Atlas Data Federation 和 Online Archive 现在可以部署在 Azure 中

Karthic Subramanian and Sabina Friden
February 29, 2024 | Updated: March 18, 2024

Microsoft Azure 用户即将迎来激动人心的发展，这标志着数据管理能力的重大飞跃。首先，Atlas Data Federation 现已在 Azure 上正式推出。这意味着您现在可以直接在 Azure 中部署它，甚至可以从 Microsoft Azure Blob Storage 查询数据。这还不是全部。我们还在 Azure 上全面推出了 Atlas Online Archive。这些进步实现了基于 Azure 的数据解决方案，开创了高效存档解决方案的新时代。

这两项更新都是巨大进步，令 Azure 上的数据管理变得更强大和更灵活。让我们深入了解这对您意味着什么！

Atlas Data Federation 中的 Azure 支持（正式版）

借助 Atlas Data Federation，用户可以跨多个 Atlas 数据库和云对象存储解决方案（例如 Amazon S3 和现在的 Microsoft Azure Blob Storage）无缝查询、转换和创建视图。此功能以前是 AWS 独有的，现在是变局者，允许在 Azure 中直接部署，并能够利用 Microsoft Azure Blob Storage 获取数据见解。

Atlas Data Federation 的主要功能

云灵活性：在 AWS 和 Azure 之间进行选择来托管联合数据库实例。
多样化的数据源：将 MongoDB Atlas 集群或 Azure 存储解决方案（Azure Blob Storage 和 Azure Data Lake Storage Gen2）整合为数据源，以进行全面查询，包括跨区域查询。
高级聚合：全面的聚合功能，包括 $match、$lookup、$queryHistory、$merge、$out 等操作符。
- 为 Azure Blob Storage 和 Azure Data Lake Storage Gen2 提供直接 $out 支持。
Azure 上的 Atlas SQL 查询：在 Azure 上执行 SQL 查询，集成 MongoDB 数据以获得统一的分析体验。

Atlas Data Federation 通过将多个来源的数据组合到单个联合视图中，简化了复杂数据集的访问和分析，为更明智的业务决策提供了宝贵的见解。

立即探索 Azure 上的 Atlas Data Federation。

Atlas Online Archive 中的 Azure 支持（正式版）

Atlas Online Archive 扩展到 Azure，确保数据层级不仅高效，而且集成，将存档数据保留在 Azure 生态系统中。此集成解决了之前默认使用 AWS 进行存储的限制，即使对于 Azure 托管的集群也是如此。

Atlas Online Archive 的主要功能

提供商选择：选择 AWS 或 Azure，以配合您的云战略。
自动存档：设置规则以自动将旧数据移动到经济高效的云存储，从而消除手动卸载。
统一查询端点：通过单个端点访问所有数据，确保在不影响数据可用性的情况下快速获得见解。
集成 MongoDB Atlas 用户界面管理：在熟悉的 Atlas 界面中管理数据层级和存档，简化操作和维护。

使用 Atlas Online Archive 大规模无缝管理 MongoDB Atlas 数据层级。 Atlas Online Archive 使您能够高效管理数据生命周期，轻松平衡成本和可访问性。

最后，有几点需要考虑：

2 月 28 日当天或之后在 Azure 集群上新创建的任何存档都将默认为 Azure 区域。请注意，只有当特定 Azure 集群上没有预先存在的 AWS 存档时，Online Archive 的存储区域才会默认为 Azure 集群
如果 Azure 集群上存在任何预先存在的 AWS Online Archives，则该特定集群上所有新创建的存档都将保留在 AWS 上。
云提供商或存储区域一旦配置就无法编辑或修改

立即在 Azure 上充分发挥 Atlas Online Archive 的潜力。

我们很高兴能够支持您的数据管理之旅，通过这些新的 Azure 功能增强对数据的控制和灵活性。随着 MongoDB Atlas 不断扩展为多云解决方案，我们将确保您的数据策略与您的业务需求一样动态且多功能。

有关入门指导，请查看我们的 Atlas Data Federation 或 Atlas Online Archive 文档。

感谢您信任 MongoDB Atlas 作为您的开发者数据平台。欢迎来到多云数据管理的未来！

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Atlas Data Federation and Online Archive Can Now Be Deployed in Azure

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Exciting developments are on the horizon for users of Microsoft Azure, marking a significant leap in data management capabilities. First off, Atlas Data Federation is now Generally Available on Azure. This means you can now deploy it directly within Azure and even query data from Microsoft Azure Blob Storage. And that's not all. We've also launched the General Availability of Atlas Online Archive on Azure. These advancements usher in a new era of efficient archiving solutions for Azure-based data solutions. Both updates are big steps forward in making data management on Azure more powerful and flexible. Let's dive into what this means for you! Azure support in Atlas Data Federation (General Availability) With Atlas Data Federation, users can seamlessly query, transform, and create views across multiple Atlas databases and cloud object storage solutions, such as Amazon S3 and now Microsoft Azure Blob Storage. This feature, previously exclusive to AWS, is a game-changer, allowing direct deployment within Azure and the ability to tap into Microsoft Azure Blob Storage for data insights. Figure 1: Tap into Azure Blob Storage easily from the Atlas UI Key features of Atlas Data Federation Cloud flexibility: Choose between AWS and Azure for hosting federated database instances. Diverse data sources: Incorporate MongoDB Atlas clusters or Azure storage solutions (Azure Blob Storage and Azure Data Lake Storage Gen2) as data sources for comprehensive queries, including cross-region. Advanced aggregation: Comprehensive aggregation capabilities with operators inclusive of $match, $lookup, $queryHistory, $merge, $out, etc. Direct $out support for Azure Blob Storage and Azure Data Lake Storage Gen2. Atlas SQL queries on Azure: Execute SQL queries on Azure, integrating MongoDB data for a unified analysis experience. Atlas Data Federation simplifies accessing and analyzing complex data sets by combining data across multiple sources into a single, federated view, providing valuable insights for more informed business decisions. Explore Atlas Data Federation on Azure Today . Azure support in Atlas Online Archive (General Availability) Atlas Online Archive's expansion to Azure ensures that data tiering is not only efficient but also integrated, keeping archival data within the Azure ecosystem. This integration addresses the previous limitation of defaulting to AWS for storage, even for Azure-hosted clusters. Figure 2: Seamlessly select Azure when choosing a region Key features of Atlas Online Archive Provider choice: Opt for AWS or Azure to align with your cloud strategy. Automatic archiving: Set rules to move older data to cost-effective cloud storage automatically, eliminating manual offloading. Unified querying endpoint: Access all data through a single endpoint, ensuring quick insights without compromising data availability. Integrated MongoDB Atlas UI management: Manage your data tiering and archiving within the familiar Atlas interface, streamlining operations and maintenance. Seamlessly manage your MongoDB Atlas data tiering at scale with Atlas Online Archive. Atlas Online Archive empowers you to manage your data lifecycle efficiently, balancing cost and accessibility with ease. Finally, here are a few points to consider: Any newly created archive on an Azure cluster on or after 02/28 will default to Azure regions. Note that storage regions for Online Archive will default to Azure clusters only if there are no pre-existing AWS archives on that specific Azure cluster If there are any pre-existing AWS Online Archives on Azure clusters, then all newly created archives on that specific cluster will remain on AWS. Cloud providers or storage regions cannot be edited or modified once configured Embrace the Full Potential of Atlas Online Archive on Azure Today . We're thrilled to support your data management journey, offering enhanced control and flexibility over your data through these new Azure capabilities. As MongoDB Atlas continues to expand as a multi-cloud solution, we're here to ensure your data strategy is as dynamic and versatile as your business needs. For guidance on getting started, check out our documentation on Atlas Data Federation or Atlas Online Archive . Thank you for trusting MongoDB Atlas as your Developer Data Platform. Welcome to the future of multi-cloud data management!

February 29, 2024

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024