Product Updates
The most recent MongoDB product releases and updates
Elevate Your Python AI Projects with MongoDB and Haystack
MongoDB is excited to announce an integration with Haystack, enhancing MongoDB Atlas Vector Search for Python developers. This integration amplifies our commitment to providing developers with cutting-edge tools for building AI applications centered around semantic search and Large Language Models (LLMs). We’re excited to partner with MongoDB to help developers build top-tier LLM applications. The new Haystack and MongoDB Atlas integration lets developers seamlessly use MongoDB data in Haystack, a reliable framework for creating quality LLM pipelines for use cases like RAG, QA, and agentic pipelines. Whether you're an experienced developer or just starting, your gen AI projects can quickly progress from prototype to adoption, accelerating value for your business and end-users. Malte Pietsch, co-founder and CTO, deepset Simplifying AI app development with Haystack Haystack is an open-source Python framework that simplifies AI application development. It enables developers to start their projects quickly, experiment with different AI models, and to efficiently scale their applications. Indeed, Haystack is particularly effective for building applications requiring semantic understanding and natural language processing (NLP), such as chatbots and question-answering systems. Haystack’s core features include: Components: Haystack breaks down complex NLP tasks into manageable components, such as document retrieval or text summarization. With the new MongoDB-Haystack integration, MongoDB becomes the place where all your data lives, ready for Haystack to use. Pipelines: Haystack lets you link components together into pipelines for more complex tasks. With this integration, your MongoDB data flows through these pipelines. Agents: Haystack Agents use LLMs to resolve complex queries. They can decide which tools (or components) to use for a given question, leveraging MongoDB data to deliver smarter answers. Atlas Vector Search: Enhance AI development with Haystack At the heart of the new integration is MongoDB Atlas Vector Search , transforming how applications search and retrieve data. By leveraging vector embeddings, Atlas Vector Search goes beyond mere keyword matching: it interprets the intent behind queries, enabling applications to provide highly relevant, context-aware responses. This is a breakthrough for Python developers who aim to build applications that think and understand like humans. Building on this foundation, the Atlas Vector Search and Haystack integration gives Python developers a powerful toolkit for navigating the complexities of AI application development. MongoDB becomes a dynamic document store within Haystack's framework, optimizing data storage, processing, and retrieval. Additionally, the integration eases the use of advanced AI models from leading providers such as OpenAI and Cohere into your applications. Developers can thus create applications that do more than just answer queries—they grasp and act on the underlying intent, ensuring responses are both accurate and contextually relevant. What this means for Python developers For Python developers, this integration means: Faster development: Developers can focus on building and innovating rather than spending time configuring and managing infrastructure. MongoDB's integration with Haystack means you can get up and running quickly, leveraging the best of both technologies to accelerate your development cycles. Smarter applications: By utilizing Haystack's powerful Natural Language Processing tooling in combination with MongoDB Atlas Vector Search’s efficient data handling, developers can create applications that understand and process natural language more effectively. This results in applications that can provide more accurate and contextually relevant responses that resonate with user intent. Access to pre-trained AI models: With seamless integration of leading generative AI models from providers like OpenAI, Anthropic, Cohere, Hugging Face, and AWS Bedrock, Python developers can easily incorporate advanced AI functionalities into their projects. This means developers can quickly adopt state-of-the-art models without the need for extensive training or fine-tuning, saving time and resources. Flexible and scalable pipelines: Haystack's modular approach to building AI applications, through its use of components and pipelines, allows developers to create flexible and scalable solutions. With MongoDB data seamlessly flowing through these pipelines, you can easily adapt and expand your applications to meet growing demands and new challenges. Robust search capabilities: Atlas Vector Search transforms the way applications retrieve and interpret data, going beyond simple keyword searches. It enables applications to perform high-precision searches that return more relevant and semantically rich results. This advanced search capability is crucial for developing applications that require high levels of semantic understanding and accuracy. By integrating MongoDB with Haystack, Python developers are equipped with a powerful toolkit that not only simplifies the AI development process but also significantly enhances the intelligence and functionality of their applications. Whether you are building chatbots, search engines, or other AI-driven applications, this integration provides the tools you need to create innovative and impactful solutions. Get started now Start leveraging the MongoDB and Haystack integration for your AI development. Explore our tutorial , documentation , or check out our github repository to begin building smarter, more intuitive Python projects today!
Exact Nearest Neighbor Vector Search for Precise Retrieval
With its ability to efficiently handle high-dimensional, unstructured data, vector search delivers relevant results even when users don’t know what they’re looking for and uses machine learning models to find similar results across any data type. Rapidly emerging as a key technology for modern applications, vector search empowers developers to build next-generation search and generative AI applications faster and easier. MongoDB Atlas Vector Search goes beyond the approximate nearest neighbor (ANN) methods with the introduction of exact nearest neighbor (ENN) vector search . This innovative capability guarantees retrieval of the absolute closest vectors to your query, eliminating the accuracy limitations inherent in ANN. In sum, ENN vector search can help you unleash a new level of precision for your search and generative AI applications, improving benchmarking and moving to production faster. When exact nearest neighbor (ENN) vector search benefits developers While ANN shines in searching across large datasets, ENN vector search offers advantages in specific scenarios: Small-scale vector data: For datasets under 10,000 vectors, the linear time complexity of ENN vector search makes it a viable option, especially considering the added development complexity of tuning ANN parameters. Recall benchmarking of ANN queries: ANN queries are fast, particularly as the scale of your indexed vectors increases, but it may not be easy to know whether the retrieved documents by vector relevance correspond to the guaranteed closest vectors in your index. Using ENN can help provide that exact result set for comparison with your approximate result set, using jaccard similarity or other rank-aware recall metrics. This will allow you to have much greater confidence that your ANN queries are accurate since you can build quantitative benchmarks as your data evolves. Multi-tenant architectures: Imagine a scenario with millions of vectors categorized by tenants. You might search for the closest vectors within a specific tenant (identified by a tenant ID). In cases where the overall vector collection is large (in the millions) but the number of vectors per tenant is small (a few thousand), ANN's accuracy suffers when applying highly selective filters. ENN vector search thrives in this multi-tenant scenario, delivering precise results even with small result sets. Example use cases The small dataset size allows for exhaustive search within a reasonable timeframe, making exact nearest neighbor approach a viable option for finding the most similar data point, improving accuracy confidence in a number of use cases, such as: Multi-tenant data service: You might be building a business providing an agentic service that understands your customers’ data and takes actions on their behalf. When retrieving relevant proprietary data for that agent, it is critical that the right metadata filter be applied and that ENN be executed to retrieve the right sets of documents only corresponding to the appropriate data tenant IDs. Proof of concept development: For instance, a new recommendation engine might have a limited library compared to established ones. Here, ENN vector search can be used to recommend products to a small set of early adopters. Since the data is limited, an exhaustive search becomes practical, ensuring the user gets the most relevant recommendations from the available options. How ENN vector search works on MongoDB Atlas The ENN vector search feature in Atlas integrates seamlessly with the existing $vectorSearch stage within your Atlas aggregation pipelines. Its key characteristics include: Guaranteed accuracy: Unlike ANN, ENN always returns the closest vectors to your query, adhering to the specified limit. Eventual consistency: Similar to approximate vector search, ENN vector search follows an eventual consistency model. Simplified configuration: Unlike approximate vector search, where tuning numCandidates is crucial, ENN vector search only requires specifying the desired limit of returned vectors. Scalable recall evaluation: Atlas allows querying a large number of indexed vectors, facilitating the calculation of comprehensive recall sets for effective evaluation. Fast query execution: ENN vector search query execution can maintain sub-second latency for unfiltered queries up to 10,000 documents. It can also provide low-latency responses for highly selective filters that restrict a broad set of documents into 10,000 documents or less, ordered by vector relevance. Build more with ENN vector search ENN vector search can be a powerful tool when building a proof of concept for retrieval-augmented generation (RAG), semantic search, or recommendation systems powered by vector search. It simplifies the developer experience by minimizing overhead complexity and latency while giving you the flexibility to implement and benchmark precise retrieval. Explore more use cases and build applications faster, start experimenting with ENN vector search.
Announcing MongoDB Server 8.0 Platform Support Improvements
Last month at MongoDB.local NYC 2024, we announced the preview of MDB 8.0 , the next evolution of MongoDB’s modern database. With MongoDB 8.0, we’re focused on delivering the unparalleled performance, scalability, and operational resilience necessary to support the creation of next-generation applications. For that to be possible, users must be able to deploy MongoDB on industry-standard operating systems. As a result, we are updating our Server Platform Policy to ensure that customers have the best possible experience when using MongoDB. Starting in MongoDB 8.0, there will be two new changes: When a new major version of MongoDB is released, we will only release it operating system (OS) versions that are fully supported by the vendor for the duration of the MongoDB version’s life. In short, we will support an operating system if the operating system’s Extended Lifecycle Support (ELS) date is after the MongoDB Server’s End of Life (EOL) date. We will release new MongoDB Server versions (both major and minor) on the minimum supported minor version of the OS (defined by the OS vendor). Once an OS minor version is no longer supported by the vendor, we will update future MongoDB Server versions to the next supported OS minor version. As always, MongoDB reserves the right to discontinue support for platforms based on lack of user demand and/or technical difficulties (e.g., if a platform doesn’t support required libraries or compiler features). Ensuring best-in-class security MongoDB routinely updates our documentation to indicate which platforms a new version of the MongoDB Server will be available on with the general availability release of that new server version. To ensure that MongoDB customers can meet strong regulatory and security requirements, our software is developed, released, and distributed in accordance with industry security best practices. Given the mission-critical nature of MongoDB’s business—providing a highly secure, performant data platform to tens of thousands of customers in over 100 countries—we strive to provide strong and consistent security assurances across all of our products. In addition, MongoDB partners also need guarantees about the security development lifecycle of our products so they can provide the best experience to their customers. By ensuring that our software runs only on platform versions that are receiving security patches, we aim to limit the vulnerabilities that might be introduced by customers running EOL operating systems. The significance of this change With every major server release, MongoDB determines the supported builds for that general availability (GA) release according to the planned vendor platform’s end of life date —meaning the MongoDB major release will not support the operating system if the operating system’s extended lifecycle support ends before the MongoDB EOL date. This also applies to server container images delivered to our customers. Furthermore, to guarantee security assurances for operating systems that have a minimum minor version, we will only build new versions of MongoDB Server software on a vendor-supported major/minor version of the operating system. Concretely, we will build new versions of MongoDB on a minimum minor version until it hits a maintenance event (defined on a per-vendor basis), and at that point future MongoDB server builds will be updated to the new supported minor version. Separately, when a vendor publishes a new major version of an operating system after a given version of MongoDB reaches GA, we will evaluate whether the latest MongoDB release will run on this new OS version, or we will wait for the next major MongoDB release before documenting formal platform support on our website. Walkthrough: How it could work for you Consider the RHEL 9 Planning Guide below and the hypothetical release cadence of MongoDB version X.0. As long as version X.0 is released three years before the end of RHEL 9 support, which as noted by RHEL is 2032 , we will provide support on RHEL 9. This means that 2029 will be the last year that MongoDB releases a server version on RHEL 9. Next, consider that version X.0 will be released at the end of 2025. Following the Extended Update Support Plan, we will build version X.0 on RHEL 9.6 until the start of 2026 when RHEL 9.8 becomes available. And then for future versions, MDB X.Y will begin being built on RHEL 9.8 until we require the minimum version to be 9.10 in 2027. RHEL 9 planning guide Building the future Overall, these coming changes to the MongoDB Server Platform Policy underscore MongoDB’s commitment to helping developers innovate quickly and easily while providing an even more highly secure and performant data platform. Stay tuned for additional updates about MongoDB 8.0—which will provide optimal performance by dramatically increasing query performance, improving resilience during periods of heavy load, making scalability easier and more cost-effective, and making time series collections faster and more efficient. For more information about the Server Platform Policy updates, please refer to our documentation .
Leveraging Database Observability at MongoDB: Unlocking Performance Insights and Optimization Strategies
This post is the first in a three-part series on leveraging database observability. Observability has evolved into an essential information technology component, offering advanced insights into system performance beyond traditional monitoring. While monitoring aims to identify problems, observability helps understand and resolve them. Businesses prioritizing observability experience less downtime, leading to enhanced user experiences and improved ROI. Indeed, Splunk’s The State of Observability 2023 report quantified the financial impact of downtime—more than $150,000 per hour. Furthermore, observability leaders reported 33% fewer outages and achieved eight times better ROI than new adopters. Throughout this series, we'll define database observability at MongoDB, explore our suite of tools, delve into third-party integrations, and discuss everyday use cases. We will also establish a shared methodology and vocabulary for discussing observability at MongoDB and highlight the tools and features that have delivered value for our customers. Observability and MongoDB’s strategy Monitoring involves using tools to track real-time operations and alert teams to issues. As defined by Gartner, observability evolves monitoring into a process that provides deep insights into digital business applications, enhancing innovation and customer experience. The key difference is that monitoring detects the presence of issues, while observability gathers detailed information to understand and resolve them, which is crucial for modern IT infrastructure needs. Databases, in particular, play a critical role in this IT ecosystem, where performance and resilience directly impact business outcomes. This advancement is essential for DevOps, Database Administrators, and economic buyers responsible for these databases, as it enhances system reliability, encourages innovation, and supports financial objectives. Ultimately, observability provides comprehensive insights into system performance, health, and reliability by seamlessly integrating and contextualizing telemetry data. MongoDB leverages a unique observability strategy with out-of-the-box tools that automatically monitor and optimize customer databases. Explicitly designed for MongoDB environments, our system provides continuous feedback and answers critical questions—What is happening? Where is the issue? Why is it occurring? How do I fix it?—to enhance performance, increase productivity, and minimize downtime. Supporting MongoDB Atlas (our fully managed platform), Cloud Manager, and Ops Manager, as well as tailored monitoring solutions for the full range of developer data platform products (from enhanced search functionalities to app services and search nodes). Our approach meets the evolving needs of customer applications by: Leveraging MongoDB expertise: The MongoDB observability suite integrates efficiency and best practices from the beginning of the development cycle. As MongoDB platform experts, we use our deep knowledge to provide top-tier optimization insights. We apply our extensive understanding of our tools to ensure our customers benefit from a high-performing and resilient database. Offering streamlined metrics: We integrate our metrics seamlessly into our customers' central observability stacks and workflows. This creates a 'plug-and-play' experience that effortlessly aligns with popular monitoring systems like Datadog, New Relic, and Prometheus. Thus, it provides a unified view of customer application performance and deep insights into their database within a comprehensive dashboard. Breaking down MongoDB’s observability offerings Tailored database performance management MongoDB employs automated tools for comprehensive database performance management, focused on real-time optimization, strategic scaling, and best practices in schema design. Using out-of-the-box tools ensures high-performing, scalable, and cost-efficient database environments ideal for modern applications. Key features include: Performance Advisor : Provides index recommendations to improve read and write performance, significantly boosting overall efficiency. Schema Advisor : Supports flexible schema design and query execution analysis to enhance performance, scalability, and validation rules for schema compliance. Opt-in Autoscaling (only available in Atlas): Optimizes resource use, manages operational costs, ensures continuous availability, and adjusts resources based on demand, preventing downtimes. Performance Advisor in action Foundational monitoring for in-depth insights MongoDB provides foundational monitoring tools and out-of-the-box insights for optimal cluster health and performance after initial database setup. These tools both help reduce the burden of performing manual tasks while laying the groundwork for detailed and granular analysis of metrics and system performance aimed at enhancing query performance, reducing execution times, and lowering resource usage. These tools include: Monitoring Charts : These charts offer detailed metrics on hardware, database operations, replication status, sharded, and search/vector search with a fine-grained metric resolution to identify issues and track trends. Real-Time Performance Panel : This panel displays live network traffic, database operations, and hardware stats, helping to identify critical operations, evaluate query times, and monitor network load and throughput. Query Insights : The recently announced Namespace Insights provides users with collection-level latency statistics. At the same time, the enhanced cluster-centric Query Profiler gives an expanded view of query performance, significantly enhancing visibility and operational efficiency across the cluster. Learn more about both! Comprehensive alerting and seamless integrations MongoDB Atlas's sophisticated alerting system offers over 200 event types, providing teams with comprehensive control and visibility over their environments. Users can fine-tune their alerting strategy with customization options to fit their specific requirements. Additionally, MongoDB Atlas enhances team collaboration and ensures a unified view of application performance through seamless integrations with third-party tools like Slack, PagerDuty, and DataDog. These integrations simplify management tasks and leverage existing workflows for optimum operational effectiveness. What’s next? Enhanced database observability Observability is more than just a technical requirement—it's a strategic asset that enhances operational efficiency and economic viability. Through MongoDB's observability suite, organizations can optimize and scale system performance and fuel innovation. MongoDB is dedicated to continuously improving this suite to manage large-scale data better and meet demanding performance standards. Our commitment is reflected in our efforts to advance MongoDB's observability features, providing specific insights that deliver actionable intelligence tailored to our customers' needs. Look for the next post in this series, where we'll explore various tools and their integration, illustrated through common use cases. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. For more information, see Monitor Your Database Deployment .
Stay Compliant with MongoDB’s Latest Certifications: ISO 9001, TISAX, HDS, and TX-RAMP
Ensuring compliance with regulations and security standards across industries and regions is a crucial aspect of MongoDB’s commitment to protecting customer data. That’s why we’re excited to announce that MongoDB Atlas has achieved certifications for ISO 9001, TISAX, HDS, and TX-RAMP, further solidifying our dedication to data security and regulatory compliance for both enterprise and public sector organizations. MongoDB Atlas achieved these certifications across AWS, Azure, and Google Cloud supported regions, thus providing customers the flexibility to adopt a multi-cloud model to support their workloads. In order to achieve each of these four new certifications, MongoDB Atlas underwent independent verification of its quality management, platform security, privacy, documentation, and organizational controls. These certifications—and the independent verifications required to achieve them—help ensure that Atlas meets organizations’ compliance, regulatory, and policy objectives, including the unique compliance needs of highly regulated industries. Read on to learn more about MongoDB’s new ISO 9001, TISAX, HDS, and TX-RAMP certifications, and how they can benefit organizations of all sizes. ISO 9001 Developed by the International Organization for Standardization (ISO), ISO 9001:2015 is an international standard for quality management systems (QMS) that is widely recognized across industries and organizations of all sizes. It provides a framework and guiding principles to systematically deliver products and services at consistently high quality to customers while ensuring compliance with regulations. MongoDB Atlas’s ISO 9001:2015 certification provides assurance to customers that we have implemented a robust QMS and are committed to consistently meeting their requirements and complying with all applicable regulations. It also emphasizes the importance of process control and continual improvement at MongoDB, which leads to greater consistency in product or service quality over time. Visit the Trust Center to learn more about MongoDB's ISO 9001 certification . TISAX The Trusted Information Security Assessment Exchange, or TISAX, is a certification program for information security in the automotive industry. Based on information security requirements created by the German Association of the Automotive Industry (VDA), TISAX helps European automotive companies streamline security evaluations by providing an industry-specific security framework for assessing information security for the wide landscape of suppliers, OEMs, and partners that contribute to the automotive supply chain. There are three assessment levels of TISAX certification. MongoDB has demonstrated compliance with the assessment level 3 (AL3) TISAX certification, which is the highest assessment level available and signifies a supplier's ability to handle and protect highly sensitive data, while also maintaining high availability. MongoDB Atlas's TISAX certification assists automotive industry customers in meeting their rigorous compliance needs. Additionally, it assures these customers that their data will be safeguarded to the highest standards within MongoDB Atlas, with robust measures in place for business continuity, disaster recovery, and risk mitigation. Visit the Trust Center to learn more about MongoDB’s TISAX certification . HDS France's HDS regulations and certification, known as Hébergeur de Données de Santé (Health Data Hosting), ensure organizations comply with basic requirements for hosting personal health data. According to the French Public Health Code, any organization hosting health data from healthcare activities in France must obtain HDS certification. By securing HDS certification, MongoDB Atlas helps those customers hosting health data in France to comply with HDS regulations and instills confidence that robust security controls and practices are in place to protect this highly sensitive data. Visit the Trust Center to learn more about MongoDB’s HDS certification . TX-RAMP The Texas Risk and Authorization Management Program, or TX-RAMP, is a certification program established in Texas that ensures the security of cloud computing services used by state governmental agencies. In order to demonstrate compliance with the security criteria required for TX-RAMP certification, MongoDB Atlas was assessed by the Texas Department of Information Resources (DIR). MongoDB Atlas for Government is already TX-RAMP certified by virtue of it being FedRAMP Moderate authorized. By securing TX-RAMP certification, MongoDB Atlas simplifies procurement for public sector customers in Texas seeking to adopt MongoDB Atlas on AWS, Azure, or Google Cloud. Visit the Trust Center to learn more about MongoDB’s TX-RAMP certification . MongoDB is dedicated to securing your data. We do so through state-of-the-art technical and organizational security controls, numerous regulatory and compliance resources, and a constantly growing collection of third-party attestations and certifications. Our new ISO 9001, TISAX, HDS, and TX-RAMP certifications help us ensure compliance with regulations and security standards across diverse industries and regions, both for enterprise and public sector organizations. To learn more about MongoDB’s technical and organizational security measures, visit the Technical and Organizational Security Measures page .
What’s New From MongoDB at Microsoft Build 2024
This week, thousands of engineers, database administrators, and developers are gathering in Seattle for Microsoft Build , Microsoft’s annual developer event. In addition to being on site for meetings and learning sessions, MongoDB is excited to showcase our latest innovations for building generative AI apps and more. First, we’re happy to announce that MongoDB now offers dedicated Search Nodes on Microsoft Azure . We offer both compute-optimized nodes for text or application search workloads, and memory-optimized nodes for vector, semantic search, or gen AI workloads. Search Nodes enhance performance and availability through workload isolation while reducing architectural complexity. The availability of Search Nodes on Azure is the latest example of how the partnership between MongoDB and Microsoft helps organizations of all sizes boost developer productivity and build modern applications faster. Keep reading for more on how MongoDB’s capabilities and integrations with Microsoft are helping customers create, innovate, and scale applications. Integrating services and technology to speed AI development The last year of AI innovation set a clear imperative for every organization—to meet customer expectations, they need to modernize their applications. However, many companies aren’t sure where to start with AI, so MongoDB recently announced the launch of the MongoDB AI Application Program (MAAP) alongside industry-leading AI partners. MAAP will provide customers with strategic advisory, professional services, and an integrated end-to-end technology stack from MongoDB and key partners like Microsoft. We’ve also made several technology announcements to enable building gen AI applications, including native support for MongoDB Atlas Vector Search in Microsoft Semantic Kernel , and a dedicated MongoDB Atlas integration for OpenAI’s ChatGPT Plugin . With the new integration, developers can seamlessly and securely enhance the power of large language models from OpenAI, Azure OpenAI, and Hugging Face with proprietary data in Atlas to build powerful retrieval-augmented generation applications using Python or C#. Developing faster with intelligent tools and frameworks In addition to helping developers build powerful gen AI applications through services like Atlas Vector Search, we’ve been working to enhance developer productivity, making it easier than ever to build applications with MongoDB. For example, we’ve introduced intelligent features to first-party tools like MongoDB Compass and Atlas Charts that support natural language. We also recently announced AI-powered SQL query conversions in Relational Migrator to help teams easily move their workloads to MongoDB. MongoDB is expanding the use of AI to enhance its integration with the world’s most popular integrated development environment, Visual Studio Code. We’re excited to announce the MongoDB Participant for the Github CoPilot chat experience, designed to empower developers to generate queries from natural language, understand collection schemas, and instantly access MongoDB documentation. Sign up for the private preview here . MongoDB also supports a variety of programming frameworks to improve productivity and accelerate application development—while ensuring data consistency and quality. Now generally available, the MongoDB Provider for Entity Framework Core (EF Core), encourages C# developers to build their next project on MongoDB. This new offering helps C# developers—39% of whom use EF Core—unlock the full power of MongoDB using the EF Core APIs and design patterns they already know and love. Streamlining comprehensive data analysis For years, MongoDB and Microsoft have facilitated the large-scale analysis of application-generated data to aid business development. Tools like Microsoft Power BI provide a comprehensive view of business intelligence data for developers and analysts with complex data estates using relational databases alongside MongoDB. MongoDB’s Power BI Connector for Atlas —previously supporting Import Mode—now also supports DirectQuery, which we announced a few weeks ago at MongoDB.local NYC . This allows for real-time querying of MongoDB data and is ideal for large datasets. To further enable customers working in the Microsoft ecosystem, we’ve recently made Atlas Data Federation and Atlas Online Archive generally available on Azure . These services enable users to query, transform, and create views across multiple Atlas databases and Azure cloud storage solutions, like Blob Storage and Data Lake Storage Gen2, simplifying data management and archiving within the Azure ecosystem. Building the future together MongoDB's partnership with Microsoft has made developing modern applications faster and easier. We're thrilled to announce these new capabilities at Microsoft Build 2024 , and look forward to helping our joint customers build amazing things together this year. “MongoDB’s relationship with Microsoft has never been better, and with these latest integrations, our momentum continues to grow,” said Alan Chhabra, MongoDB’s EVP of Worldwide Partners. “Already, many of the largest enterprises and most advanced startups in the world run MongoDB Atlas on Microsoft Azure. These latest innovations will empower even more of our customers to take full advantage of their data to build truly transformational generative AI-powered applications.” MongoDB’s partnership with Microsoft sets projects up for success today and tomorrow by delivering robust, integrated solutions that cater to developers' needs. MongoDB and Microsoft are pushing the boundaries for innovation and service for the developer community. To learn more about our recent announcements and for the latest product updates, visit our What’s New page. And head to our campaign page to learn more about how to build smarter and develop faster with MongoDB Atlas on Microsoft Azure.
Announcing DirectQuery Support for the MongoDB Atlas Connector for Power BI
Last year, we introduced the MongoDB Atlas Power BI Connector , a certified solution that has transformed how businesses gain real-time insights from their MongoDB Atlas data using their familiar Microsoft Power BI interface. Today, we’re excited to announce a significant enhancement to this integration: the introduction of DirectQuery support. DirectQuery mode provides a direct connection to your MongoDB Atlas database, allowing Power BI to query data in real-time. This means that your Power BI visualizations and reports will always reflect the latest data without importing and storing data within Power BI. This is especially beneficial for analyzing large datasets where up-to-date information is crucial, ensuring decisions are made efficiently without losing performance due to repetitive data imports and storage complexities. How DirectQuery in MongoDB Atlas Power BI Connector works: The Power BI Connector is supported through MongoDB’s Atlas SQL Interface , which is easily enabled from the Atlas console. Atlas SQL, powered by Atlas Data Federation , allows you to integrate data across sources and apply transformations directly, enhancing your analytics. Once enabled, you’ll receive a SQL Endpoint or URL to input into your MongoDB Atlas SQL Connection Dialog within Power BI Desktop. Here, you can choose between two connectivity modes: Import or DirectQuery. Once connected through DirectQuery, Query folding takes place with Power Query , which is how data retrieval and transformation of source data is optimized. You can also achieve data transformation using a SQL Statement, either with the SQL Statement option in the Atlas SQL Interface or within the M Code script accessed via the Power Query Advanced Editor. After your data is transformed and ready for analysis, start building reports with your Atlas data within the Power BI Desktop! Then, simply save, publish, and distribute within the Power BI online app, which is now part of the Microsoft Fabric platform. Watch our comprehensive tutorial below covering how to connect your Atlas data to Power BI , control SQL schemas in Atlas, and use DirectQuery to gain real-time access to your data for business insights. Power BI Connector for MongoDB Atlas is a Microsoft-certified solution. It not only supports the advanced capabilities of DirectQuery but also continues to offer Import Mode for scenarios where data volume is manageable and detailed data modeling is preferred. Whether you’re analyzing real-time data streams or creating comprehensive reports, the Atlas Power BI Connector adapts to your needs, ensuring your business leverages the full power of MongoDB Atlas. DirectQuery Support is available now and can be accessed by updating your existing MongoDB Atlas Power BI Connector or downloading it here . Start transforming your data analysis and making more informed decisions with real-time Atlas data. 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 .
MongoDB Provider for Entity Framework Core Now Generally Available
We are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now generally available . This allows developers using EF Core to build C# and .NET applications with MongoDB and to take advantage of our powerful developer data platform while continuing to use APIs and design patterns they already know and love. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Building for the C# and .NET communities Nearly one-third of all developers use C# to build applications, with the population of C# developers reaching upwards of 10 million developers worldwide . What’s more, 39 percent of C# developers use EF Core , which is beloved as an abstraction layer to simplify working with data during development. In the past, C# developers could use MongoDB’s C# driver but didn’t have first-party support for EF Core, so some turned to community-built projects that could be helpful—but lacked official backing or ongoing support from MongoDB. With the official MongoDB Provider for EF Core now generally available, developers can confidently use C# and EF Core when building with MongoDB for production-grade workloads. Gaurav Seth, Partner Director, Product Management at Microsoft, shared his excitement about the new integration, highlighting its importance for the .NET developer community: We are pleased to deepen the relationship between .NET developers and MongoDB through the new MongoDB Provider for Entity Framework Core,” said Gaurav Seth. “This advancement bridges the gap between MongoDB and Entity Framework Core, enabling .NET developers to leverage the full spectrum of MongoDB’s capabilities within the familiar EF environment. With this integration, .NET developers can now more easily incorporate MongoDB’s powerful features into their EF-based applications, further enhancing the robustness and scalability of their solutions. Gaurav Seth, Partner Director, Product Management at Microsoft What's in the new Provider for EF Core With the general availability release, the MongoDB Provider for EF Core offers developers the following capabilities, building upon the foundational features released in the public preview: Compatibility with Entity Framework Core 8 & .NET 8: Fully compatible with the latest EF Core and .NET versions, ensuring your projects are up-to-date with the newest features and improvements. Advanced Querying and Data Operations: Provides a comprehensive suite of querying options, including complex operations and aggregates like Where, OrderBy, and ThenBy, enabling precise data retrieval and deeper analytical insights within your applications. Mapping and Configuration Flexibility: Extended mapping capabilities for properties and entities, including support for various data types and composite keys, providing greater flexibility and precision in how data is structured and stored. Array and List Handling: Improved handling of arrays and lists, enabling more complex data structures to be easily managed and manipulated within your applications. Logging: Enhanced logging for better visibility of operations. We will continue to offer support for the following capabilities launched in the Public Preview: Support for code-first workflows : Allows users to build without an initial database; you create the classes for your application and then match your data model to the classes, not the other way around. Basic CRUD methods: Basic create, read, update, and delete (CRUD) operations are supported. String and numeric type operators: String and numeric type operators needed for basic CRUD operations will be supported. We anticipate supporting more complex operators in future iterations of the Provider. Embedded documents: The Provider supports embedded documents, making it easier to store related information in the same database record. Class mapping and serialization: Your classes in C# will map to MongoDB in a predictable way, including when working with IDs as well as date and/or time values. LINQ query support: The Provider will support LINQ queries with fluent query syntax. Change tracking: The Provider allows you to track and save changes made to entities with each DbContext instance back to your MongoDB database. Benefits of using the Provider for EF Core With the MongoDB Provider for EF Core, C# developers can unlock the full power of MongoDB's developer data platform to build modern applications while leveraging a familiar API interface, query paradigm (LINQ), and design patterns. Developers looking to modernize their data layer can do so with MongoDB while remaining free from cloud vendor lock-in since MongoDB works with all major cloud providers and for multi-cloud deployments. How to get started with MongoDB Provider for Entity Framework Core All you need to do is download the MongoDB Provider for EF Core from the NuGet package manager and build a DbContext that points to a MongoDB Provider instance. The Provider connects to MongoDB and handles the rest, so you can quickly harness the joint value of EF Core and MongoDB. Learn more by diving into our documentation . After you try the new Provider for EF Core, leave us feedback . Your input is important for helping us continue to improve the product experience. Get started today to unleash the power of your data with MongoDB and EF Core.
Building Modern Applications Faster: New Capabilities at MongoDB.local NYC 2024
Today, we kicked off MongoDB.local NYC and unveiled new capabilities across our developer data platform. The updates and capabilities announced today pave the way for a new era of app modernization and will allow developers to unleash the full potential of transformative technology like AI. Here’s an overview of our announcements, from a comprehensive update to MongoDB to AI-powered intelligent developer experiences: This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Modern applications need a modern database Cutting-edge modern applications must deliver both an exceptional experience and additional revenue. To meet these demands, developers require a database solution that offers optimal performance, scale, and operational resilience—while maintaining cost efficiency. So today, we’re thrilled to announce the preview of MongoDB 8.0 —the next evolution of MongoDB’s modern database. MongoDB 8.0 is focused on delivering unparalleled performance, scalability, security, and operational resilience to support the creation of next-generation applications, including sophisticated AI-driven solutions. It provides optimal performance by dramatically increasing query performance, improving resilience during periods of heavy load, making scalability easier and more cost-effective, and making time series collections faster and more efficient. Modernizing your next application with MongoDB is now easier As application modernization projects gain momentum, migrations are becoming a pressing reality for development and database teams. Transitioning from legacy relational systems to modern databases like MongoDB is essential to keeping up with technological shifts like AI. However, modernization and migrations have many challenges, from converting complex schemas and translating large amounts of application code to keeping databases in sync during long modernization projects. Announced in June 2023, MongoDB Relational Migrator streamlines the migration process by automating tasks like schema design, data migrations, and application code generation. Maintaining data synchronization is paramount in long-running modernization projects—where legacy relational databases must coexist with MongoDB until the project is complete. Today, we are pleased to announce that MongoDB Relational Migrator is now integrated with Confluent Cloud to support long-running change data capture (CDC) sync jobs. These jobs ensure operational resilience and observability, addressing the complexities of phased transitions without the added burden of managing Apache Kafka independently. Furthermore, migrating from legacy relational databases often involves significant effort in rewriting SQL queries, stored procedures, and triggers, which has traditionally been time-consuming and difficult. Now available in public preview, an AI-powered SQL Query Converter Tool has been introduced to MongoDB Relational Migrator that automates the process of converting existing SQL queries, stored procedures, and triggers to work with MongoDB in languages like JavaScript, Java, or C#. This streamlined approach—paired with MongoDB professional services—enables a simplified migration process that can scale effectively. Helping developers build faster with confidence on MongoDB We recognize the vital role that developers play in the success of every project, which is why we’re dedicated to making their MongoDB experience as seamless as possible. Frameworks are a great way for developers to boost productivity, improve code consistency and quality, and ultimately deliver code faster. For the C# developer community, we are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now generally available . This allows C# developers building with EF Core to unlock the full power of MongoDB's developer data platform—while still using the EF Core APIs and design patterns they already know and love. And, recognizing the needs of the PHP community, we’re also proud to introduce the Laravel Aggregation Builder . This feature simplifies the process of building complex aggregation queries within Laravel, the most popular framework among PHP developers. By enhancing the integration of MongoDB with Laravel, we aim to boost productivity and ease the complexity of query operations, ensuring PHP developers can also enjoy an optimized development experience with MongoDB. Generating queries and visualizations with AI Since its initial release in 2015, MongoDB Compass has helped developers quickly build and debug queries and aggregations for their application code. Today, MongoDB Compass introduces an AI-powered, natural language query experience , making it even easier for developers to use MongoDB’s powerful Query API. Now generally available, this feature lets developers use natural language to generate executable MongoDB Query API syntax for everything from simple queries to sophisticated aggregations through an intelligent and guided experience. For example, a developer can input "Filter vacation rentals by location, group the remaining documents by number of bedrooms, and calculate the average nightly rental price," MongoDB Compass will suggest code to execute the stages of the aggregation pipeline. Data visualizations are a powerful way of understanding application data, and embedding charts into user-facing applications further enhances their utility and appeal to developers. However, creating visualizations is often hampered by the need for in-depth knowledge of the dataset and proficiency in using business intelligence tools—skills that many developers may not have. Now available in public preview, we introduced an easy-to-use visualization tool with generative AI capabilities in MongoDB Atlas Charts . Using natural language prompts, developers can easily render charts and build dashboards, making visualizing data and enriching their apps simple and fast. For example, developers can input ‘Show me the list of movies released in the last year sorted by genre,’ and MongoDB Atlas Charts will gather data and quickly generate the requested visualization. Today’s announcements underscore MongoDB’s commitment to helping developers innovate quickly and easily. For more about the MongoDB.local NYC 2024 updates, check out the product announcements page on our website.
Welcome to MongoDB.local NYC 2024!
AI promises to upend how enterprises operate and reach customers … if only they could first find the "On" button. Despite the tremendous promise of AI, most companies still find themselves in the experimentation phase, working through proofs of concept, hampered by unfamiliar technologies that don't work well together. But MongoDB is uniquely positioned to help developers turn all this AI noise into "signal" that benefits customers. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . This week at MongoDB.local NYC, thousands of developers and executives—representing Fortune 500 companies and cutting-edge startups—have gathered to discuss and demonstrate the real-world successes they've had building on MongoDB's developer data platform. MongoDB is fast becoming the industry’s go-to memory database for retrieval-augmented generation (RAG) and agentic systems, offering a unified data model across the entire AI stack. But this isn’t just a technology story, as important as that is. MongoDB also now offers essential programs and services to make AI much more accessible. In short, MongoDB is taking developers from experimentation to impact, and advancing our long-standing mission of making it easy to work with data. Demystifying AI Businesses are eager to adopt generative AI, but they don’t know where to start. The AI landscape is incredibly complex—and seems to get more so by the minute. This complexity, coupled with limited in-house AI expertise and concerns about the performance and security risks of integrating disparate technologies, is keeping too many organizations on the sidelines. MongoDB can help. To get organizations started, we’re announcing the MongoDB AI Applications Program (MAAP) . With MAAP, we give customers the blueprints and reference architectures to easily understand how to build AI applications. We also take on the heavy lifting of integrating MongoDB's developer data platform with leading AI partners like Anthropic, Cohere, Fireworks AI, Langchain, LlamaIndex, Nomic, Anyscale, Credal.ai, and Together AI, all running on the cloud provider of your choice. MAAP will be available to customers in early access starting in July. In addition to MAAP, we’re also introducing two new professional services engagements to help you build AI-powered apps quickly, safely, and cost-effectively: An AI Strategy service that leverages experts to help customers identify the highest-impact AI opportunities and to create specific plans on how to pursue them. For customers who have already identified use cases to pursue, an AI Accelerator service that brings expert consulting—from solution design through prototyping—to enable customers to execute their AI application roadmap from idea to production. Once developers get to building AI apps, they’ll find that MongoDB allows them to speak the data “language” of AI. Our developer data platform unifies all different data types alongside your real-time operational data—including source data, vector embeddings, metadata, and generated data—and supports a broad range of use cases. Not only do we give developers the most intuitive way to work with their data, we also keep improving where they can do so. Many developers first experience MongoDB in a local environment before moving to a fully managed cloud service like MongoDB Atlas. So, I'm excited to share that we will be introducing full-text search and vector search in MongoDB Community Edition later this year, making it even easier for developers to quickly experiment with new features and streamlining end-to-end software development workflows when building AI applications. These new capabilities also enable support for customers who want to run AI-powered apps on devices or on-premises. As customers begin to mature these applications, cost becomes an important consideration. Last year, we introduced dedicated nodes for Atlas Search on AWS. Using dedicated nodes, customers can isolate their vector search workloads and scale them up or down independently from operational workloads, improving performance and ensuring high availability. By giving customers workload isolation without data isolation, they can manage resources efficiently without additional complexity. Today, we’re announcing Atlas Search nodes on all three cloud providers, which customers can configure programmatically using the Atlas CLI or our Infrastructure-as-Code integrations . Learn more about how MongoDB is the best solution to the challenges posed by the fast-moving generative AI landscape . Real-time and highly performant Though AI rightly claims center stage at MongoDB .local NYC this week, it's not the only way we're helping developers. From real-time fraud detection , to predictive maintenance , to content summarization , customers need to efficiently process large volumes of high-velocity data from multiple sources. Today, we’re also announcing the general availability of Atlas Stream Processing , the public preview of Atlas Edge Server , and improved performance of time series workloads with MongoDB 8.0. Together, these capabilities enable customers to design applications that solve virtually any business challenge. Learn more about how MongoDB powers modern application requirements . These are just a few of the things we're announcing this week. Whether you’re just dipping your toes into the world of generative AI or are well on your way, MongoDB’s developer data platform, strong and diverse network of partners, and proven industry solutions will give you a competitive edge in a fast-moving market. Please take a minute to see what we've built for you, so that you can more easily build for your customers. Enjoy the conference, and we hope to see you soon! To see more announcements and get the latest product updates, visit our What’s New page. And head to the MongoDB.local hub to see where we’re stopping along our 2024 world tour.
Top AI Announcements at MongoDB.local NYC
The AI landscape is evolving so quickly that it’s no surprise customers are overwhelmed by their choices. Between foundation models for everything from text to code, AI frameworks, and the steady stream of AI-related companies being founded daily, developers and organizations face a dizzying array of AI choices. MongoDB empowers customers through a developer data platform that helps them avoid vendor lock-in from cloud providers or AI vendors in this fast-moving space. This freedom allows customers to choose the large language model (LLM) that best suits their needs - now or in the future, whether it's open source or proprietary. Today at MongoDB.local NYC, we announced many new product capabilities, partner integrations, services, and solution offering that enable development teams to get started and build customer-facing solutions with AI. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Run everywhere, with whatever technology you are using in your AI stack MongoDB’s flexible document model is built on the ethos of “data that is accessed and used together is stored together.” Vectors are a natural extension of this capability, meaning customers can store their source data, metadata, and related vector embeddings in the same document. All of this is accessed and queried with a common Query API, making vector data easy to combine and work with other types of data stored within MongoDB. MongoDB Atlas—our fully managed, multi-cloud developer data platform—makes it easy to build AI-powered applications and experiences, with the breadth and depth of MongoDB’s AI partnerships and integrations—no matter which language, application framework, foundation model, or technology partner is used or preferred by developers. This year, we’re continuing to focus on our AI partnerships and integrations to make it easier for developers to build innovative applications with generative AI, including: Python and JavaScript using the dedicated Langchain-MongoDB package Python and C# Microsoft Semantic Kernel integration for Atlas Vector Search AI models from Mistral and Cohere AI models on the Fireworks AI platform Addition of Atlas Vector Search as a knowledge base in Amazon Bedrock Atlas as a datastore enabling storage, query, and retrieval using natural language in ChatGPT Atlas Vector Search as a datastore on Haystack Atlas Vector Search as a datastore on DocArray Collaboration with Google Gemini Code Assist and Amazon Q to quickly prototype new features and accelerate application development. Google Vertex AI Extension to harness natural language with MongoDB queries MongoDB integrates well with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. We continue investing in making the entire AI stack work seamlessly, enabling developers to take advantage of generative AI capabilities in their applications easily. MongoDB’s integrations and our industry-leading multi-cloud capabilities allow organizations to move quickly and avoid lock-in to any particular cloud provider or AI technology in a rapidly evolving space. Build high-performance AI applications securely and at scale Workload isolation, without data isolation, is critical for building performant, scalable AI applications. Search Nodes in MongoDB Atlas provide dedicated computing and enable users to isolate memory-intensive AI workloads for superior performance and higher availability. Users can optimize resource consumption for their use case, upsizing or downsizing the hardware for that specific node irrespective of the rest of the database cluster. Search Nodes make optimizing performance for vector search queries easy without over or under-provisioning an entire cluster. The IaC integrations with Hashicorp Terraform Atlas Provider and Cloudformation enable developers to configure and programmatically deploy Search Nodes at scale. Search Nodes are an integral part of Atlas - our fully managed, battle-tested, multi-cloud platform. Previously, we announced the availability of Search Nodes for our AWS and Google Cloud customers. We are excited to announce the preview of Search Nodes for our Azure customers at MongoDB.local NYC. Search Nodes on Atlas helps developers move faster by removing the friction of integrating, securing, and maintaining the essential data components required to build and deploy modern AI applications. Improve developer productivity with AI-powered experiences Today, we also announced new and improved releases of our intelligent developer experiences in MongoDB Compass , MongoDB Relational Migrator , and MongoDB Atlas Charts , aiming to enhance developer productivity and velocity. With the updated releases, developers can use natural language to query their data using MongoDB Compass, troubleshoot common problems during development, perform SQL-to-Query API conversion right from within MongoDB Relational Migrator , and quickly build charts and dashboards using natural language prompts in MongoDB Atlas Charts. Collectively, these intelligent experiences will help developers build differentiated features with greater control and flexibility, making it easier than ever to build applications with MongoDB. Enable development teams to get started and build customer-facing solutions faster and easier with AI MongoDB makes it easy for companies of all sizes to build AI-powered applications. To provide customers with a straightforward way to get started with generative AI, MongoDB is announcing the MongoDB AI Application Program (MAAP). Based on usage patterns for common AI use cases, customers receive a functioning application built on a reference architecture backed by MongoDB Atlas, vetted AI models and hosting solutions, technical support, and a full-service engagement led by our Professional Services team. We’re launching with an incredible group of industry-leading partners, including Anthropic, Anyscale, AWS, Cohere, Credal.ai, Fireworks.ai, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. MongoDB is in a unique position in the market to be able to pull together such an impressive AI partner ecosystem in a single customer-focused program, and we’re excited to see how MAAP will help customers more easily go from ideation to fully functioning generative AI applications. Last year, to further enable startups to build AI solutions with MongoDB Atlas, we launched the AI Innovators Program , an extension of MongoDB for Startups , which offers an additional $5000 in Atlas credits to our AI startups. This year, we are expanding the program by introducing an AI Startup Hub , which features a curated guide for getting started with MongoDB and AI, quickstarts for MongoDB and select AI partners, and startup credit offerings from our AI partners. We provide two new AI Accelerator consulting packages for larger enterprise companies: AI Essentials and AI Implementation. While MAAP is aimed exclusively at building highly vetted reference architectures, these consulting packages allow customers to design, build, and deploy open-ended AI prototypes and solutions into their applications. Data has always been a competitive advantage for organizations, and MongoDB makes it easy, fast, and flexible to innovate with data. We continue to invest in making all the other parts of the AI stack easy for organizations: vetting top partners to ensure compatibility with different parts of the application stack, building a managed service that spans multiple clouds in operation, and ensuring the openness that's always been a part of MongoDB which avoids vendor lock-in. How does MongoDB Atlas unify operational, analytical, and generative AI data services to streamline building AI-enriched applications? Check out our MongoDB for AI page to learn more.
MongoDB Introduces Workload Identity Federation for Database Access
Update June 5, 2024: Workload Identity Federation is now generally available. Head over to our docs page to learn more. MongoDB Atlas customers run workloads (applications) inside AWS, Azure, and Google Cloud. Today, to enable these workloads to authenticate with MongoDB Atlas cluster—customers create and manage MongoDB Atlas database users using the natively supported SCRAM (password) and X.509 authentication mechanisms and configure them in their workloads. Customers have to manage the full identity lifecycle of these users in their applications, including frequently rotating secrets. To meet their evolving security and compliance requirements, our enterprise customers require database users to be managed within their existing identity providers or cloud providers of their choice. Workload Identity Federation will be in general availability later this month and allows management of MongoDB Atlas database users with Azure Managed Identities, Azure Service Principals, Google Service Accounts, or an OAuth2.0 compliant authorization service. This approach makes it easier for customers to manage, secure, and audit their MongoDB Atlas database users in their existing identity provider or a cloud provider of their choice and enables them to have "passwordless" access to their MongoDB Atlas databases. Along with Workload Identity Federation, Workforce Identity Federation , which was launched in public preview last year, will be generally available later this month. Workforce Identity Federation allows organizations to configure access to MongoDB clusters for their employees with single sign-on (SSO) using OpenID Connect. Both features complement each other and enable organizations to have complete control of database access for both application users and employees. Workload Identity Federation support will be available in Atlas Dedicated Clusters on MongoDB 7.0 and above, and is supported by Java, C#, Node, and Python drivers. Go driver support will be added soon. Quick steps to get started with Workload Identity Federation: Configure Atlas with your OAuth2.0 compatible workload identity provider such as Azure or Google Cloud. Configure Azure Service Principal or Google Cloud Service Accounts for the Azure or Google Cloud resource where your application runs. Add the configured Azure Service Principal or Google Cloud Service Account as Atlas database users with Federated authentication. Using Python or any supported driver inside your application, authenticate and authorize with your workload identity provider and Atlas clusters. To learn more about Workload Identity Federation, please refer to the documentation . And to learn more about how MongoDB’s robust operational and security controls protect your data, read more about our security features .