Atlas Edge Server is Now in Public Preview

We’re excited to announce that Atlas Edge Server is now in public preview! Any developer on Atlas can now deploy Edge Server for their connected infrastructure. Learn more in our docs or get started today.

Developers value MongoDB’s developer data platform for the flexibility and ease of use of the document model, as well as for helpful tools like search and charts that simplify data management. As a crucial component of our Atlas for the Edge solution, Atlas Edge Server extends these capabilities to remote and network-constrained environments.

First announced at MongoDB.local London 2023, Atlas for the Edge enables local data processing and management within edge environments and across edge devices, reducing latency, enhancing performance, and allowing for disconnection resilience.

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What's new in public preview?

One of our top priorities is providing developers with a seamless experience when managing their data and applications. We continuously seek to enhance this experience, which is why, starting today, Atlas Edge Server can be directly downloaded, configured, and managed through the Atlas UI.

Developers who deploy from the Atlas UI will be able to choose between two onboarding flows to ensure that their configuration is tailored to their needs. This includes both developers who want to connect their edge server with a MongoDB driver or client, and those who want to support connecting to the Edge Server via Device Sync.

Why Atlas Edge Server?

While edge computing brings data processing closer to end-users and offers substantial benefits, such as network resilience and increased security, a number of challenges inherent to edge computing can make it difficult to fully leverage. Edge computing challenges include managing complex networks, handling large volumes of data, and addressing security concerns, any of which can deter organizations from adopting edge computing. Additionally, the costs associated with building, maintaining, and scaling edge computing systems can be significant.

Atlas for the Edge and Atlas Edge Server alleviate these challenges. Atlas Edge Server provides a MongoDB instance equipped with a synchronization server that can be deployed on local or remote infrastructure. It enables real-time synchronization, conflict resolution, and disconnection tolerance. This ensures that mission-critical applications and devices operate seamlessly, even with intermittent connectivity. Edge Server allows for selective synchronization of only modified fields, conserving bandwidth and prioritizing crucial data transfers to Atlas. It also maintains edge client functionality even with intermittent cloud connectivity, preventing disruptions to essential operations like inventory management and point-of-sale systems. Processing data locally reduces latency and enables rapid data insights, reducing dependency on central databases.

We'll meet you at the edge

The Public Preview of Atlas Edge Server underscores MongoDB’s ongoing commitment to enhancing our developer data platform for distributed infrastructures. As we continue to invest in Atlas for the Edge, MongoDB’s goal is to equip teams with a robust data solution that not only offers an exceptional developer experience but also empowers them to drive innovative solutions for their businesses and customers.

Get started today, or visit the Atlas for the Edge web page to learn more about how companies are benefiting from our edge solution.

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提升数据库性能：在 MongoDB Atlas 中引入 Query Insights

今天，MongoDB Atlas 在 .local 推出新的 Query Insights（查询见解）标签页，以便增强用户直接在 Atlas 用户界面中监控、管理和优化数据库性能的方式。此项新功能可通过更强大的查询分析工具和详细的命名空间级指标，让开发者更深入地了解数据库的性能，从而更快地解决问题并提高性能。 随着时间的推移，应用程序和工作负载也会发生变化，因此越发难以追踪造成数据库资源紧张的低效查询。指标可能会因各种原因而激增，因此开发者需要正确的工具来确定问题的根源，以便能快速识别和解决此问题。MongoDB Atlas 的 Query Insights 通过 Namespace Insights（命名空间见解）和升级后的查询分析器这两个关键功能来提升 MongoDB 的可观察性，从而直接应对这些挑战。 Query Insights 可通过可行的情报来实现性能优化 MongoDB Atlas Query Insights 的推出表明了 MongoDB 对实现高级数据库管理的承诺。此功能可通过详细且可操作的见解来增强平台的可观察性能力。此功能将命名空间见解和升级后的查询分析器集成到一个新的动态界面中，以便通过简化诊断和缩短故障排除时间来帮助提高数据库性能。 新增的 Namespace Insights 可为用户提供集合级的延迟统计信息，并全面了解集群上最热门的集合在一段时间内的性能。如此一来，开发者便可回答“谁或什么导致了此问题？”，从而有助于识别性能趋势和确定查询优化措施的优先级。 增强后的以集群为中心的查询分析器引入了更全面的视图，以显示更长时间段内缓慢且低效的查询。掌握整个集群中数据的整体情况有助于在各节点之间实现更顺畅的导航，并可延长回溯期以识别趋势。如此，最终便可缩短故障排除时间，从而提高开发者的工作效率并改善整体数据库性能。 Query Insights 的主要优点 Query Insights 可为 MongoDB Atlas 用户提供多项新的优点，其中包括： 精细化遥测： 利用命名空间级延迟统计信息更快地识别和解决数据库问题 提高可观察性： 更轻松地发现性能趋势、确定根本原因和调试应用程序 提高工作效率： 更全面地了解慢速操作，从而缩短故障排除时间 进行试用！ Query Insights 页面可通过呈现集合与操作级别的详细信息来提供针对数据库性能的更精细见解。Namespace Insights>（命名空间见解>）页面可提供按总延迟时间划分的前 20 个集合的对应指标。 将鼠标悬停在图表上可查看集合在一段时间内的相对性能。这些信息有助于回答以下问题：“谁/什么导致了问题？” 使用查询分析器可查看特定的慢速操作。单击散点图中的某一点可显示有关每个慢速操作的其他元数据。 单击" View More Details "（查看更多详细信息）可查看有关每个慢速操作的更多指标和元数据，其中包括应用名称、操作、计划摘要、执行统计信息等。 帮助用户实现最佳性能 MongoDB Atlas 中 Query Insights 的推出突显了 MongoDB 对增强我们平台可观察性能力的承诺。通过为用户提供优化数据库性能所需的工具和见解，MongoDB 可助力开发者缩短调试时间并将更多时间用于创造，从而降低总体拥有成本、最大限度地提高效率并为用户的运营注入巨大价值。 注册我们的云数据库服务 MongoDB Atlas ，以便查看 Query Insights 的实际应用。有关更多信息，请参阅“ 监控查询性能 ”。

May 2, 2024
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Payments Modernization and the Role of the Operational Data Layer

To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms. Driven by multiple factors—real-time payments, regulatory shifts like Payment Services Directive 2 (PSD2), heightened customer expectations, the power of open banking, and the disruptive force of fintech startups—the need for payment modernization has never been more pressing. But transformation is not without its challenges. Complex systems, industry reliance on outdated technology, high upgrade costs, and technical debt all pose formidable obstacles. This article will explore modernization approaches and how MongoDB helps smooth transformations. Approaches to modernization As businesses work to modernize their payment systems, they need to overcome the complexities inherent in updating legacy systems. Forward-thinking organizations embrace innovative strategies to streamline their operations, enhance scalability, and facilitate agile responses to evolving market demands. Two such approaches gaining prominence in the realm of payment system modernization are domain-driven design and microservices architecture : Domain-driven design: This approach focuses on a business's core operations to develop scalable and easier-to-manage systems. Domain-driven design ensures that technology serves strategic business goals by aligning system development with business needs. At its core, this approach seeks to break down complex business domains into manageable components, or "domains," each representing a distinct area of business functionality. Microservices architecture: Unlike traditional monolithic architectures, characterized by tightly coupled and interdependent components, a microservices architecture decomposes applications into a collection of loosely coupled services, each of which is responsible for a specific business function or capability. It introduces more flexibility and allows for quicker updates, facilitating agile responses to changing business requirements. Discover how Wells Fargo launched their next-generation card payments by building an operational data store with MongoDB . Modernizing with an operational data layer In the payments modernization process, the significance of an operational data layer (ODL) cannot be overstated. An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. The simplest representation of this pattern looks something like the sample reference architecture below. Figure 1: Operational Data Layer structure An ODL is deployed in front of legacy systems to enable new business initiatives and to meet new requirements that the existing architecture can’t handle—without the difficulty and risk of fully replacing legacy systems. It can reduce the workload on source systems, improve availability, reduce end-user response times, combine data from multiple systems into a single repository, serve as a foundation for re-architecting a monolithic application into a suite of microservices, and more. The ODL becomes a system of innovation, allowing the business to take an iterative approach to digital transformation. Here's why an ODL is considered ideal for payment operations: Unified data management: Payment systems involve handling a vast amount of diverse data, including transaction details, customer information, and regulatory compliance data. An ODL provides a centralized repository for storing and managing this data, eliminating silos and ensuring data integrity. Real-time processing: An ODL enables real-time processing of transactions, allowing businesses to handle high numbers of transactions swiftly and efficiently. This capability is essential for meeting customer expectations for instant payments and facilitating seamless transactions across various channels. Scalability and flexibility: Payment systems must accommodate fluctuating transaction volumes and evolving business needs. An ODL offers scalability and flexibility, allowing businesses to scale their infrastructure as demand grows. Enhanced security: An ODL incorporates robust security features —such as encryption, access controls, and auditing capabilities—to safeguard data integrity and confidentiality. By centralizing security measures within the ODL, businesses can ensure compliance with regulatory requirements and mitigate security risks effectively. Support for payments data monetization: Payment systems generate a wealth of data that can provide valuable insights into customer behavior, transaction trends, and business performance. An ODL facilitates real-time analytics and reporting by providing a unified platform for collecting, storing, and analyzing this data. Transform with MongoDB MongoDB’s fundamental technology principles ensure companies can reap the advantages of microservices and domain-driven design—specifically, our flexible data model and built-in redundancy, automation, and scalability. Indeed, the document model is tailor-made for the intricacies of payment data, ensuring adaptability and scalability as market demands evolve. Here’s how MongoDB helps with domain-driven design and microservice implementation to adopt industry best practices: Ease of use: MongoDB’s document model makes it simple to model or remodel data to fit the needs of payment applications. Documents are a natural way of describing data. They present a single data structure, with related data embedded as sub-documents and arrays, making it simpler and faster for developers to model how data in the application will be mapped to data stored in the database. In addition, MongoDB guarantees the multi-record ACID transactional semantics that developers are familiar with, making it easier to reason about data. Flexibility: MongoDB’s dynamic schema is ideal for handling the requirements of microservices and a domain-driven design. Domain-driven design emphasizes modeling the domain to reflect the business requirements, which may evolve over time. MongoDB's flexible schema allows you to store domain objects as documents without rigid schema constraints, facilitating agile development and evolution of the domain model. Speed: Using MongoDB for an ODL means you can get better performance when accessing data, and write less code to do so. A document is a single place for the database to read and write data for an entity. This locality of data ensures the complete document can be accessed in a single database operation that avoids the need internally to pull data from many different tables and rows. Data access and microservice-based APIs: MongoDB integrates seamlessly with modern technologies and frameworks commonly used in microservices architectures. MongoDB's flexible data model and ability to handle various data types, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. Scalability: Even if an ODL starts at a small scale, you need to be prepared for growth as new source systems are integrated, adding data volume, and new consuming systems are developed, increasing workload. MongoDB provides horizontal scale-out on low-cost, commodity hardware or cloud infrastructure using sharding to meet the needs of an ODL with large data sets and high throughput requirements. High availability: Microservices architectures require high availability to ensure that individual services remain accessible even in the event of failures. MongoDB provides built-in replication and failover capabilities, ensuring data availability and minimal downtime in case of server failures. Payment modernization is not merely a trend but a strategic imperative. By embracing modern payment solutions and leveraging the power of an ODL with MongoDB, organizations can unlock new growth opportunities, enhance operational efficiency, and deliver superior customer experiences. Learn how to build an operational data layer with MongoDB using this Payments Modernization Solution Accelerator . Learn more about how MongoDB is powering industries on our solution library .

May 15, 2024