MongoDB Atlas Serverless, customers can quickly scale up or down in response to changing demand, ensuring they're not wasting resources on idle infrastructure. To drive awareness and use of our products’ sustainability features, we have released a quick reference blog and detailed white paper on sustainable architecture. Finally, to help our customers understand how these changes impact their footprint, MongoDB will now include a note on attributable carbon emissions on our customers’ invoices. Changes in our offices, operations, and beyond We are making actionable changes to gain momentum towards our larger net-zero goal within our offices. We are switching over 100% of the lights in our offices to LEDs, have eliminated many single-use items, and are reducing power consumption by regulating usage outside of peak hours– this is as simple as putting Zoom Screens on timers. Additionally, we have introduced a Supplier Code of Conduct to ensure ESG compliance throughout our value chain. Finally, we have partnered with our Green Team ERG to enable employee engagement in our sustainability goals. Our Green Team fosters employee engagement by organizing community building and educational events centered around environmentalism and act as a voice for our employees' drive for corporate sustainability. As of this year employees can donate towards a project selected by our Green Team leads focused on reforestation through points earned in our employee recognition tool. We are just getting started, and our commitment to you is to be as transparent as possible throughout this process. We encourage you to follow our progress on our Sustainability webpage and check out our latest CSR report , which dives deeper into our emissions benchmark. At MongoDB, we believe that sustainability is everyone's responsibility, and we are committed to doing our part to create a more sustainable future.
The MongoDB World Tour is Coming to NYC
Last year marked our big return to live events with MongoDB World in NYC. For this year, MongoDB is hosting a series of events in cities all around the world that we're calling "dot local" because now the show comes to you. Our next stop is New York and it's approaching fast. If you live anywhere near NYC, you'll want to reserve a spot on your calendar for MongoDB.local NYC on June 22, a one-day conference on all things MongoDB, including new product announcements, hands-on workshops, and opportunities to learn how customers are using MongoDB to reinvent their businesses. Register today for MongoDB.local NYC, June 22, 2023 at the Javits Center in NYC. Register Now Live sessions on working with MongoDB The MongoDB user community represents a wide range of backgrounds and experiences. So we've tailored our sessions to cater to the diversity of our developer community. From data modeling and schema design to app-driven analytics and mobile data sync, our sessions will explore real-world scenarios and how to optimize MongoDB for performance, reliability, and scale. A few sessions from the full-day agenda include: Building Richer User Experiences Powered by Application-Driven Analytics From RDBMS to NoSQL at Enterprise Scale Implementing Time Series: Practical Use Cases Across Multiple Industries From MLOps to Generative AI: How to Bring ML Into Your Apps with MongoDB Be the first to hear about breaking news on the product front. We've been hearing from customers who are facing a lot of challenges today, including increasing data privacy and security requirements, as well as the pain of modernizing their applications. So we'll be announcing some recent solutions for de-risking application migration and modernization along with some very exciting product announcements that are enabling customers to build next-generation applications. In the keynote, members of our executive leadership team — MongoDB CEO Dev Ittycheria and Chief Product Officer Sahir Azam — will be announcing the latest features and updates for MongoDB, along with how they see developing modern applications shaping up over the next few years. Answers and opportunities abound One of the most popular activities at all MongoDB events is our Ask the Experts consultations. This is where attendees get one-to-one time with a MongoDB expert who helps them solve problems and find improvements they can make to their deployments. Don't miss this chance to uplevel your MongoDB skills in a live and personal setting. There's more than one way to get your questions answered at MongoDB.local. You can see live demos at the MongoDB booth, get hands-on learning at the MongoDB University booth, and engage in networking throughout the day and at a closing happy hour. So don't wait to register. Is .local coming to your city? MongoDB.local NYC is one of the first stops in our world tour. After the Big Apple, we're heading to Boston, Washington, D.C., London, Paris, Milan, and other major cities across the globe. We could even be visiting a city near you. Head to the MongoDB.local hub to see where we'll be showing up next. How to register We look forward to seeing you at MongoDB.local NYC . Our registration page has all you need to sign up for the conference, including a schedule of events, speaker profiles, and attendee resources, like how to justify your trip and travel details. So don't wait, register today! Register Now for MongoDB.local NYC
MongoDB Atlas Expands Globally with AWS
We’re proud to announce our global expansion of MongoDB Atlas on AWS (Amazon Web Services) in the Middle East, Europe, and APAC. The launch of regions in the United Arab Emirates (UAE), Zurich, Spain, Hyderabad, and Melbourne expands availability of MongoDB Atlas to 27+ AWS regions around the world. The UAE region is an AWS Recommended Region , meaning it has three Availability Zones (AZ), bringing significant infrastructure to the Middle East. When you deploy a cluster in the UAE, Atlas automatically distributes replicas to the different AZs for higher availability. If there’s an outage in one zone, the Atlas cluster will automatically fail over to keep running in the other two. And you can also deploy multi-region clusters with the same automatic failover built-in. We’re delighted that — as with customers in Bahrain, Cape Town, and more — United Arab Emirates organizations will now be able to keep data in their own country, delivering low-latency performance and ensuring confidence in data locality. UAE customers in government, financial services, and utilities in particular will benefit from this expansion. In addition to the launch in the UAE region, MongoDB Atlas is now available in Zurich and Spain, expanding to our already strong presence in the EMEA and giving our customers the ability to build and run applications with data sovereignty requirements for the region. MongoDB was awarded AWS Marketplace Partner of the Year - EMEA for 2022, and we are committed to continuing to make Atlas easily accessible across the region. Our expansion in APAC is also particularly exciting given the recent momentum of MongoDB Atlas on AWS in the region. Increased availability in India and Australia will help to secure the opportunity for APAC developers to have wider access to build with high performance. Companies like Open Government Products, Bendigo&Adelaide, Cathay Pacific, Dongwha, and Kasikorn will benefit from closer availability zones. We’re confident our developers around the world will appreciate this capability as they build tools to improve citizens’ lives and better serve their local users. Get started with MongoDB Atlas for free today on AWS Marketplace Learn more about MongoDB Atlas on AWS
Temenos Banking Cloud Scales to Record High Transactions with MongoDB Atlas and Microsoft Azure
Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post. Banking used to be a somewhat staid, hyper-conservative industry, seemingly evolving over eons. But the emergence of Fintech and pure digital players in the market paired with alternatives in technology is transforming the industry. The combination of MACH , BIAN and composable designs enables true innovation and collaboration within the banking sector, and the introduction of cloud services makes these approaches even easier to implement. Just ask Temenos, the world's largest financial services application provider, providing banking for more than 1.2 billion people . Temenos is leading the way in banking software innovation and offers a seamless experience for their client community in over 150 countries. Temenos embraces a cloud-first, microservices-based infrastructure built with MongoDB, giving customers flexibility, while also delivering significant performance improvements. Financial institutions can embed Temenos components, like Pay-as-you-go, which delivers new functionality to their existing on-premises environments, on their own cloud deployments or through a full banking as a service experience with Temenos Transact powered by MongoDB on various cloud platforms. This new MongoDB-based infrastructure enables Temenos to rapidly innovate on its customers' behalf, while improving security, performance, and scalability. Fintech, payments and core banking Temenos and MongoDB joined forces in 2019 to investigate the path toward data in a componentized world. Over the past few years, our teams have collaborated on a number of new, innovative component services to enhance the Temenos product family, and several banking clients are now using those components in production. However, the approach we've taken allows banks to upgrade on their own terms. By putting components “in front” of the Temenos Transact platform , banks can start using a componentization solution without disrupting their ability to serve existing customer requirements. From May 2023 onwards, banks will have the capability to deploy Temenos Infinity microservices as well as the core banking Temenos Transact exclusively on the developer data platform from MongoDB and derive even more value. Making the composable approach even more valuable, Temenos implemented their new data backend firmly based on JSON and the document model . MongoDB allows fully transparent access to data and the exploitation of additional features of the developer data platform. These features include Atlas Search , application-driven analytics , and AI through workload isolation. Customers also benefit from the geographic distribution of data based solely on the customer requirements, be it in a single country driven by sovereignty requirements or distributed across continents to ensure always-on and best possible data access and speed for trading. Improved performance and scale In contrast to the retail-centric benchmark last year , the approach this time was to test broader functionality and include more diverse business areas – all while increasing the transaction volume by 50%. The benchmark scenario simulated a client with 50 million retail customers, 100 million accounts and a Banking-as-a-Service (BaaS) offering for 10 brands and 50 million embedded finance customers on a single cloud instance. In the test, Temenos Banking Cloud processed 200 million embedded finance loans and 100 million retail accounts at a record-breaking 150,000 transactions per second. In doing so, Temenos proved its robust and scalable platform can support banks’ business models for growth through BaaS or distributing their products themselves. The benchmark included not just core transaction processing, but a composed solution combining payments, financial crime mitigation (FCM), a data hub, and digital channels. "No other banking technology vendor comes close to the performance and scalability of Temenos Banking Cloud. We consistently invest more in cloud technologies and have more banks live with core banking in the cloud than any of our peers. With global non-cash transaction volumes skyrocketing in response to fast-emerging trends like BaaS, banks need a platform that allows them to elastically scale based on business demand, provide composable capabilities on-demand at a low cost, while reducing their environmental impact. This benchmark with Microsoft and MongoDB proves the capability of Temenos’ platform to power the world’s biggest banks and their BaaS offerings with hundreds of millions of customers, efficiently and sustainably in the cloud." Tony Coleman, Chief Technology Officer, Temenos This solution landscape reflects an environment where everyone on the planet runs two banking transactions a day on a single bank. This throughput should cater to any Tier 1 banking deployment, in size and performance, and cover any future growth plans that they have. Below are the transaction details that comprise the actual benchmark mix. As mentioned above it is a broad mix of different functionalities behaving like a retail bank and a fintech institute, which provides multiple product brands, e.g. cards for different retails. Besides the sheer performance of the benchmark, the ESG footprint of the overall landscape shrunk again versus last year’s configuration as the MongoDB Atlas environment was the sole database and no secondary systems were required. Temenos Transact optimized with MongoDB The JSON advantage Temenos made significant engineering efforts to decapsulate the data layer, which was previously stored as PIC, and make JSON formatted data available to their user community. MongoDB was designed from its inception to be a database focused on delivering a great development experience. JSON’s ubiquity made it the obvious choice for representing data structures in MongoDB’s document data model. Below you can see how Temenos Transact stores data vs Oracle or MSSQL vs MongoDB. Temenos and MongoDB have an aligned data store – Temenos Transact application code operates on documents (JSON) and MongoDB stores documents in JSON in one place, making it the perfect partnership. MongoDB enables the user community through its concept of additional nodes in the replica set to align further secondary applications integrated into the same database without interrupting and disturbing the transactional workload of Temenos Transact. The regular occurring challenge with legacy relational database management systems (RDBMS) where secondary applications suddenly have unexpected consequences to the primary application is a problem of the past with MongoDB. Workload Isolation with MongoDB MongoDB Atlas will operate in most cases in three availability zones, where two zones are located in the same region for pure availability and a single node is located in a remote region for disaster recovery. This environment provides the often required RPO/RTO “0” while delivering unprecedented performance. Two nodes in each of the first availability zones provision the transactional replica set and ensure the consistency and operation of the Temenos Transact application. In each availability zone, a third isolated workload node is co-located with the same data set as the other two nodes but is excluded from the transactional processing. These isolated workload nodes provide capacity for additional functionalities. In the example above, one node provides access to the MongoDB Atlas Federation and a second node provides the interface for MongoDB Atlas Search. As the nodes store data in near real-time – replication is measured in sub milliseconds as they are in the same availability zone – this allows exciting new capabilities like real-time large language model (LLM), e.g. ChatGPT, or machine learning connecting to a Databricks lake house. The design is discussed in more detail in this article . The below diagram shows a typical configuration for such a cluster setup in the European market for Microsoft Azure: one availability zone in Zurich, one availability zone in Geneva, and an additional node out of both in Ireland. Additionally, we configured isolated workloads in Zurich and Geneva. MongoDB Atlas allows the creation of such a cluster within seconds, configured to the specific requirements of the solution deployed. Typical configuration for a cluster setup for the European market for Microsoft Azure Should the need arise, MongoDB can have up to 50 nodes in a single replica set so for each additional isolated workload, one or more nodes can be made available when and where needed. Even at locations beyond the initial three chosen! For this benchmark the use of a MongoDB Atlas cluster M600 was utilized which was oversized based on the CPU utilization of 20-60% depending on the node type. Looking backward a smaller MongoDB Atlas M200 would have been easily sufficient. Nonetheless MongoDB Atlas delivered the needed database performance with one third of the resources of last year's result, but delivering 50% more throughput. Additionally MongoDB Atlas performed two times faster in throughput per transaction (measured in milliseconds). Signed, sealed, and delivered. This benchmark gives clients peace of mind that the combination of core banking with Temenos Transact and MongoDB is ready to support the needs of even the largest global banks. While thousands of banks rely on MongoDB for many parts of their operations ranging from login management and online banking, to risk and treasury management systems, Temenos' adoption of MongoDB is a milestone. It shows that there is significant value in moving from a legacy database technology to MongoDB, allowing faster innovation, eliminating technical debt along the way, and simplifying the landscape for financial institutions, their software vendors, and service providers. PS: We know benchmarks can be deceiving and every scenario in each organization is different. Having been in the benchmark business for a long time, you should never trust just ANY benchmark. In fact, my colleague, MongoDB distinguished engineer John Page, wrote a great blog about how to benchmark a database . If you would like to learn more about how you can use MongoDB to move towards a composable system, architecting for real-time adaptability, scalability, and resilience, take a look at the below resources: Componentized core banking built upon MongoDB Tony Coleman, CTO at Temenos and Boris Bialek, Global Head, Industry Solutions at MongoDB discuss the partnership at MongoDB World 2022 Remodel your core banking systems with MongoDB
Application-Driven Analytics: Why are Operational and Analytical Workloads Converging?
Fifteen years ago, our vision was to provide developers a new approach to databases. As industry change is constant, we are working to bring you another shift so you can stay ahead of the curve – application-driven analytics. Application-driven analytics isn’t about replacing your centralized data warehouse or data lakehouse. Rather it’s about augmenting them; bringing a new class of analytics directly into applications where they are built by developers. In his recent Analyst Perspective, Matt Aslett, VP & Research Director at Ventana Research was very clear there remains different functional requirements for dedicated operational and analytical systems. However, he noted the growth in intelligent applications infused with the results of analytic processing. This in turn is driving operational / OLTP data platforms such as MongoDB to integrate native analytics functionality. In the Perspective, Aslett goes on to describe some of the recent product enhancements introduced by MongoDB to support analytics, and wraps up with this advice: I recommend that organizations evaluating potential database providers for new, intelligent operational applications include MongoDB Atlas in their evaluations. If you are interested in learning more about Ventana Research’s insights, take a look at the company’s Analyst Perspective: MongoDB’s Atlas Delivers Data-Driven Applications . From manufacturing to retail and finance Beyond the research from industry analysts, organizations are increasingly working to capture the opportunities presented by application-driven analytics. Bosch Global Software uses MongoDB at the core of its IoT systems powering automotive, industrial, and smart home use cases. Being able to generate analytics in real time is a key capability of the company’s applications. As discussed in his recent article in The New Stack , Kai Hackbarth, senior technology evangelist at Bosch, talked about the value MongoDB provides: From my history [of doing this for] 22 years, we never had the capabilities to do this before. Global retailer Marks and Spencer rebuilt its Sparkes rewards program , moving from a packaged app to an in-house solution with MongoDB. The company reduced its time to build one million personalized customer offers from one hour to just five minutes. It is now able to serve those offers at 10x lower latency with the loyalty program driving 8x higher customer spend. As part of its digital transformation initiative, Toyota Financial Services built a new operational data layer powered with MongoDB Atlas . The data layer connects the company’s internal mainframe backend systems with customers engaging the company through new digital channels. MongoDB handles customer onboarding along with fraud prevention. The native OLTP and analytics capabilities provided by MongoDB Atlas were key. They eliminated the need for Toyota Financial Services to integrate and build against separate database, cache, object store, and data warehouse technologies. All dramatically simplifying the company’s technology estate. MongoDB helps us make better decisions and build better products. Ken Schuelke, Division Information Officer, Toyota Financial Services Enabling developers for application-driven analytics How is MongoDDB helping developers make the shift to smarter apps and intelligent software? MongoDB Atlas unifies the core transactional and analytical data services needed to deliver app-driven analytics. It puts powerful analytics capabilities directly into the hands of developers in ways that fit their workflows. With Atlas, they land data of any structure, index, query, and analyze it in any way they want, and then archive it. All while working with a unified API and without having to build their own data pipelines or duplicate data. MongoDB Atlas supports any level of application intelligence. From querying and searching records to aggregating and transforming data through to feeding rules-based engines and machine learning models. Figure 1: MongoDB Atlas combines transactional and analytical processing in a multi-cloud data platform. Atlas automatically optimizes how data is ingested, processed, and stored, maximizing the efficiency of the application’s operational and analytical workloads. These capabilities are packaged in an elegant and integrated multi-cloud data architecture. Getting started There are many ways you can get started in building more intelligent apps. If you want to read more about the use-cases and business drivers, download our App-Driven Analytics whitepaper . Alternatively if you want to dive straight in, sign up for an account on MongoDB Atlas . From there, they can 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.
What's New in Atlas Charts: Suggested Charts, Auto Activation, and Contextual Help
Atlas Charts is the native data visualization tool for quickly and easily analyzing your data in MongoDB Atlas. Today, we’re announcing a collection of updates that further streamline the Charts experience: Suggested charts: a quicker way to build visualizations More contextual help in the chart builder Automatic Charts activation for all project members Suggested charts Charts has always offered a simple UI with an easy to use, drag and drop interface that lets you quickly build charts and visualize your application data. However, we still found that some users could benefit from extra help when building out new charts. Rather than starting from an empty screen where you need to drag appropriate fields into the chart type selected, what if you could simply select an automatically suggested chart, and start applying customization from there? That’s exactly what suggested charts offer. We experimented with this feature late last year and now we have turned it on for everyone! Simply add a chart into one of your dashboards to try it out today. Figure 1: Using the new auto suggested charts in the chart builder. Help button in the chart builder As you might expect, the chart builder is where you do your chart creation. Similar to suggested, last year we experimented with ways to provide more contextual help for users when building new charts. Now, we are surfacing helpful docs articles to educate users on key chart building topics like: filtering, adding fields, selecting the right chart type, and more. Sometimes it can be intimidating to know exactly what chart to use and how to achieve the style and customization you want – the help button in chart builder will make this much easier. Building a chart and have a question? Just click into the Get help button and check out one of our highlighted topics, or choose View all topics to read the main Charts documentation. Streamlining Charts activation We’re constantly looking for ways to help Atlas users with data visualization quicker. So starting with this latest release, when you click into the Charts tab from the Atlas UI, you will automatically be set up to start visualizing your data – no activation required. Additionally, Atlas users browsing collections within a specific cluster, can now more quickly navigate directly into Charts for quick visualization. When viewing a collection, the Visualize your data button, seamlessly opens in the chart builder with the current collection selected as the chart’s data source. Paired with the new suggested chart, users see a list of chart suggestions to quickly and easily build a relevant chart based on their collection data. Note: you may see a “Charts” tab in the collection view instead of the Visualize your data button, as shown below. This is due to an experiment we are currently running. FIgure 3: Seamlessly navigate from an Atlas collection into the chart builder in Atlas Charts. This is a continuation of our effort to optimize the overall Charts experience. Last year we made strides in this area by introducing features like streamlined data sources and org-wide sharing . Keep on the lookout for more Charts features that further simplify your experience visualizing Atlas data across your team. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.