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Announcing Search Index Management in MongoDB Compass

You can now create and manage Atlas Search and Atlas Vector Search indexes on the interface many of you know and love: MongoDB Compass . Seamlessly build full-text and semantic search applications on top of your Atlas database, delivering swift and relevant results for a range of use cases including e-commerce sites, customer support chatbots, recommendation systems, and more. Gone are the days of juggling multiple tools to bring your search queries to fruition. And, with a variety of templates to choose from, Compass simplifies learning search index syntax so you can focus on what’s most important to you: building exceptional end-user experiences on top of your search queries. Try it out To get started, connect to an Atlas cluster from Compass. If you don’t have one, sign up . From there, simply navigate to Compass’ Indexes tab and select Create Search Index . It’s easy to build your first search index using one of our templates. Select either Search or Vector Search, and use the appropriate template. In this example, we’re going to create a Vector Search index. Once you're satisfied with your index definition, click Aggregate to start testing out your pipeline in Compass. Compass’ new search index experience leads you to results in just three guided steps, all without leaving the comfort of Compass. To learn more about search indexing in Compass, visit our documentation . If you have feedback about Compass’ search index experience, let us know on our feedback forum . Happy indexing!

March 18, 2024

From Relational Databases to AI: An Insurance Data Modernization Journey

Imagine you’re a data architect, a developer, or a data engineer at an insurance company. Management has asked you and your team to build a new AI claim adjustment system, a customer-facing LLM-powered chatbot, and an application to streamline the underwriting process. However, doing so is far from straightforward due to the challenges you face on a daily basis. The bulk of your time is spent navigating your company’s outdated legacy systems, which were built in the 1970s and 1980s. Some of these legacy platforms were written in COBOL and CICS, and today very few people on your team know how to develop and maintain those technologies. Moreover, the data models you work with are another source of frustration. Every interaction with them is a reminder of the intricate structures that have evolved over time, making data manipulation and analysis a nightmare. In sum, legacy systems are preventing your team—and your company—from innovating and keeping up with both your industry and customer demands. Whether you’re trying to modernize your legacy systems to improve operational efficiency, or to boost developer productivity, or if you want to build AI-powered apps that integrate with large language models (LLMs), MongoDB has a solution for that. In this post, we’ll walk you through a journey that starts with a relational data model refactored into MongoDB collections, vectorization and querying of unstructured data and, finally, retrieval augmented generation (RAG) : asking large language models (LLMs) questions about data in natural language. Identifying, modernizing, and storing the data Our journey starts with an assessment of the data sources we want to work with. As shown below, we can bucket the data into three different categories: Structured legacy data: Tables of claims, coverages, billings, and more. Is your data locked in rigid relations schemas? This tutorial is a step-by-step guide on how to migrate a real-life insurance relational model with the help of MongoDB Relational Migrator , refactoring 21 tables to only five MongoDB collections. Structured data (JSON): You might have files of policies, insurance products, or forms in JSON format. Check out our docs to learn how to insert those into a MongoDB collection. Unstructured data (PDFs, Audios, Images, etc.): If you need to create and store a numerical representation (vector embedding) of, for instance, claim-related photos of accidents or PDFs of policy guidelines, you can have a look at this blog that will walk you through the process of generating embeddings of pictures of car crashes and persisting them alongside existing fields in a MongoDB collection. Figure 1: Storing different types of data into MongoDB Regardless of the original format or source, our data has finally landed into MongoDB Atlas into what we call a Converged AI Data Store, which is a platform that centrally integrates and organizes enterprise data, including vectors, that enable the development of ML- and AI-powered applications. Accessing, experimenting and interacting with the data It’s time to put the data to work. The Converged AI Data Store unlocks a plethora of use cases and efficiency gains, both for the business and for developers. The next step of the journey is about the different ways we can interact with our data: Database and Full Text Search: Learn how to run database queries, start from the basics and move up to advanced features such as facets, fuzzy search, autocomplete, highlighting, and more with Atlas Search . Vector Search: We can finally leverage unstructured data. The Image Search blog we mentioned earlier also explains how to create a Vector Search index and run vector queries against embeddings of photos. RAG: Combining Vector Search and the power of LLMs, it is possible to interact in natural language with our data (see Figure 2 below), asking complex questions and getting detailed answers. Follow this tutorial to become a RAG expert. Figure 2: Retrieval augmented generation (RAG) diagram where we dynamically combine our custom data with the LLM to generate reliable and relevant outputs Having explored all the different ways we can ask questions of the data, we made it to the end of our journey. You are now ready to modernize your company’s systems and finally be able to keep up with the business’ demands. What will you build next? If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks

March 14, 2024

Using Generative AI and MongoDB to Tackle Cybersecurity’s Biggest Challenges

In the ever-evolving landscape of cybersecurity, organizations face a multitude of challenges that demand innovative solutions harnessing cutting-edge technologies. One of the most pressing issues is the increasing sophistication of cyber threats, including malware, ransomware, and phishing attacks, which are becoming more difficult to detect and mitigate. Additionally, the rapid expansion of digital infrastructures has widened the attack surface, making it harder for security teams to monitor and protect every entry and egress point. Another significant challenge is the shortage of skilled cybersecurity professionals — estimated by independent surveys to number around 4 million staff worldwide 1 — which leaves many organizations vulnerable to attack. These challenges underscore the need for advanced technologies that can augment human efforts to secure digital assets and data. How can generative AI help? Generative AI (gen AI) has emerged as a powerful tool in addressing these cybersecurity challenges. By leveraging large language models (LLMs) to generate new data or patterns based on existing datasets, generative AI can provide innovative solutions in several key areas: Enhanced threat detection and response Generative AI can be used to create simulations of cyber threats, including sophisticated malware and phishing attacks. These simulations can help in training machine learning models to detect new and evolving threats more accurately. Furthermore, gen AI can aid in the development of automated response systems that react to threats in real time. While this will never eliminate the need for human oversight, it will reduce the need for manual intervention and toil, allowing for quicker mitigation of attacks. For example, with the appropriate oversight it can automatically apply patches to vulnerable systems or adjust firewall rules to block attack vectors. This automated rapid response capability is particularly valuable in mitigating zero-day vulnerabilities, where the window between the discovery of a vulnerability and its exploitation by attackers can be very short. Actionable learnings from security event postmortems In the aftermath of a cybersecurity incident, conducting a thorough postmortem analysis is crucial for understanding what happened, why it happened, and how similar events can be prevented in the future. Generative AI can play a pivotal role in this process by synthesizing and summarizing complex data from a multitude of sources, including logs, network traffic, and security alerts. By analyzing this data, gen AI can identify patterns and anomalies that may have contributed to the security breach, offering insights that might be overlooked by human analysts due to the sheer volume and complexity of the information. Furthermore, it can generate comprehensive reports that highlight key findings, causative factors, and potential vulnerabilities, streamlining the postmortem process. This capability not only accelerates the recovery and learning process but also enables organizations to implement more effective remediation strategies, ultimately strengthening their cybersecurity posture. Generating synthetic data for deep model training The shortage of real-world data for training cybersecurity systems is a significant hurdle. Gen AI can create realistic, synthetic data sets that mirror genuine network traffic and user behavior without exposing sensitive information. This synthetic data can be used to train detection systems, improving their accuracy and effectiveness without compromising privacy or security. Automating phishing detection Phishing remains one of the most common attack vectors. Gen AI can analyze patterns in phishing emails and websites, generating models that predict and detect phishing attempts with high accuracy. By integrating these models into email systems and web browsers, organizations can automatically filter out phishing content, protecting users from potential threats. Putting it all together: The opportunities and the risks Generative AI holds the promise of transforming cybersecurity practices by automating complex processes, enhancing threat detection and response, and providing a deeper understanding of cyber threats. As the industry continues to integrate gen AI into cybersecurity strategies, it's crucial to remain vigilant about the ethical use of this technology and the potential for misuse. Nevertheless, the benefits it offers in strengthening digital defenses are undeniable, making it an invaluable asset in the ongoing battle against cyber threats. How does MongoDB help? With MongoDB, your development teams can build and deploy robust, correct, and differentiated real-time cyber defenses faster, and at any scale. To understand how MongoDB does this, consider that the the AI technology stack comprises three layers: The underlying compute (GPUs) and LLMs The tooling to fine-tune models along with the tooling for in-context learning and inference against the trained models The AI applications and related end-user experiences MongoDB operates at the second layer of the stack. It enables customers to bring their own proprietary data to any LLM running on any computing infrastructure to build gen AI-powered cybersecurity applications. MongoDB does this by addressing the hardest problems when adopting gen AI for cybersecurity. MongoDB Atlas securely unifies operational data, unstructured data, and vector data in a single, fully managed multi-cloud platform, avoiding the need to copy and sync data between different systems. MongoDB’s document-based architecture also allows development teams to easily model relationships between your application data and vector embeddings. This allows deeper and faster analytics and insights against security-related data. Figure 1: MongoDB Atlas brings together all of the data services needed to build modern cyber security applications in a unified API and developer data platform. MongoDB’s open architecture is integrated with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. This, combined with our industry-leading multi-cloud capabilities, allows your development teams the flexibility to move quickly and avoid lock-in to any particular cloud provider or AI technology in this rapidly evolving space. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Applying gen AI and MongoDB to real world cybersecurity applications Threat intelligence ExTrac utilizes AI-powered analytics and MongoDB Atlas to predict public safety risks by analyzing data from thousands of sources. The platform initially helped Western governments foresee conflicts but is expanding to enterprises for reputational management and more. MongoDB's document data model allows ExTrac to manage complex data efficiently, enhancing real-time threat identification. Atlas Vector Search aids in augmenting language models and managing vector embeddings for texts, images, and videos, speeding up feature development. This approach enables ExTrac to efficiently model trends, track evolving narratives, and predict risk for its customers, leveraging the flexibility and power of MongoDB to handle data of any shape and structure. Learn more in our ExTrac case study . Cybersec assessments VISO TRUST leverages AI to streamline the assessment of third-party cyber risks, making complex vendor security information quickly accessible for informed decision-making. Utilizing Amazon Bedrock and MongoDB Atlas, VISO TRUST's platform automates the due diligence of vendor security, significantly reducing the workload for security teams. Its AI-powered approach involves artifact intelligence that classifies security documents, detects organizations, and predicts security control locations within artifacts. MongoDB Atlas hosts text embeddings for a dense retrieval system that enhances the accuracy of LLMs through retrieval-augmented generation (RAG), providing instant, actionable security insights. This innovative use of technology enables VISO TRUST to offer rapid, scalable cyber risk assessments, boasting significant reductions in work and time for enterprises like InstaCart and Upwork. MongoDB's flexible document database and Atlas Vector Search play critical roles in managing and querying the vast amounts of data, supporting VISO TRUST's mission to deliver comprehensive cyber risk intelligence. Learn more in our Viso Trust case study . Steps to get started Generative AI powered by LLMs augmented with your own operational data encoded as vector embeddings is opening up many new possibilities in cyber security. If you want to learn more about the technology and its possibilities, take a look at our Atlas Vector Search learning byte . In just 10 minutes you’ll get an overview of different use cases and how to get started. 1 Hill, M. (2023, April 10). Cybersecurity workforce shortage reaches 4 million despite significant recruitment drive . CSO.

March 13, 2024

How MongoDB Enables Digital Twins in the Industrial Metaverse

The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features. But first: What is the metaverse, and why is it so important to manufacturers? We often use the term, "digital twin" to refer to a virtual replication of the physical world. It is commonly used for simulations and documentation. The metaverse goes one step further: Not only is it a virtual representation of a physical device or a complete factory, but the metaverse also reacts and changes in real time to reflect a physical object’s condition. The advent of the industrial metaverse over the past decade has given manufacturers an opportunity to embrace a new era of innovation, one that can enhance collaboration, visualization, and training. The industrial metaverse is also a virtual environment that allows geographically dispersed teams to work together in real time. Overall, the metaverse transforms the way individuals and organizations interact to produce, purchase, sell, consume, educate, and work together. This paradigm shift is expected to accelerate innovation and affect everything from design to production across the manufacturing industry. Here are some of the ways the metaverse — powered by MongoDB — is having an impact manufacturing. Design and prototyping Design and prototyping processes are at the core of manufacturing innovation. Within the metaverse, engineers and designers can collaborate seamlessly using VR, exploring virtual spaces to refine and iterate on product designs. MongoDB's flexible document-oriented structure ensures that complex design data, including 3D models and simulations, is efficiently stored and retrieved. This enables real-time collaboration, accelerating the design phase while maintaining the precision required for manufacturing excellence. Training and simulation Taking a digital twin and connecting it to physical assets enables training beyond traditional methods and provides immersive simulations in the metaverse that enhance skill development for manufacturing professionals. VR training, powered by MongoDB's capacity to manage diverse data types — such as time-series, key-values and events — enables realistic simulations of manufacturing environments. This approach allows workers to gain hands-on experience in a safe virtual space, preparing them for real-world challenges without affecting production cycles. Gamification is also one of the most effective ways to learn new things. MongoDB's scalability ensures that training data, including performance metrics and user feedback, is efficiently handled to continuously enlarge the training modules and the necessary resources for the ever-increasing amount of data. Maintenance and repair Maintenance and repair operations are streamlined through AR applications within the metaverse. The incorporation of AR and VR technologies into manufacturing processes amplifies the user experience, making interactions more intuitive and immersive. Technicians equipped with AR devices can access real-time information overlaid onto physical equipment, providing step-by-step guidance for maintenance and repairs. MongoDB's support for large volumes of diverse data types, including multimedia and spatial information, ensures a seamless integration of AR and VR content. This not only enhances the visual representation of data from the digital twin and the physical asset but also provides a comprehensive platform for managing the vast datasets generated during AR and VR interactions within the metaverse. Additionally, MongoDB's geospatial capabilities come into play, allowing manufacturers to manage and analyze location-based data for efficient maintenance scheduling and resource allocation. The result is reduced downtime through more efficient maintenance and improved overall operational efficiency. From the digital twin to metaverse with MongoDB The advantages of a metaverse for manufacturers are enormous, and according to Deloitte many executives are confident the industrial metaverse “ will transform research and development, design, and innovation, and enable new product strategies .” However, the realization is not easy for most companies. Challenges include managing system overload, handling vast amounts of data from physical assets, and creating accurate visualizations. The metaverse must also be easily adaptable to changes in the physical world, and new data from various sources must be continuously implemented seamlessly. Given these challenges, having a data platform that can contextualize all the data generated by various systems and then feed that to the metaverse is crucial. That is where MongoDB Atlas , the leading developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling, and providing access to the data via a unified query interface as seen in Figure 1. Figure 1: MongoDB connecting to a physical & virtual factory Generative AI with Atlas Vector Search With MongoDB Atlas, customers can combine three systems — database, search engine, and sync mechanisms — into one, delivering application search experiences for metaverse users 30% to 50% faster . Atlas powers use cases such as similarity search, recommendation engines, Q&A systems, dynamic personalization, and long-term memory for large language models (LLMs). Vector data is integrated with application data and seamlessly indexed for semantic queries, enabling customers to build easier and faster. MongoDB Atlas enables developers to store and access operational data and vector embeddings within a single unified platform. With Atlas Vector Search , users can generate information for maintenance, training, and all the other use cases from all possible information that is accessible. This information can come from text files such as Word, from PDFs, and even from pictures or sound streams from which an LLM then generates an accurate semantic answer. It’s no longer necessary to keep dozens of engineers busy, just creating useful manuals that are outdated at the moment a production line goes through first commissioning. Figure 2: Atlas Vector Search Transforming the manufacturing industry with MongoDB In the digital twin and metaverse-driven future of manufacturing, MongoDB emerges as a linchpin, enabling cost-effective virtual prototyping, enhancing simulation capabilities, and revolutionizing training processes. The marriage of MongoDB with AR and VR technologies creates a symbiotic relationship, fostering innovation and efficiency across design, training, and simulation. As the manufacturing industry continues its journey into the metaverse, the partnership between MongoDB and virtual technologies stands as a testament to the transformative power of digital integration in shaping the future of production. Learn more about how MongoDB is helping organizations innovate with the industrial metaverse by reading how we Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps , how IIoT data can be integrated in 4 steps into MongoDB, or how MongoDB drives Innovations End-To-End in the whole Manufacturing Chain .

March 12, 2024

Building AI With MongoDB: How GoBots AI for E-commerce Increases Retailer Sales Conversion by 40%

Major retail brands have long been using various forms of AI, for example statistical analysis and machine learning models, to better serve their customers. But with its high barriers to entry, one key channel has been slower to embrace the technology. By connecting large and small brands with customers, e-commerce marketplaces such as Amazon, Mercado Libre, and Shopify are among the fastest growing retail routes to market. Since 2016, GoBots has been working to extend the benefits of AI to any retailer on any marketplace. It uses AI, analytics, and MongoDB Atlas to make e-commerce easier, more convenient, and smarter for brands serving Latin America. “We are building an AI-driven customer service platform that revolutionizes e-commerce experiences,” says Victor Hochgreb, Co-Founder and CEO of GoBots. “Our solution makes the benefits of AI available to any retailer, whether large or small. With our GoBots natural language understanding (NLU) model, retailers automate customer interactions such as answering questions and resolving issues through intelligent assistants. At the same time, they leverage data analytics to offer personalized customer experiences.” Hochgreb goes on to say, “GoBots increases engagement and conversion rates for over 600 clients across Latin America, including Adidas, Bosch, Canon, Chevrolet, Dell, Electrolux, Hering, HP, Nike, and Samsung.” Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Figure 1: GoBots NLU AI models analyze customer questions and issues, providing human-like answers in seconds Exploring GoBot's AI stack GoBots’ custom NLU models are built using the Rasa framework. Hochgreb says, “We have a neural network trained on over 150 million question-answer examples and more than 50 bots — specialists in different segments — to understand more specific questions.” Models are fine tuned with data from the retailer's own product catalog and website corpus. The model runtime is powered by a PyTorch microservice on Google Cloud . The larger GoBots platform is built with Kotlin and orchestrated by Kubernetes, providing the company with cloud freedom as its business expands and evolves. Figure 2: GoBots question processing architecture The GoBots AI assistants kick into action as soon as a customer asks a question on the marketplace site, with the questions stored in MongoDB Atlas . GoBots’ natural language models are programmatically called via a REST API to perform tasks like named entity recognition (NER), user intent detection, and question-answer generation with all inferences also stored in MongoDB. If the models are able to generate an answer with high confidence, the GoBots service will respond directly to the customer in real time. In case of a low confidence response, the models flag the question to a customer service representative who receives a pre-generated suggested response. They can then verify the response and reply to the customer. Increasingly the company’s engineers are also evaluating the capabilities of large language models (LLMs) to respond to customer questions. It is testing both commercial models from OpenAI as well as open source models such as Llama-2 and Mixtral hosted on Hugging Face. With all question-answer pairs from the different models written to the MongoDB Atlas database, the data is used to further tune the natural language models while also guiding model evaluations. The company has also recently started using Atlas Vector Search to identify and retrieve semantically similar answers to past questions. The search results power a co-pilot-like experience for customer service representatives and provide in-context training to its fleet of LLMs. Having our source data and metadata stored and synced side by side with our vector embeddings dramatically accelerates how quickly my developers build with AI. It also improves the quality of the outputs we return to customers, driving higher conversions and customer satisfaction. Victor Hochgreb, Co-Founder and CEO of GoBots Why MongoDB? With the power of MongoDB’s developer data platform and flexibility of MongoDB’s document model, GoBots builds higher-performing AI-powered applications faster: MongoDB Atlas provides a single data platform that serves multiple operational and AI use cases. This includes user data and product catalogs as well as a store for AI model inferences, outputs of multiple AI models for experimentation and evaluation purposes, a data source for fine-tuning models, and for vector search. The company is evaluating the use of Atlas Triggers for invoking AI model API calls in an event-driven manner as the underlying data changes. The field of natural language processing is rapidly progressing with new AI models released all the time. Finding the right AI model for a use case that balances the performance-price tradeoff requires experimentation on historical data. The flexibility provided by MongoDB’s document model allows the development team to continually enrich historical questions with outputs generated by different models and compare the results. This means that they are not blocked behind complex schema changes that would otherwise slow down the pace of harnessing new data in their models for training and inference. The question-answer pairs output by the company’s NLU models and LLMs are complex data structures with many nested entities and arrays. Being able to persist these directly to the database without first having to transform them into a tabular structure improves developer productivity and reduces application latency. It was this flexibility that was behind the decision to use MongoDB from the outset. “We were building fast, continually testing new features to scale what worked and kill what didn’t,” says Hochgreb. “Only MongoDB provided the developer ease of use and flexibility to meet my time-to-market demands”. The company initially ran MongoDB itself before upgrading to MongoDB Atlas in 2019. “The company was growing fast and I wanted to focus my engineering team on building, not operating. That is exactly what Atlas and its managed service enabled us to do,” says Hochgreb. “With Atlas we were able to maintain high uptime in the face of constant service scaling, with deep monitoring and observability into our platform. In the first year of running in MongoDB Atlas we were able to avoid hiring a full-time infrastructure engineer, and instead redirected the resource into my development team, building new customer features.” GoBots has been able to expand MongoDB usage to deliver even higher value features in its platform over time. It uses MongoDB’s app-driven intelligence to power dashboards that help retailers track questions and complaints, identify opportunities, measure marketing activities, and optimize the customer journey across the marketplace. Its adoption of Atlas Vector Search is the latest example of how the company is expanding application functionality without losing the benefits of building and running on the single, unified Atlas developer data platform. Figure 3: Real-time analytics provide retailers with instant insights to better serve their customers and grow revenue. The results and what's next By working with hundreds of customers running on Latin America’s largest marketplaces, GoBots has built a compelling track record of achievement: By using GoBots AI for ecommerce with MongoDB Atlas, customers have grown sales conversions by 40% and reduced time to customer response by 72%. Looking forward, GoBots adoption of generative AI and vector search will further drive results across the retail marketplace experience. Being part of MongoDB’s AI Innovators Program provides GoBots with free Atlas credits along with access to live technical reviews, helping the company de-risk AI developments. If you are building your own AI-powered apps, apply for the program and take MongoDB Atlas for a spin. It's the quickest way to see why retailers around the world use MongoDB .

March 6, 2024

RegData & MongoDB: Streamline Data Control and Compliance

While navigating the requirements of keeping data secure in highly regulated markets, organizations can find themselves entangled in a web of costly and complex IT systems. Whether it's the GDPR safeguarding European personal data or the Monetary Authority of Singapore's guidelines on outsourcing and cloud computing , the greater the number of regulations organizations are subjected to, particularly across multiple geographical locations, the more intricate their IT infrastructure becomes, and organizations today face the challenge of adapting immediately or facing the consequences. In addition to regulations, customer expectations have become a major driver for innovation and modernization. In the financial sector, for example, customers demand a fast and convenient user experience with real-time access to transaction info, a fully digitized mobile-first experience with mobile banking, and personalization and accessibility for their specific needs. While these sorts of expectations have become the norm, they conflict with the complex infrastructures of modern financial institutions. Many financial institutions are saddled with legacy infrastructure that holds them back from adapting quickly to changing market conditions. Established financial institutions must find a way to modernize, or they risk losing market share to nimble challenger banks with cost-effective solutions. The banking market today is increasingly populated with nimble fintech companies powered by smaller and more straightforward IT systems, which makes it easier for them to pivot quickly. In contrast, established institutions often operate across borders, meaning they must adhere to a greater number of regulations. Modernizing these complex systems requires the simultaneous introduction of new, disruptive technology without violating any regulatory constraints, akin to driving a car while changing a tire. The primary focus for established banks is safeguarding existing systems to ensure compliance with regulatory constraints while prioritizing customer satisfaction and maintaining smooth operations as usual. RegData: Compliance without risk Multi-cloud application security platform, RegData embraces this challenge head-on. RegData has expertise across a number of highly regulated markets, from healthcare to public services, human resources, banking, and finance. The company’s mission is clear—delivering a robust, auditable, and confidential data protection platform within their comprehensive RegData Protection Suite (RPS), built on MongoDB. RegData provides its customers with more than 120 protection techniques , including 60 anonymization techniques, as well as custom techniques (protection of IBANs, SSNs, emails, etc), giving them total control over how sensitive data is managed within each organization. For example, by working with RegData, financial institutions can configure their infrastructure to specific regulations, by masking, encrypting, tokenizing, anonymizing, or pseudonymizing data into compliance. With RPS, company-wide reports can be automatically generated for the regulating authorities (i.e., ACPR, ECB, EU-GDPR, FINMA, etc.). To illustrate the impact of RPS, and to debunk some common misconceptions, let’s explore before and after scenarios. Figure 1 shows the decentralized management of access control. Some data sources employ features such as Field Level Encryption (FLE) to shield data, restricting access to individuals with the appropriate key. Additionally, certain applications implement Role-Based Access Control (RBAC) to regulate data access within the application. Some even come with an Active Directory (AD) interface to try and centralize the configuration. Figure 1: Simplified architecture with no centralized access control However, each of these only addresses parts of the challenge related to encrypting the actual data and managing single-system access. Neither FLE nor RBAC can protect data that isn’t on their data source or application. Even centralizing efforts like the AD interface exclude older legacy systems that might not have interfacing functionalities. The result in all of these cases is a mosaic of different configurations in which silos stay silos, and modernization is risky and slow because the data may or may not be protected. RegData, with its RPS solution, can integrate with a plethora of different data sources as well as provide control regardless of how data is accessed, be it via the web, APIs, files, emails, or others. This allows organizations to configure RPS at a company level. All applications including silos can and should interface with RPS to protect all of the data with a single global configuration. Another important aspect of RPS is its functions with tokenization, allowing organizations to decide which columns or fields from a given data source should be encrypted according to specific standards and govern the access to corresponding tokens. Thanks to tokenization, RPS can track who accesses what data and when they access it at a company level, regardless of the data source or the application. This is easy enough to articulate but quite difficult to execute at a data level. To efficiently manage diverse data sources, fine-grained authorization, and implement different protection techniques, RegData builds RPS on top of MongoDB's flexible and document-oriented database. The road to modernization As noted, to fully leverage RegData’s RPS, all data sources should go through the RPS. RPS works like a data filter, putting in all of the information and extracting protected data on the other side, to modernize and innovate. Just integrating RegData means being able to make previously siloed data available by masking, encrypting, or anonymizing it before sending it out to other applications and systems. Together, RegData and MongoDB form a robust and proven solution for protecting data and modernizing operations within highly regulated industries. The illustration below shows the architecture of a private bank utilizing RPS. Data can only be seen in plain text to database admins when the request comes from the company’s headquarters. This ensures compliance with regulations, while still being able to query and search for data outside the headquarters. This bank goes a step further by migrating their Customer Relationship Management (CRM), core banking, Portfolio Management System (PMS), customer reporting, advisory, tax reporting, and other digital apps into the public cloud. This is achieved while still being compliant and able to automatically generate submittable audit reports to regulating authorities. Figure 2: Private bank business care Another possible modernization scheme—given RegData’s functionalities—is a hybrid cloud Operational Data Layer (ODL), using MongoDB Atlas . This architectural pattern acts as a bridge between consuming applications and legacy solutions. It centrally integrates and organizes siloed enterprise data, rendering it easily available. Its purpose is to offload legacy systems by providing alternative access to information for consuming applications, thereby breaking down data silos, decreasing latency, allowing scalability, flexibility, and availability, and ultimately optimizing operational efficiency and facilitating modernization. RegData integrates, protects, and makes data available, while MongoDB Atlas provides its inherent scalability, flexibility, and availability to empower developers to offload legacy systems. Figure 3: Example of ODL with both RegData and MongoDB In conclusion, in a world where finding the right solutions can be difficult, RegData provides a strategic solution for financial institutions to securely modernize. By combining RegData's regulatory protection and modern cloud platforms such as MongoDB Atlas, the collaboration takes on the modernizing challenge of highly regulated sectors. Are you prepared to harness these capabilities for your projects? Do you have any questions about this? Then please reach out to us at industry.solutions@mongodb.com or info@regdata.ch You can also take a look at the following resources: Hybrid Cloud: Flexible Architecture for the Future of Financial Services Implementing an Operational Data Layer

February 29, 2024

Atlas Data Federation und Online-Archiv können jetzt in Azure bereitgestellt werden

Für Benutzer von Microsoft Azure zeichnen sich spannende Entwicklungen ab, die einen bedeutenden Sprung in den Möglichkeiten der Datenverwaltung darstellen. Zunächst einmal ist Atlas Data Federation jetzt allgemein auf Azure verfügbar. Das bedeutet, dass Sie es jetzt direkt in Azure einsetzen und sogar Daten aus Microsoft Azure Blob Storage abfragen können. Und das ist noch nicht alles. Wir haben auch die allgemeine Verfügbarkeit von Atlas Online Archive auf Azure gestartet. Diese Neuerungen leiten eine neue Ära effizienter Archivierungslösungen für Azure-basierte Datenlösungen ein. Beide Updates sind ein großer Schritt nach vorn, um die Datenverwaltung auf Azure leistungsfähiger und flexibler zu machen. Was das für Sie bedeutet, erfahren Sie im Folgenden! Azure-Unterstützung in Atlas Data Federation (allgemeine Verfügbarkeit) Mit Atlas Data Federation können Benutzer nahtlos Abfragen, Transformationen und Ansichten über mehrere Atlas-Datenbanken und Cloud-Objektspeicherlösungen wie Amazon S3 und jetzt auch Microsoft Azure Blob Storage erstellen. Diese Funktion, die bisher ausschließlich AWS vorbehalten war, ist ein echter Durchbruch. Sie ermöglicht die direkte Bereitstellung in Azure und den Zugriff auf Microsoft Azure Blob Storage für Dateneinblicke. Abbildung 1: Nutzen Sie Azure Blob Storage ganz einfach über die Atlas-Benutzeroberfläche Hauptmerkmale von Atlas Data Federation Cloud-Flexibilität: Wählen Sie zwischen AWS und Azure, um föderierte Datenbankinstanzen zu hosten. Diverse Datenquellen: Binden Sie MongoDB Atlas-Cluster oder Azure-Speicherlösungen (Azure Blob Storage und Azure Data Lake Storage Gen2) als Datenquellen für umfassende Abfragen ein, einschließlich regionsübergreifender Abfragen. Erweiterte Aggregation: Umfassende Aggregationsmöglichkeiten mit Operatoren wie $match, $lookup, $queryHistory, $merge, $out usw. Direkte $out-Unterstützung für Azure Blob Storage und Azure Data Lake Storage Gen2. Atlas SQL-Abfragen auf Azure: Führen Sie SQL-Abfragen auf Azure aus und integrieren Sie dabei MongoDB-Daten für eine einheitliche Analyseerfahrung. Atlas Data Federation vereinfacht den Zugriff auf und die Analyse von komplexen Datensätzen, indem es Daten aus verschiedenen Quellen in einer einzigen, föderierten Ansicht zusammenfasst und so wertvolle Erkenntnisse für fundiertere Geschäftsentscheidungen liefert. Entdecken Sie noch heute Atlas Data Federation auf Azure . Azure-Unterstützung im Atlas Online-Archiv (allgemeine Verfügbarkeit) Die Ausweitung von Atlas Online Archive auf Azure stellt sicher, dass das Daten-Tiering nicht nur effizient, sondern auch integriert ist und die Archivdaten innerhalb der Azure-Umgebung bleiben. Diese Integration behebt die bisherige Einschränkung, dass AWS für die Speicherung standardmäßig verwendet wird, selbst bei in Azure gehosteten Clustern. Abbildung 2: Nahtlose Auswahl von Azure bei der Auswahl einer Region Hauptmerkmale von Atlas Online Archive Wahl des Anbieters: Entscheiden Sie sich für AWS oder Azure, um Ihre Cloud-Strategie zu unterstützen. Automatische Archivierung: Legen Sie Regeln fest, um ältere Daten automatisch in einen kostengünstigen Cloud-Speicher zu verschieben, so dass die manuelle Verlagerung entfällt. Einheitlicher Abfrage-Endpunkt: Greifen Sie auf alle Daten über einen einzigen Endpunkt zu, um schnelle Einblicke zu erhalten, ohne die Datenverfügbarkeit zu beeinträchtigen. Integrierte MongoDB Atlas UI-Verwaltung: Verwalten Sie Ihr Daten-Tiering und Ihre Archivierung über die vertraute Atlas-Oberfläche und optimieren Sie so den Betrieb und die Wartung. Verwalten Sie Ihr MongoDB Atlas-Daten-Tiering nahtlos und in großem Umfang mit Atlas Online Archive. Mit Atlas Online Archive können Sie den Lebenszyklus Ihrer Daten effizient verwalten und dabei ein ausgewogenes Verhältnis zwischen Kosten und Zugänglichkeit schaffen. Abschließend noch ein paar Punkte, die es zu beachten gilt: Jedes neu erstellte Archiv auf einem Azure-Cluster am oder nach dem 28.02. wird standardmäßig in Azure-Regionen erstellt. Beachten Sie, dass Speicherregionen für Online-Archive nur dann standardmäßig auf Azure-Clustern angelegt werden, wenn auf dem betreffenden Azure-Cluster noch keine AWS-Archive vorhanden sind Wenn es bereits bestehende AWS Online-Archive auf Azure-Clustern gibt, verbleiben alle neu erstellten Archive auf diesem speziellen Cluster auf AWS Einmal konfigurierte Cloud-Anbieter oder Speicherregionen können nicht bearbeitet oder geändert werden Nutzen Sie noch heute das volle Potenzial von Atlas Online Archive auf Azure . Wir freuen uns darauf, Sie bei der Datenverwaltung zu unterstützen und Ihnen mit diesen neuen Azure-Funktionen mehr Kontrolle und Flexibilität über Ihre Daten zu bieten. Da MongoDB Atlas als Multi-Cloud-Lösung weiter expandiert, sind wir hier, um sicherzustellen, dass Ihre Datenstrategie so dynamisch und vielseitig ist, wie Ihr Unternehmen es benötigt. Eine Anleitung für die ersten Schritte finden Sie in unserer Dokumentation zu Atlas Data Federation oder Atlas Online Archive . Vielen Dank, dass Sie sich für MongoDB Atlas als Ihre Datenplattform für Entwickler entschieden haben. Willkommen in der Zukunft der Multi-Cloud-Datenverwaltung!

February 29, 2024

Ahora se puede implementar Atlas Data Federation y Online Archive en Azure

Se avecinan desarrollos emocionantes para los usuarios de Microsoft Azure, lo que marca un salto significativo en las capacidades de administración de datos. En primer lugar, Atlas Data Federation ahora cuenta con disponibilidad general en Azure. Esto significa que ahora puede implementarlo de forma directa dentro de Azure e incluso consultar datos de Microsoft Azure Blob Storage. Y eso no es todo. También lanzamos la disponibilidad general de Atlas Online Archive en Azure. Estos avances marcan el comienzo de una nueva era de soluciones de archivado eficaces para soluciones de datos basadas en Azure. Ambas actualizaciones son grandes avances para hacer que la administración de datos en Azure sea más eficaz y flexible. ¡Veamos qué significa esto para usted! Compatibilidad con Azure en Atlas Data Federation (disponibilidad general) Con Atlas Data Federation, los usuarios pueden consultar, transformar y crear vistas sin problemas en múltiples bases de datos de Atlas y soluciones de almacenamiento de objetos en cloud, como Amazon S3 y ahora Microsoft Azure Blob Storage. Esta característica, antes exclusiva para AWS, es revolucionaria, lo que permite la implementación directa dentro de Azure y la capacidad de aprovechar Microsoft Azure Blob Storage para obtener información estratégica de datos. Figura 1: Acceda fácilmente a Azure Blob Storage desde la IU de Atlas Características principales de Atlas Data Federation Flexibilidad en cloud: elija entre AWS y Azure para alojar instancias de bases de datos federadas. Diversas fuentes de datos: incorpora clusters de MongoDB Atlas o soluciones de almacenamiento de Azure (Azure Blob Storage y Azure Data Lake Storage Gen2) como fuentes de datos para realizar consultas completas, incluso entre regiones. Agregación avanzada: capacidades de agregación integrales con operadores que incluyen $match, $lookup, $queryHistory, $merge, $out, etc. Compatibilidad directa de $out para Azure Blob Storage y Azure Data Lake Storage Gen2. Consultas de SQL de Atlas en Azure: ejecute consultas de SQL en Azure, que integra datos de MongoDB para una experiencia de análisis unificada. Atlas Data Federation simplifica el acceso y el análisis de conjuntos de datos complejos al combinar datos de múltiples fuentes en una única visión federada, proporcionando información valiosa para tomar decisiones empresariales más informadas. Explore Atlas Data Federation en Azure hoy mismo . Compatibilidad con Azure en Atlas Online Archive (disponibilidad general) La expansión de Atlas Online Archive a Azure garantiza que la organización de datos por niveles no solo sea eficiente, sino que también esté integrada, manteniendo los datos de archivo dentro del ecosistema de Azure. Esta integración resuelve la limitación anterior de utilizar por defecto AWS para el almacenamiento, incluso para los clusters alojados en Azure. Figura 2: Seleccione Azure al elegir una región Características clave de Atlas Online Archive Opción de proveedor: opte por AWS o Azure para alinearse con su estrategia de cloud. Archivado automático: establezca reglas para mover datos antiguos a un almacenamiento rentable en cloud de forma automática y elimina la descarga manual. Punto de conexión de consulta unificado: acceda a todos los datos a través de un único punto de conexión, lo que garantiza información rápida sin comprometer la disponibilidad de los datos. Gestión integrada de la IU de MongoDB Atlas: gestione el archivado de datos y su organización por niveles desde la conocida interfaz de Atlas, lo que agiliza las operaciones y el mantenimiento. Gestione sin problemas la organización de datos por niveles de MongoDB Atlas a escala con Atlas Online Archive. Atlas Online Archive le permite administrar su ciclo de vida de datos de manera eficiente, equilibrando costos y accesibilidad con facilidad. Finalmente, aquí hay algunos puntos a considerar: Cualquier archivo recién creado en un cluster de Azure a partir del 02/28 será predeterminado para las regiones de Azure. Tenga en cuenta que las regiones de almacenamiento para Online Archive se asignarán por defecto a clústeres de Azure sólo si no hay archivos de AWS preexistentes en ese cluster de Azure específico Si hay archivos AWS Online preexistentes en clusters de Azure, todos los archivos recién creados en ese cluster específico permanecerán en AWS. Los proveedores de cloud o las regiones de almacenamiento no se pueden editar ni modificar una vez configurados Aproveche todo el potencial de Atlas Online Archive en Azure hoy mismo . Estamos encantados de dar asistencia técnica a su recorrido de gestión de datos, lo que ofrece mayor control y flexibilidad sobre sus datos a través de estas nuevas capacidades de Azure. A medida que MongoDB Atlas continúa expandiéndose como una solución multi-cloud, estamos aquí para garantizar que su estrategia de datos sea tan dinámica y versátil como su negocio. Para obtener una guía introductoria, consulte nuestra documentación sobre Atlas Data Federation o Atlas Online Archive . Gracias por confiar en MongoDB Atlas como su plataforma de datos para desarrolladores. Bienvenido al futuro de la administración de datos multi-cloud

February 29, 2024

Atlas Data Federation et Online Archive peuvent désormais être déployés dans Azure

Des développements prometteurs se profilent à l'horizon pour les utilisateurs de Microsoft Azure, marquant une avancée significative dans les capacités de gestion des données. Tout d'abord, Atlas Data Federation est désormais disponible sur Azure. Cela signifie que vous pouvez désormais le déployer directement dans Azure et même interroger des données de Microsoft Azure Blob Storage. Et ce n'est pas tout. Nous avons également lancé la disponibilité générale d'Atlas Online Archive sur Azure. Ces avancées s'appuient dans une nouvelle ère de solutions d'archivage efficaces pour les solutions de données basées sur Azure. Ces deux mises à jour constituent un grand pas en avant pour rendre la gestion des données sur Azure plus puissante et plus flexible. Voyons ce que cela signifie pour vous ! Prise en charge d'Azure dans Atlas Data Federation (disponibilité générale) Avec Atlas Data Federation, les utilisateurs peuvent interroger, transformer et créer des vues en toute transparence sur plusieurs bases de données Atlas et solutions de stockage d'objets sur le cloud, telles qu'Amazon S3 et maintenant Microsoft Azure Blob Storage. Cette fonctionnalité, auparavant exclusive à AWS, change la donne en permettant un déploiement direct au sein d'Azure et la possibilité de puiser dans Microsoft Azure Blob Storage pour obtenir des informations sur les données. Figure 1 : accéder facilement à Azure Blob Storage depuis l'interface utilisateur d'Atlas Principales fonctionnalités d'Atlas Data Federation Flexibilité du cloud : choisissez entre AWS et Azure pour héberger des instances de bases de données fédérées. Diverses sources de données : intégrez des clusters MongoDB Atlas ou des solutions de stockage Azure (Azure Blob Storage et Azure Data Lake Storage Gen2) en tant que sources de données pour des requêtes complètes, y compris interrégionales. Agrégation avancée : capacités d'agrégation complètes avec des opérateurs tels que $match, $lookup, $queryHistory, $merge, $out, etc. Prise en charge directe de $out pour Azure Blob Storage et Azure Data Lake Storage Gen2. Requêtes SQL Atlas sur Azure : exécutez des requêtes SQL sur Azure, en intégrant les données MongoDB pour une expérience d'analyse unifiée. Atlas Data Federation simplifie l'accès et l'analyse d'ensembles de données complexes en combinant des données provenant de sources multiples en une vue unique et fédérée, ce qui permet d'obtenir des informations précieuses pour prendre des décisions plus éclairées. Découvrez Atlas Data Federation sur Azure dès aujourd'hui . Prise en charge d'Azure dans Atlas Online Archive (disponibilité générale) L'extension d'Atlas Online Archive à Azure garantit que la hiérarchisation des données est non seulement efficace mais aussi intégrée, en conservant les données d'archives au sein de l'écosystème Azure. Cette intégration permet de remédier à la limitation précédente qui consistait à utiliser par défaut AWS pour le stockage, même pour les clusters hébergés sur Azure. Figure 2 : sélectionner Azure sans problème lors du choix d'une région Principales fonctionnalités d'Atlas Online Archive Choix du fournisseur : optez pour AWS ou Azure pour vous aligner sur votre stratégie cloud. Archivage automatique : définissez des règles pour transférer automatiquement les données plus anciennes vers un stockage cloud rentable, éliminant ainsi le déchargement manuel. Point de terminaison unifié pour les requêtes : accédez à toutes les données depuis un point de terminaison unique, ce qui vous permet d'obtenir rapidement des informations sans compromettre la disponibilité des données. Gestion intégrée de l'interface MongoDB Atlas : gérez la hiérarchisation et l'archivage de vos données dans l'interface familière d'Atlas, ce qui simplifie les opérations et la maintenance. Gérez vos données MongoDB Atlas en toute transparence et à grande échelle avec Atlas Online Archive. Atlas Online Archive vous permet de gérer efficacement le cycle de vie de vos données, en équilibrant facilement les coûts et l'accessibilité. Enfin, voici quelques points à prendre en considération : Toute archive nouvellement créée sur un cluster Azure le ou après le 28 février sera par défaut dans les régions Azure. Notez que les régions de stockage pour Online Archive seront par défaut des clusters Azure seulement s'il n'y a pas d'archives AWS préexistantes sur ce cluster Azure spécifique S'il existe des clusters AWS Online Archives on Azure préexistants, toutes les archives nouvellement créées sur ce cluster spécifique resteront sur AWS. Les fournisseurs cloud ou les régions de stockage ne peuvent pas être édités ou modifiés une fois qu'ils ont été configurés Exploitez tout le potentiel d'Atlas Online Archive sur Azure dès aujourd'hui . Nous sommes ravis de vous accompagner dans votre démarche de gestion des données, en vous offrant un meilleur contrôle et une plus grande flexibilité sur vos données grâce à ces nouvelles fonctionnalités Azure. Alors que MongoDB Atlas continue de se développer en tant que solution multi-cloud, nous sommes là pour veiller à ce que votre stratégie de données soit aussi dynamique et polyvalente que les besoins de votre entreprise. Pour vous aider à vous lancer, consultez notre documentation sur Atlas Data Federation ou Atlas Online Archive . Nous vous remercions d'avoir choisi MongoDB Atlas comme plateforme de données pour développeurs. Bienvenue dans l'avenir de la gestion des données multicloud !

February 29, 2024

Atlas Data Federation e Online Archive possono ora essere distribuiti in Azure

Per gli utenti di Microsoft Azure si prospettano sviluppi entusiasmanti, che segneranno un salto significativo nelle capacità di gestione dei dati. Innanzitutto, Atlas Data Federation è ora disponibile a livello generale in Azure. Ciò significa che ora puoi distribuirlo direttamente all'interno di Azure e persino interrogare i dati da Microsoft Azure Blob Storage. Ma non è tutto: abbiamo anche lanciato la disponibilità generale di Atlas Online Archive su Azure. Questi progressi inaugurano una nuova era di soluzioni di archiviazione efficienti per soluzioni di dati basate su Azure. Entrambi gli aggiornamenti rappresentano grandi passi avanti nel rendere la gestione dei dati su Azure più potente e flessibile. Capiamo più da vicino ciò che questo significa per te! Supporto tecnico di Azure in Atlas Data Federation (disponibilità generale) Con Atlas Data Federation, gli utenti possono eseguire query, trasformare e creare visualizzazioni su più database Atlas e soluzioni di archiviazione di oggetti cloud, come Amazon S3 e ora Microsoft Azure Blob Storage. Questa funzionalità, precedentemente esclusiva di AWS, rappresenta una vera e propria svolta in quanto consente la distribuzione diretta in Azure e la possibilità di attingere all'archiviazione BLOB di Microsoft Azure per ottenere informazioni dettagliate sui dati. Figura 1: Accedi facilmente ad Azure Blob Storage dall'interfaccia utente di Atlas Caratteristiche principali di Atlas Data Federation Flessibilità cloud: scegli tra AWS e Azure per ospitare istanze di database federate. Fonti di dati diversificate: incorpora i cluster MongoDB Atlas o le soluzioni di archiviazione di Azure (Azure Blob Storage e Azure Data Lake Storage Gen2) come origini dati per query complete, anche interregionali. Aggregazione avanzata: funzionalità di aggregazione complete con operatori comprensivi di $match, $lookup, $queryHistory, $merge, $out ecc. Supporto diretto $out per Azure Blob Storage e Azure Data Lake Storage Gen2. Query SQL di Atlas su Azure: esegui query SQL su Azure, integrando i dati MongoDB per un'esperienza di analisi unificata. Atlas Data Federation semplifica l'accesso e l'analisi di set di dati complessi combinando dati su più fonti in un'unica vista federata, fornendo preziose informazioni per decisioni aziendali più informate. Esplora subito Atlas Data Federation in Azure . Assistenza Azure su Atlas Online Archive (disponibilità generale) L'espansione di Atlas Online Archive su Azure garantisce che il tiering dei dati non sia solo efficiente ma anche integrato, mantenendo i dati di archivio all'interno dell'ecosistema Azure. Questa integrazione risolve la precedente limitazione dell'impostazione predefinita di AWS per lo storage, anche per i cluster ospitati in Azure. Figura 2: selezionare facilmente Azure quando si sceglie una regione Funzionalità principali dell'archivio online di Atlas Scelta del provider: scegli AWS o Azure per allinearti alla tua strategia cloud. Archiviazione automatica: imposta regole per spostare automaticamente i dati più vecchi in uno storage cloud conveniente, eliminando l'offload manuale. Endpoint di query unificato: accedi a tutti i dati tramite un unico endpoint per garantire insight rapidi senza compromettere la disponibilità dei dati. Gestione integrata dell'IU MongoDB Atlas: gestisci il tiering e l'archiviazione dei dati all'interno della consueta interfaccia Atlas, semplificando le operazioni e la manutenzione. Gestisci senza problemi il tiering dei dati MongoDB Atlas su larga scala con Atlas Online Archive, che consente di gestire il ciclo di vita dei dati in modo efficiente, bilanciando i costi e l'accessibilità con facilità. Infine, ecco alcuni punti da considerare: Qualsiasi archivio appena creato in un cluster di Azure a partire dal 28/02 verrà impostato per impostazione predefinita sulle regioni di Azure. Tieni presente che le regioni di storage per l'archivio online verranno impostate automaticamente sui cluster di Azure solo se non sono presenti archivi AWS preesistenti su quello specifico cluster di Azure Se sono presenti archivi AWS Online preesistenti su cluster di Azure, tutti gli archivi appena creati su quel cluster specifico rimarranno su AWS I provider di cloud o le regioni di archiviazione non possono essere modificati una volta configurati Sfrutta subito tutto il potenziale di Atlas Online Archive in Azure . Siamo entusiasti di supportare il tuo percorso di gestione dei dati, offrendo maggiore controllo e flessibilità sui tuoi dati attraverso queste nuove funzionalità di Azure. Con l'espansione di MongoDB Atlas come soluzione multi-cloud, siamo qui per garantire che la tua strategia dati sia dinamica e versatile quanto le esigenze della tua azienda. Per istruzioni su come iniziare, consulta la nostra documentazione su Atlas Data Federation o Atlas Online Archive . Grazie per aver scelto MongoDB Atlas come piattaforma dati per sviluppatori. Benvenuti nel futuro della gestione dei dati multi-cloud!

February 29, 2024

O Atlas Data Federation e o Online Archive agora podem ser implantados no Azure

Desenvolvimentos interessantes estão no horizonte para os usuários do Microsoft Azure, marcando um salto significativo nos recursos de gerenciamento de dados. Em primeiro lugar, o Atlas Data Federation agora está disponível de forma geral no Azure. Isso significa que agora você pode implantá-lo diretamente no Azure e até mesmo consultar dados do Blob Storage do Microsoft Azure. E isso não é tudo. Também lançamos a disponibilidade geral do Atlas Online Archive no Azure. Esses avanços dão início a uma nova era de soluções de arquivamento eficientes para soluções de dados baseadas no Azure. Ambas as atualizações são grandes avanços para tornar o gerenciamento de dados no Azure mais poderoso e flexível. Vamos nos aprofundar no que isso significa para você! Suporte ao Azure na Atlas Data Federation (disponibilidade geral) Com o Atlas Data Federation, os usuários podem consultar, transformar e criar visualizações em vários bancos de dados do Atlas e em soluções de armazenamento de objetos em cloud, como o Amazon S3 e, agora, o Microsoft Azure Blob Storage. Esse recurso, antes exclusivo do AWS, é um divisor de águas, permitindo a implantação direta no Azure e a capacidade de acessar o Blob Storage do Microsoft Azure para obter insights de dados. Figura 1: acesse o armazenamento de Blobs do Azure facilmente a partir da UI do Atlas Principais recursos do Atlas Data Federation Flexibilidade de cloud: escolha entre AWS e Azure para hospedar instâncias de bancos de dados federados. Diversas fontes de dados: incorpore as soluções de armazenamento do MongoDB Atlas cluster ou do Azure (Azure Blob Storage e Azure Data Lake Storage Gen2) como fontes de dados para queries abrangentes, inclusive entre regiões. Agregação avançada: recursos abrangentes de agregação com operadores que incluem $match, $lookup, $queryHistory, $merge, $out, etc. Suporte direto de $out para o Armazenamento de Blobs do Azure e o Armazenamento de Data Lake do Azure Gen2. Consultas SQL do Atlas no Azure: execute consultas SQL no Azure, integrando dados do MongoDB para uma experiência de análise unificada. A Atlas Data Federation simplifica o acesso e a análise de conjuntos de dados complexos, combinando dados de várias fontes em uma única visualização federada, fornecendo insights valiosos para decisões comerciais mais informadas. Explore o Atlas Data Federation no Azure hoje mesmo . Suporte ao Azure no Atlas Online Archive (disponibilidade geral) A expansão do Atlas Online Archive para o Azure garante que a classificação dos dados em camadas não seja apenas eficiente, mas também integrada, mantendo os dados de arquivamento no ecossistema do Azure. Essa integração aborda a limitação anterior de usar o AWS como padrão para o armazenamento, mesmo para o cluster hospedado no Azure. Figura 2: selecione facilmente o Azure ao escolher uma região Principais recursos do Atlas Online Archive Escolha do provedor: opte pelo AWS ou Azure para se alinhar com sua estratégia de cloud. Arquivamento automático: defina regras para mover automaticamente os dados mais antigos para um armazenamento econômico em cloud, eliminando o descarregamento manual. Endpoint de consulta unificado: acesse todos os dados por meio de um único endpoint, garantindo insights rápidos sem comprometer a disponibilidade dos dados. Gerenciamento integrado da interface do usuário do MongoDB Atlas: gerencie sua classificação e arquivamento de dados na interface familiar do Atlas, simplificando as operações e a manutenção. Gerencie seu armazenamento em camadas de dados do MongoDB Atlas em escala com o Atlas Online Archive. O Atlas Online Archive permite que você gerencie seu ciclo de vida de dados com eficiência, equilibrando custo e acessibilidade com facilidade. Por fim, aqui estão alguns pontos a serem considerados: Qualquer arquivo recém-criado em um cluster do Azure em 28/02 ou após essa data terá como padrão as regiões do Azure. Observe que as regiões de armazenamento do Online Archive serão padronizadas para clusters do Azure somente se não houver arquivos do AWS pré-existentes nesse cluster específico do Azure Se houver arquivos on-line do AWS pré-existentes em clusters do Azure, todos os arquivos recém-criados nesse cluster específico permanecerão no AWS Provedores de cloud e regiões de armazenamento não podem ser editados ou modificados depois de configurados Aproveite todo o potencial do Atlas Online Archive no Azure hoje mesmo . Estamos entusiasmados em dar suporte à sua jornada de gerenciamento de dados, oferecendo controle e flexibilidade aprimorados sobre seus dados por meio desses novos recursos do Azure. À medida que o MongoDB Atlas continua a se expandir como uma solução multicloud, estamos aqui para garantir que sua estratégia de dados seja tão dinâmica e versátil quanto suas necessidades de negócios. Para obter orientação sobre como começar, consulte nossa documentação sobre o Atlas Data Federation ou o Atlas Online Archive . Obrigado por confiar no MongoDB Atlas como sua plataforma de dados para desenvolvedores. Bem-vindo ao futuro do gerenciamento de dados multicloud!

February 29, 2024

Atlas Data Federation과 Online Archive, 이제 Azure에서 배포 가능

Microsoft Azure 사용자에게 데이터 관리 기능의 혁신적 발전이 곧 찾아옵니다. 우선, 이제 Azure에서 Atlas Data Federation 이 정식 출시되었습니다. 이제 Azure 내에서 직접 배포하고 Microsoft Azure Blob Storage에서 데이터를 쿼리할 수도 있습니다. 이뿐만이 아닙니다. 또한 Azure에서 Atlas Online Archive 를 정식 버전으로 출시했습니다. 이러한 발전은 Azure 기반 데이터 솔루션을 위한 효율적인 아카이빙 솔루션의 새로운 시대를 열었습니다. 두 업데이트 모두 Azure의 데이터 관리를 더욱 강력하고 유연하게 만드는 큰 진전입니다. 이것이 여러분에게 어떤 의미인지 자세히 알아봅시다! Atlas Data Federation의 Azure 지원(정식 출시) Atlas Data Federation을 이용하면 사용자는 Amazon S3와 최근 지원이 추가된 Microsoft Azure Blob Storage를 포함한 여러 Atlas 데이터베이스와 클라우드 객체 스토리지 솔루션 간에 원활하게 쿼리를 실행하고, 데이터를 변환하며, 뷰를 생성할 수 있습니다 이전에는 AWS에서만 제공되던 이 기능은 Azure 내 직접 배포와 Microsoft Azure Blob Storage를 활용한 데이터 인사이트 획득을 가능하게 하는 혁신적인 변화입니다. 그림 1: Atlas UI에서 Azure Blob Storage를 쉽게 활용 Atlas Data Federation의 주요 기능 클라우드 유연성: 페더레이션 데이터베이스 인스턴스 호스팅을 위해 AWS와 Azure 중에서 선택하세요. 다양한 데이터 소스 활용: MongoDB Atlas 클러스터 또는 Azure 스토리지 솔루션(Azure Blob Storage 및 Azure Data Lake Storage Gen2)을 데이터 소스로 활용하여 사용자는 여러 리전에 걸친 포괄적인 쿼리를 수행할 수 있습니다. 고급 집계: $match, $lookup, $queryHistory, $merge, $out 등을 포함하는 연산자를 사용한 포괄적인 집계 기능을 제공합니다. Azure Blob Storage 및 Azure Data Lake Storage Gen2에 대한 직접적인 $out 지원을 제공합니다. Azure에서 Atlas SQL 쿼리 실행: MongoDB 데이터와 통합하여 Azure에서 SQL 쿼리를 실행함으로써 통합된 분석 경험을 제공합니다. Atlas Data Federation은 여러 소스에서 온 데이터를 단일 페더레이션 뷰로 결합하여 복잡한 데이터 세트에 대한 액세스 및 분석을 간소화하고, 보다 정보에 기반한 비즈니스 의사 결정을 지원하기 위한 귀중한 인사이트를 제공합니다. 지금 Azure의 Atlas Data Federation을 살펴보세요 . Atlas Online Archive의 Azure 지원(정식 출시) Atlas Online Archive의 Azure 확장으로, 데이터 계층화는 이제 효율적일 뿐만 아니라 Azure 에코시스템과 완전히 통합되어 아카이브 데이터의 Azure 내 보존을 보장합니다. 이 통합은 Azure에서 호스팅되는 클러스터의 경우에도 스토리지를 AWS로 기본 설정하는 이전 제한 사항을 해결합니다. 그림 2: 리전 선택 시 Azure를 원활하게 선택 Atlas Online Archive의 주요 기능 제공업체 선택: 클라우드 전략에 맞게 AWS 또는 Azure를 선택하세요. 자동 아카이빙: 오래된 데이터를 비용 효율적인 클라우드 스토리지로 자동으로 이동하는 규칙을 설정하여 수동 오프로딩을 없애세요. 통합 쿼리 엔드포인트: 단일 엔드포인트를 통해 모든 데이터에 액세스하여 데이터 가용성 저하 없이 빠른 인사이트를 확보할 수 있습니다. 통합된 MongoDB Atlas UI 관리: 익숙한 Atlas 인터페이스 내에서 데이터 계층화 및 아카이빙을 관리하여 운영 및 유지 관리를 간소화하세요. Atlas Online Archive를 통해 대규모로 MongoDB Atlas 데이터 계층화를 원활하게 관리하세요. Atlas Online Archive를 사용하면 데이터 라이프사이클을 효율적으로 관리하여 비용과 접근성의 균형을 쉽게 맞출 수 있습니다. 마지막으로 몇 가지 고려해야 할 사항이 있습니다. 2월 28일 이후에 Azure 클러스터에 새로 생성된 모든 아카이브는 기본적으로 Azure 리전으로 설정됩니다. Online Archive의 스토리지 리전은 해당 특정 Azure 클러스터에 기존 AWS 아카이브가 없는 경우에만 Azure 클러스터로 기본 설정됩니다. Azure 클러스터에 기존 AWS Online Archive가 있는 경우, 해당 특정 클러스터에 새로 생성된 모든 아카이브는 AWS에 유지됩니다. 클라우드 제공업체 또는 스토리지 리전은 한 번 구성하면 편집하거나 수정할 수 없습니다. 지금 Azure에서 Atlas Online Archive의 잠재력을 최대한 활용하세요 . 새로운 Azure 기능을 통해 데이터에 대한 향상된 제어 및 유연성을 제공하여 데이터 관리 여정을 지원하게 되어 기쁘게 생각합니다. MongoDB Atlas가 멀티 클라우드 솔루션으로 계속 확장함에 따라, MongoDB Atlas 팀은 비즈니스 요구에 부합하는 역동적이고 유연한 데이터 전략을 제공하는 데 전념하고 있습니다. 시작하는 방법에 대한 지침은 Atlas Data Federation 또는 Atlas Online Archive 에 대한 설명서를 참조하세요. 개발자 데이터 플랫폼으로 MongoDB Atlas를 신뢰해 주셔서 감사합니다. 멀티 클라우드 데이터 관리의 미래에 오신 것을 환영합니다!

February 29, 2024