November 2023 Summaries

MongoDB is expanding its presence in New Zealand to support local businesses in building modern applications and leveraging AI technology. With hundreds of customers already in Aotearoa, including Pathfinder and Rapido, the company is investing in hiring and growth to continue to grow its community in the country. The expansion aims to help organisations overcome challenges such as handling complex data requirements and reducing infrastructure sprawl. MongoDB's developer data platform provides a unified experience for building modern applications, while also enabling companies to adapt to a multicloud future with over 110+ locations worldwide. Local partnerships with cloud hyperscalers and strong adoption of MongoDB Atlas demonstrate the company's commitment to supporting Kiwi businesses in their digital transformation journey.

MongoDB has unveiled the private preview of its Edge Server platform, designed to help retailers overcome challenges related to low or intermittent connectivity issues in their stores. The platform aims to bridge the gap between connected retail stores and the cloud by providing a powerful platform that empowers retailers to achieve their goals. Edge computing is a strategic technology approach that brings computational power closer to where data is generated and processed, allowing operations to continue uninterrupted. With Atlas Edge Server, retailers can streamline their operations, optimize inventory management, and personalize customer experiences while ensuring seamless synchronization of data between edge devices and the cloud. The platform introduces a tiered synchronization mechanism, ensures data is efficiently synced with the cloud, and automatically handles conflicts, eliminating the need for complex conflict-resolution code. By deploying Atlas Edge Server in-store, retailers can transform their stores into dynamic, customer-centric hubs of innovation, leading to enhanced inventory management, seamless operational workflows, customized shopping experiences, and improved customer satisfaction.

The Atlas Vector Search has achieved the highest .NET Promoter Score (NPS) among search tools and is the second most used vector database, only five months after its launch. The Retool State of AI 2023 report found that developers are using vector databases to create applications with AI, particularly in search-sensitive applications such as product catalogs or content discovery. The report also revealed that less than 20% of respondents use vector databases, but the trend is expected to increase due to personalization of models and infrastructure of IA. The MongoDB Atlas Vector Search is a robust, economic, and fast solution that allows developers to store and search embeddings vectors in the same system as their operational database, avoiding additional complexity and costs. The report also provides insights into the state of AI, including the adoption and maturity of AI, the use of open-source models, and the challenges faced by developers when integrating vector databases with other systems.

The State of AI 2023 survey conducted by Retool found that MongoDB Atlas Vector Search has the highest Net Promoter Score (NPS) among developers, indicating its popularity and user satisfaction. The survey also revealed that less than 20% of respondents use vector databases currently, but the trend is increasing towards personalization of models and infrastructure for AI. Developers are adopting Atlas Vector Search due to its ease of integration with other databases in their application's technology stack, as well as its ability to provide faster performance and lower costs compared to traditional vector database solutions. The survey highlights the growing adoption of vector databases, which are becoming increasingly important in applications that require semantic search and contextual understanding. Additionally, the survey provides insights into the perceptions of developers about AI, investment priorities, impact on future job prospects, and the level of maturity of AI adoption among organizations.

Retool hat eine Umfrage durchgeführt, die den Puls von über 1.500 Technikleuten aus verschiedenen Branchen und Fachgebieten erfasst hat, um zu verstehen, wie diese künstliche Intelligenz (KI) nutzen und damit bauen. Die Ergebnisse zeigen, dass MongoDB Atlas Vector Search den höchsten .NET Promoter Score erzielt hat und die zweithäufigste genutzte Vektordatenbank ist, obwohl es nur fünf Monate nach seiner Veröffentlichung ist. Vektordatenbanken sind eine Schlüsselkomponente in einem neuen Architekturmuster namens Retrieval-Augmented Generation (RAG), das die Argumentationsfähigkeiten vorab trainierter Allzweck-LLMs kombiniert und ihnen unternehmensspezifische Echtzeitdaten zuführt. Die Umfrage zeigt, dass weniger als 20 % der Befragten Vektordatenbanken nutzen, aber mit dem wachsenden Trend zur individuellen Anpassung von Modellen und KI-Infrastruktur wird die Akzeptanz garantiert zunehmen. Entwickler übernehmen Atlas Vector Search, da es eine robuste, kostengünstige und unglaublich schnelle Lösung bietet, um KI-gestützte App zu erstellen, die dem Unternehmen auf einzigartige Weise dienen können. Der MEAN Ansatz von MongoDB ermöglicht Entwicklern, schnellere Entwicklungszyklen, eine leistungsstärkere App mit geringerer Latenz und aktuelleren Daten zu erreichen, gepaart mit geringerem Overhead und geringeren Kosten.

Atlas Vector Search obtient le meilleur taux de satisfaction des développeurs dans l'enquête Retool State of AI 2023, démontrant son efficacité et sa popularité parmi les professionnels du domaine. Les bases de données vectorielles ont connu un essor phénoménal en raison de leur capacité à fournir des applications alimentées par l'IA qui sont pertinentes pour les activités de l'entreprise, exactes, fiables et à jour. Le rapport Retool révèle que moins de 20% des développeurs utilisent actuellement des bases de données vectorielles, mais avec la tendance croissante à la personnalisation des modèles et de l'infrastructure d'IA, l'adoption est assurée de croître. MongoDB Atlas Vector Search est présenté comme une solution robuste, rentable et extrêmement rapide qui permet aux développeurs de stocker et de rechercher les encastrements vectoriels natifs dans le même système que celui qu'ils utilisent comme base de données opérationnelle, évitant ainsi les problèmes de complexité et de latence associés à l'intégration avec d'autres bases de données. Le rapport Retool offre également une vision globale du paysage des bases de données vectorielles, de l'adoption de l'IA par les entreprises et des tendances en matière de recherche vectorielle.

India has emerged as a cornerstone in MongoDB's Technical Services growth story, marked by the team's 100% growth in just two years, with Bengaluru and Gurugram being at the forefront of this expansion. The team provides critical technical assistance to customers and partners, and its growth underscores the significance of these cities as hubs for Technical Services. Multiple customer-facing teams support various aspects of MongoDB's products and services, requiring strong critical thinking skills and collaboration across teams and departments. The team is committed to delivering exceptional support through a collaborative approach, with technical excellence centers and training programs fostering continuous learning and development. An exciting development is the introduction of the Swing Shift in India, which leverages engineering talent to enhance support during India and EMEA hours, improving service continuity for APAC and EMEA customers.

The Tencent Games company has partnered with MongoDB to develop a game online service platform called Level Infinite PGOS. This platform is designed to support overseas games and provides a suite of tools for developers, including a database solution that uses MongoDB as its core storage component. The platform aims to simplify the development process by providing a more efficient way to manage player data, match players for multiplayer games, handle in-game transactions, and track game events. With Level Infinite PGOS, Tencent Games can optimize its development experience and improve its operational capabilities, while also expanding its global presence in the gaming industry.

Building AI with MongoDB: Retrieval-Augmented Generation (RAG) Puts Power in Developers’ Hands` presents the benefits of retrieval-augmented generation (RAG), a powerful combination that allows developers to build AI-powered apps grounded in enterprise data and knowledge, without specialized data science teams. This approach enables accurate, up-to-date, and relevant outputs, achieved through pre-trained general-purpose LLMs fed with real-time company-specific data. The use of MongoDB Atlas Vector Search facilitates this process, providing a robust, cost-effective, and blazingly fast solution for developers to build AI-driven semantic search and RAG experiences. Three novel use cases are highlighted: Eni's geological data unlocking, Potion's video personalization at scale, and Kovai's bringing power of Vector Search to enterprise knowledge bases. These examples showcase the growing adoption of RAG in the enterprise landscape, with MongoDB Atlas playing a crucial role in enabling developers to build AI-powered applications efficiently.

MongoDB 分片是一种将数据分布或分割到多台计算机上的方法，允许进行水平扩展和处理大型现代工作负载。通过分片实现横向扩展，可以自动将MongoDB数据库跨多个节点和区域进行扩展，以处理写入密集型工作负载、不断增长的数据大小以及数据存储要求。分片提供了接近无限的扩展，增加读/写吞吐量和存储容量，从而实现更高的可扩展性和性能。MongoDB 提供多种分片策略，包括范围分片、散列分片和区域分片，以满足不同应用程序的需求。在使用 MongoDB 分片时，需要遵循一些最佳实践，例如确保分片键的均匀分布、避免散布-汇集查询以及在适当的时候使用基于散列的分片。

Volvo Group Connected Solutions` leveraged `MongoDB` to improve the efficiency of their database, handling over 65 million daily events for their fleet management platform `Volvo Connect`. The company needed a scalable and flexible database solution to support these transactions, ultimately choosing `MongoDB` due to its ease of use and functionality. With `Atlas`, they were able to easily extend their database without significant maintenance. The implementation allowed them to track the events of over 100 million vehicles daily, providing users with a comprehensive view of each vehicle's status, enabling more efficient planning and management.

THL Digital 是一个全球公司，为 6,000+ 辆租赁车提供技术支持，业务遍及澳大利亚、新西兰、英国和美国。他们的核心产品 Insights 是一款驾驶指导应用程序，可用于改进驾驶员行为、降低驾驶的风险。每一辆租赁车上都安装了车载智能通讯设备，用以记录行驶速度和位置等核心信息。在网络连接顶多只能算参差不齐的澳大利亚内陆等偏远地区，更是如此。这项工作困难重重，但MongoDB的应用程序服务的数据同步服务让一切得以成真。THL Digital 选择先将其构建在 SQLite 上的应用程序重建为 Realm 移动数据库，之后他们发现 Realm 的跨平台功能和快速查询的价值，让他们能够发挥这些价值，从而让重建简单一些。然而，尽管如此，他们依然需要开发从客户端到服务器端的同步。一开始，他们试用了几款以云方式提供的现成解决方案，但最终决定自行构建。他们发现部署基于时间的同步，即每 15 分钟执行一次同步，是一个很好的选择。然而，这个选项也带来了很多工作量，包括构建自己的 API 和 Web 服务，并创建高效的错误处理逻辑。为了解决这个问题，THL Digital了解到MongoDB Atlas Device Sync的时候，它提供了一个解决方案，可以节省大量的时间，并提供可靠的实时数据同步。由客户端端的Realm和后端的MongoDB Atlas提供支持，MongoDB Atlas Device Sync消除了Balea团队编写错误处理逻辑或复杂网络代码的需要。该解决方案改变了开发者的体验，使一个小团队能够构建通常需要数十名开发者才能完成的工作；它通过提供独特的警报系统，使司机在任何地方出行时更加安全，改变了客户体验；并且它还在整个业务中推动了成本节约。自部署MongoDB Atlas设备同步以来，THL Digital 的整体客户满意度和用户评价都得到了显著改善。驾驶员行为的改善也为车队所有者带来了重大的运营成本节约。维护和维修成本已经下降，而企业的保险费用也随着时间的推移有所降低。从开发者的角度来看，Insights团队非常高兴：在使用Realm的移动数据库、MongoDB Device Sync和Atlas之间，他们节省了数月的开发时间。

MongoDB University is a revamped online learning platform that provides developers with access to a wide range of educational resources, including fresh certifications and new content. The platform offers over 1,000 learning assets in multiple languages, including free online courses, hands-on labs, code recaps, quizzes, and more. The platform has received industry recognition, including the prestigious Silver Excellence Award from the Brandon Hall Group, and features three new courses: Data Modeling for MongoDB, Atlas Essentials, and MongoDB for SQL Professionals. Additionally, MongoDB University offers two new certifications - MongoDB Associate Developer and MongoDB Associate Database Administrator - which can be earned through a learning path and come with benefits such as bragging rights and a Credly badge. The platform is designed to provide learners with a frictionless experience and is available online with flexible learning options.

Concured是一家来自蒙特利尔的AI初创企业，致力于协助市场营销团队对标受众，有的放矢地打造网站和销售内容；同时，帮助内容营销团队脱颖而出，加速基于洞察驱动的内容个性化。

"` 使用复合索引来支持高效执行查询，遵循E SR规则以确定字段的顺序，以最大限度地优化性能。 使用覆盖查询（Covered Queries）来直接从索引中返回结果，而无需访问源文档，从而显著提升查询效率。 在考虑低基数字段上的索引时要小心，因为对具有少量唯一值的字段进行查询可能会返回大的结果集。 消除不必要的索引，以减少资源密集型的索引的负载，并使用MongoDB提供的工具来帮助您了解索引使用情况。 使用文本搜索来匹配单词，使用部分索引来减小索引大小和性能开销，利用多键索引来访问数组元素，避免未以左锚定或根据的正则表达式，避免大小写不敏感的正则表达式，并使用WiredTiger存储引擎中提供的索引优化。 使用解释计划和可视化工具，如MongoDB Compass 和 Atlas Data Browser，可以进一步改善您的对索引的理解，并提供智能和自动化的建议。

数据模型让企业付出多少成本？在经济波动导致商业环境不可预测的情况下，组织需要进一步收紧支出，用更少的资源做更多的事。在 IT 支出引人担忧的情况下，许多 IT 决策者不清楚哪个环节导致成本上升。数据建模恰恰又会从多个方面产生重大影响，如数据库的运营成本高低、处理工作负载需要的实例配置、开发和维护应用程序需要的工作量等。标准化数据模型能够高效处理简单的单表查询，但是不常见的复杂模式需要数据库联接表来生成结果，这暴露了 RDBMS 效率低下。与这些查询相关的高时间复杂度意味着需要更多的基础设施支持。关系数据库会将大量此类额外成本隐藏在幕后。当你将向关系数据库发出查询时，实际上并不能够真正查看全部表格上打开的所有连接，或合并的所有对象。一些 NoSQL 数据建模可以消除昂贵的联接表、降低 CPU 占用率、节省计算成本。使用适用于高频率模式的索引能够享受到最佳性能。没有索引，就必须读取集合中的每一个文档，观察并确定哪一个文档匹配查询条件。为了提高开发人员工作效率，需要优化数据模型，并了解如何利用 NoSQL 数据库降低维护额外成本。

国泰航空开发基于 MongoDB 和 Device Sync 的机上移动应用，旨在提高运营效率、工作效率和可持续性能力。面临的挑战包括处理大量纸质材料、数据管理和协作等问题。通过与 MongoDB 携手开发 Flight Folder 应用，国泰航空成功实现了无纸化飞行、提高了协作、精简通信、准确性，并减少对环境的影响。Flight Folder 提高了机组人员的体验，减少了飞行时间和文书量，预计在 5 年内能节省 1 亿港币，并将航空业对气候变化的影响降低到 1.5 万吨/每年。国泰航空致力于通过技术创新来打造更安全、更高效、更环保的飞行，Flight Folder 是其数字化转型的关键项目。

文档数据模型比 RDBMS 更划算，因为它可以更好地映射应用程序使用的对象，并利用应用程序代码中已定义的结构来存储数据，从而避免了物理存储之间的抽象层和增加的查询时间复杂度。这种设计方式在 NoSQL 数据库中受到重视，例如 MongoDB，它通过优化高速查询的数据模型成功地规避了 RDBMS 的复杂性问题，并提供了更好的 OLTP 和 OLAP 性能。随着存储成本的下降和 CPU 效率趋于平稳，NoSQL 解决方案成为了企业降本增效的手首选。

This series of best practices for performance is suitable for anyone, regardless of whether you are a beginner or an experienced MongoDB developer. The series will cover practical advice that can be applied to various scenarios, including using fully managed cloud database services or self-managing MongoDB. The series will start by discussing two key factors: pattern design and the key resources to consider. It will then delve into data modeling, which is crucial for performance optimization. Data modeling involves understanding application query patterns, designing a data model that corresponds to these patterns, and adjusting data models to optimize queries, insert, and update operations. The series will also cover how to apply data modeling best practices in MongoDB, including using JSON documents, arrays, subdocuments, and referencing other collections. It will discuss the key considerations when designing a data model, such as embedding versus referencing, and provide resources for further learning. Finally, it will cover how to review and refine a data model using tools like MongoDB Compass and how to optimize the work set size by adjusting RAM size.

"La recherche vectorielle est un sujet d'actualité qui a connu une explosion de popularité en 2022, principalement en raison du succès de ChatGPT et des LLM (Grands Modèles Linguistiques) développés par OpenAI. Les LLM utilisent des modèles d'intégration pour comprendre des morceaux de texte et effectuer certaines tâches en langage naturel, et la recherche vectorielle permet de trouver les vecteurs stockés qui sont les plus similaires au vecteur de la requête. La recherche vectorielle est un concept mathématique avancé qui a été disponible depuis 2016, mais il n'est devenu particulièrement populaire qu'à la fin de l'année 2022. Les LLM ont révolutionné le secteur en permettant au commun des mortels d'interagir avec les algorithmes NLP, et la recherche vectorielle est un défi qui a été posé par ces encastrements."

WINN.AI is an AI-powered sales assistant that joins virtual meetings to detect and interpret customer questions, providing relevant information for salespeople. The platform uses a multimodal suite of state-of-the-art models for speech recognition, entity extraction, and meeting summarization, relying on MongoDB Atlas as the underlying data layer. WINN.AI aims to reduce administrative tasks for sales teams, allowing them to focus more time on solving customer problems. The platform integrates with popular tools like Zoom, Hubspot, Salesforce, and others, and utilizes AI/ML techniques such as NLP algorithms and GPT models for entity extraction and summarization. MongoDB stores all data in the WINN.AI platform, providing flexibility and scalability. The company sees MongoDB as a stable anchor in the ever-changing AI tech market, allowing them to focus on building great AI-powered products without worrying about database management.

WeLab汇立集团与阿里云携手推动了MongoDB数据库的采用，解决了复杂数据结构、应用场景多元以及系统稳定高效等问题。随着业务发展，MongoDB 5.0强大数据处理功能和先进的金融风控技术帮助WeLab打造了新一代大禹风控系统，提升了写入性能超过50%，并降低了运维成本和学习难度，得到企业客户的一致好评。

Manulife, one of the largest life insurance companies in the world, has ditched its traditional relational database in favor of MongoDB to improve digital transformation and better connect with customers in real-time. The company's existing data infrastructure was becoming a drag on innovation, but MongoDB offered a flexible data model, scalability, enterprise-readiness, and cost-effectiveness. Manulife used MongoDB for various projects, including the development of an app that encourages users to meet fitness goals, a sales assistance app, policy life cycle management, and claims processing. The company is now expanding its use of NoSQL databases with MongoDB identified as the go-to solution, reflecting its recognition as an internal standard and strategic partner for NoSQL development.

"La recherche vectorielle est un sujet d'actualité qui a gagné en popularité ces dernières années, principalement grâce aux LLM et aux technologies de stockage de données avancées. La recherche vectorielle permet à les algorithmes de traiter les données linguistiques dans un ordre de grandeur supérieur à ce qui était possible auparavant. Les LLM utilisent des modèles d'intégration pour comprendre des morceaux de texte et effectuer certaines tâches en langage naturel, comme la réponse à des questions ou la traduction automatique. La popularité des LLM a augmenté avec la version de ChatGPT d'OpenAI en novembre 2022, qui a permis au commun des mortels d'interagir avec les algorithmes NLP d'une manière qui aurait été autrement réservée aux chercheurs et aux scientifiques. Les entreprises de données ont adopté le support pour la recherche vectorielle et d'autres fonctionnalités liées aux LLM et à l'IA, en raison du potentiel illimité que cette technologie offre."

The text discusses the basics of vector search and large language models (LLMs), two key technologies in artificial intelligence (AI). Vector search is a technique used to find similar data points by representing unstructured data such as text, images, and audio as vectors. These vectors are generated using machine learning techniques called embedding models, which learn to capture meaningful relationships between data points. The goal of vector search is to find the most similar data points to a given query, allowing for efficient information retrieval applications like recommendation systems, natural language processing, and computer vision. Large language models (LLMs) take this concept further by using vectors to represent text and enabling machines to understand and generate human-like language. LLMs have revolutionized the field of natural language processing (NLP), making it possible for machines to perform tasks like answering questions, translating languages, and generating text. The popularity of vector search and LLMs can be attributed to the recent breakthroughs in these technologies, particularly the release of ChatGPT by OpenAI, which made these technologies accessible to a wider audience. As a result, many data companies have started incorporating vector search and other AI-related features into their products.

The text discusses the concept of vector search and its relevance in modern AI applications. Vector search is a technique used to find similar data points by representing them as vectors in a high-dimensional space, allowing for efficient querying and retrieval of relevant information. The text explains that vector search has been around since 2016 but gained popularity with the release of OpenAI's ChatGPT model in November 2022, which made large language models (LLMs) accessible to the general public. LLMs use vectorized representations of text data to perform tasks such as language understanding and generation. The text highlights the importance of vector search in enabling efficient querying and retrieval of relevant information from large datasets, particularly in applications like natural language processing and computer vision. It also notes that the growth of popular AI models like ChatGPT has led to increased adoption of vector search technology by companies in the data industry.

The text discusses the concept of vector search and its relation to artificial intelligence (AI) and large language models (LLMs). Vector search is a technique used to find similar data points in a high-dimensional space, where each point represents a piece of information such as text, image, or audio. The process involves converting the input data into vectors and then finding the most similar vectors in a database. This allows for efficient searching and retrieval of relevant data. The text also explains how LLMs work with vector search, using models of incorporation to understand language and generate vectors that represent concepts and their context. The authors argue that the popularity of vector search and LLMs can be attributed to the OpenAI's launch of ChatGPT, which made it easy for the general public to interact with LLMs, leading to a surge in adoption and development of related technologies.

Enterprises are leveraging the integration of MongoDB Atlas and Microsoft Fabric to create intelligent applications that deliver superior customer experiences by harnessing real-time insights and predictions. MongoDB Atlas, known for its flexible data model and capabilities in operational analytics and AI applications, seamlessly integrates with Microsoft Fabric, allowing enterprises to run large-scale AI/ML and varied analytics across their data estates. This integration was highlighted at Microsoft Ignite, where Satya Nadella announced the general availability of Microsoft Fabric, emphasizing the strategic plan for MongoDB Atlas mirroring to facilitate data access in OneLake. The integration supports both batch and real-time data synchronization, enabling enterprises to combine data from multiple sources, such as MongoDB Atlas, and run diverse analytics at scale. Additionally, MongoDB Atlas's collaboration with Manulife exemplifies its transformative impact on industries, where Manulife has adopted MongoDB's NoSQL database to innovate and improve customer interactions, demonstrating the database's scalability, flexibility, and ability to handle both structured and unstructured data effectively. Moreover, MongoDB is undergoing a leadership transition with Dev Ittycheria retiring as CEO, succeeded by Chirantan “CJ” Desai, who brings significant growth-at-scale experience from ServiceNow and Cloudflare, poised to lead MongoDB into its next phase of growth amidst the rise of AI and data-intensive applications.

Samsung Knox, a mobile device security service provided by Samsung Electronics Mobile Experience (MX) business unit, has been utilizing MongoDB since 2019 to provide cloud-based advanced platform capabilities. The partnership enables Samsung Knox to leverage MongoDB's robust database performance and features such as replication sets and Enterprise Advanced tools to improve development efficiency and security. To support the growth of its services, Samsung Knox has implemented various strategies, including schema version management, data modeling, and training programs like MongoDB University, to ensure seamless migration and adoption of new technologies. The company plans to continue its collaboration with MongoDB in the future, focusing on sharing expertise with both new and experienced developers to enhance team understanding and utilization of the platform.

MongoDB has released official GitHub Actions and a dedicated Docker image for the Atlas CLI, making it easier to develop applications with MongoDB Atlas by providing seamless integration with CI/CD platforms like GitHub Actions and allowing users to work natively within their preferred toolchains. The new releases enable users to programmatically manage Atlas resources throughout the development lifecycle using the Atlas CLI commands, offering a mouseless terminal experience and flexibility in managing resources. With these releases, users can leverage native Atlas CLI commands or use predefined workflows to automate tasks such as creating projects, setting up deployments, and retrieving connection strings. The Docker image can be pulled with one command, allowing users to run the Atlas CLI in interactive mode or as a daemon for working with the Atlas CLI.

Dark Mode for Atlas is now available in Public Preview`, announced by MongoDB, introduces a new feature that addresses user requests and improves developer experience with a sleek and sophisticated interface that promotes accessibility, reduces eye strain, and enhances readability. The development team worked diligently to introduce this feature, which includes 300 converted pages, and took note of the number one requested feature in MongoDB's feedback forum. Dark mode is designed to accommodate accessibility standards, reduce battery consumption on OLED or AMOLED screens, and help users with light sensitivities while making sure it was still easy to read. The update benefits not only users but also developers by promoting a seamless dark mode experience across different tools in the developer workflow.

We're celebrating developers because they will build the future, regardless of what it holds. Our campaign recognizes their contributions to shaping computing history and their ongoing impact on technology development. The MongoDB Developer Spotlight Series aims to highlight diverse developer experiences and inspire the next generation by sharing stories from community champions and innovators like Trina Yau and Justin Jenkins.

MongoDB Atlas and Amazon Web Services (AWS) empower organizations to create scalable and intelligent applications by streamlining data infrastructure management, enabling GenAI-powered applications, and providing a unified developer experience that simplifies data management, enhances security, and accelerates developer productivity.

Kathreftis Launches World-Class Identity Access Management with Cymmetri` Kathreftis, an Indian startup, has emerged in 2022 to create a world-class identity access management platform called Cymmetri. To address the growing concern of cybersecurity threats exploiting weaknesses in identity access management, particularly multi-factor authentication systems, Kathreftis' founder and CEO Vikas Jha developed Cymmetri with four key challenges in mind: centralized identity management, 360-degree visibility, scalability and data management, and high availability and scalability. The company opted for a document database like MongoDB to simplify data storage and support its growth strategy. With the help of the MongoDB for Startups program, Kathreftis was able to reduce costs and accelerate its development. Today, Cymmetri is predominantly used by large enterprises in India and the Middle East, aiming to simplify identity access management implementation with ease of use and automation. The company strives to make identity access management solutions accessible to businesses of all sizes while reshaping the world of identity access management.

The Retool State of AI 2023 survey reveals that MongoDB Atlas Vector Search has achieved the highest Net Promoter Score (NPS) among vector databases and is widely used, with over 75% of respondents saying their companies are making efforts to get started with AI. The survey also highlights the growing trend towards customizing models and AI infrastructure, with fewer than 20% of respondents currently using vector databases. Developers are adopting Atlas Vector Search due to its unified approach to storing and searching native vector embeddings in the same system as operational data, providing faster development cycles, higher performance apps, and lower operational overhead. The survey provides insights into the state of AI adoption and maturity, including investment priorities, AI's impact on future job prospects, and the top models in use.

MongoDB has launched MongoDB Atlas for Telecommunications, a program designed to accelerate innovation and maximize data use in telecommunications companies. The program includes expert-led workshops, tailored technology partnerships, and industry-specific knowledge accelerators. This is part of the company's efforts to help CSPs (Communication Service Providers) leverage 5G networks and address issues such as data processing capabilities and security. Atlas for the Edge provides reliable data connectivity across the cloud, data centers, and devices, catering to critical real-time use cases like machine learning and disaster recovery. Additionally, Atlas Stream Processing enables real-time network performance analysis, ensuring quicker responses and enhancing customer experiences. A new feature called Vector Search allows CSPs to build intelligent applications powered by semantic search and generative AI over unstructured data. The program also aims to empower AT&T's address management system and Cisco's workforce to build a better customer experience through MongoDB Atlas, which has helped reduce development time and increase user numbers.

MongoDB is committed to creating a culture of inclusivity with employees from different backgrounds and experiences through its "Embrace the Power of Differences" value. The company has launched an initiative to empower recruiters to better understand how military experience translates into corporate skills, enabling them to have more meaningful conversations with veterans during the hiring process. This effort aims to increase the number of veterans in the workforce by providing training on identifying transferable skills and promoting a supportive environment for those who have served. By leveraging the diversity of perspectives and experiences within its employee resource groups, MongoDB is working to broaden its perspectives, foster innovation, and achieve competitive advantages.

Building AI with MongoDB: Giving Your Apps a Voice` explores how generative AI and MongoDB are being used to unlock value from data of any modality, supercharging communications through the power of voice technology. Voice brings context, depth, and emotion in ways that text, images, and video alone cannot, and its transformative journey spans over a century, from radio and telephone communication to cutting-edge generative AI. The post features three companies harnessing the power of voice with generative AI: Xoltar, Cognigy, and Artificial Nerds, highlighting their applications in patient engagement, conversational AI platform, and custom voice bots without complexity. MongoDB powers these applications through its flexible document model, event-driven data pipelines, and Atlas Search and Vector Search capabilities, enabling the companies to build new classes of user experiences and achieve real behavioral changes.

Transforming Payments with Volante and MongoDB: A Modern Cloud Solution` Volante Technologies, in partnership with MongoDB, has introduced VolPay, a groundbreaking solution that redefined the way banks approach payments technology. This modern, cloud-native solution leverages MongoDB's cutting-edge technology to provide a modular, microservices API-based application. The benefits of this collaboration include modularity, innovative tech stack, cloud native, real-time, easy integration and extension, lower total cost of ownership (TCO), and global connectivity. VolPay is an interoperable ecosystem of business services designed for payments innovation and transformation across the entire payments lifecycle. Built on a microservices architecture, VolPay is inherently real-time and ready to meet the demands of today's fast-paced payments environment. The solution offers scalability, high availability, and deployment flexibility through MongoDB Enterprise Advanced and MongoDB Atlas, delivering performance, scalability, and operational efficiency. This partnership has ushered in a new era of payments technology, enabling banks to stay ahead of the curve and provide their customers with innovative, real-time payment experiences.

삼성전자 스마트홈 서비스는 MongoDB Atlas 기반으로 개발되어 있으며, 이 기술은 인프라 관리 및 비즈니스 로직 개발 간의 균형을 유지하는 것이 중요한 부문 중 하나입니다. Samsung Electronics Digital Appliances (DA) 사업부가 MongoDB Atlas로 마이그레이션을 통해 지속적인 트래픽 증가와 워크로드 처리를 효율적으로 처리할 수 있도록 하면서, 인프라 관리 부담을 줄이고 개발자 생산성과 서비스 품질을 개선했습니다. 또한, 샤딩 전환을 통해 워크로드 운영의 안정性와 균등성을 향상시켰으며, 개발자 생산성과 서비스 품질을 향상시키는 데에도 도움이 되었습니다. Samsung Electronics DA 사업부가 MongoDB Atlas를 기반으로 하는 인프라를 구축함에 따라, 데이터베이스 장애 제로화를 달성하고 고가용성을 확보할 수 있게되었습니다.

MongoDB is launching a new initiative to boost the retail industry's pace of innovation with data-driven applications through its MongoDB Atlas for Retail platform. This platform provides expert-led innovation workshops, tailored technology partnerships, and an industry-specific knowledge incubator to help retailers adopt modern technologies and improve customer experiences. The platform spans advanced retail-industry use cases, including personalized e-commerce, omnichannel inventory management, and AI-optimized supply chain. With the rise of global e-commerce sales expected to grow 56% by 2026, retailers are moving their data to the cloud and unifying siloed architectures by adopting MongoDB Atlas for Retail. The platform enables retailers to use their data for e-commerce growth while delighting customers with application-driven insights through a unified, fully managed, and cost-effective platform.

The MongoDB Partner Ecosystem Catalog is a new tool that enables customers to easily find and discover vetted and exemplary solutions from MongoDB partners, tailored to their specific needs and use cases. The catalog allows customers to explore over 1,000 partners across the globe, with detailed information on each partner's offerings, expertise, and services. This discovery tool facilitates connections between customers and partners, accelerating innovation in industries such as retail, where MongoDB Atlas for Retail is being launched. With features like filtering by use case, industry focus, documentation, and get-started guides, the catalog aims to enhance discoverability, highlight relevant product integrations, and help developers get started quickly with building generative AI applications on MongoDB.

MongoDB has taken over development of the community-driven MongoDB integration for Laravel framework, making it a first-class citizen in their product portfolio. The library delivers a seamless experience by extending Eloquent and adding functionalities like query builder and transactions. Users can expect regular updates with improvements to functionality, bug fixes, and compatibility with recent releases. The move aims to provide an official, robust, and well-maintained library that integrates seamlessly with Laravel. With MongoDB's commitment to the PHP Laravel community, developers can have a modern and elegant experience with MongoDB.

Darwinbox, a rapidly growing HR Software-as-a-Service platform, has significantly expanded its global reach and user base since its inception in 2015, now managing HR processes from recruitment to retirement across 116 countries for over 2.2 million daily users. The platform utilizes MongoDB Atlas to handle the massive data volumes generated by this growth, benefiting from its flexible schema, scalability, and robust security features. MongoDB's document-based database architecture and managed services have been integral in providing near real-time insights and facilitating Darwinbox's expansion. Meanwhile, MongoDB is undergoing a leadership transition, as Dev Ittycheria, the current President and CEO, prepares to retire and be succeeded by Chirantan “CJ” Desai, who brings extensive experience from his roles at ServiceNow and Cloudflare. Dev emphasizes that this leadership change is a strategic move to position MongoDB for its next phase of growth, capitalizing on the rise of AI and data-intensive applications, with CJ seen as the ideal leader to guide MongoDB forward.

Amazon CodeWhisperer, a real-time code suggestion tool, has been integrated into Amazon Q Developer, enhancing its capabilities through collaboration with MongoDB and AWS's Data Science team. This integration tailors code suggestions specifically for MongoDB, improving the development experience across various IDEs such as VS Code, IntelliJ IDEA, JupyterLab, and more, with a setup guide available on the Amazon CodeWhisperer site. Users can benefit from free access without an AWS account, using the AWS Toolkit for installation and accessing features like inline code suggestions and manual request options for efficient coding assistance. Meanwhile, MongoDB has announced a leadership transition, with CEO Dev Ittycheria stepping down in favor of Chirantan “CJ” Desai, formerly of ServiceNow and Cloudflare, who brings a wealth of experience and strategic vision to guide MongoDB's next growth phase. This change is part of a strategic initiative to keep MongoDB competitive and innovative, capitalizing on the rising importance of AI and data-centric applications, while Ittycheria will remain on the Board to ensure a smooth transition.

The partnership between MongoDB Atlas and LangChain, a developer tool for building AI applications, has been announced. This partnership enables developers to build AI-powered chatbots using RAG (Retrieval-Augmented Generation) pattern with ease, thanks to the creation of LangChain Templates. These templates provide a reference architecture that can be easily deployed as a REST API using LangServe. Additionally, MongoDB Atlas and LangChain are working together to release new features for Atlas Vector Search, including a dedicated vector search aggregation stage `$vectorSearch`. This partnership aims to help developers build AI-powered applications more efficiently and effectively, while also providing them with the tools they need to modernize their edge-to-cloud strategy. The emergence of Big Data and AI/ML is pushing enterprises to adopt more sophisticated systems that enable data-driven organizations. Modern IoT solutions can capture and visualize edge data in real-time, resulting in rich insights into operations. By bringing computing to the edge, businesses can deploy a wide variety of applications that help them take action on critical data right then and there, delivering significant efficiency into operations. The partnership between MongoDB Atlas and WeKan aims to help enterprises modernize their edge-to-cloud stack with solutions that are easily implemented and adaptable to their growing data needs. By leveraging Realm, MongoDB Atlas provides an infrastructure needed for IoT solutions across industries. The solution is cloud-agnostic and compatible with services from any of the cloud providers (AWS, GCP, AZURE). It offers performant behavior for heavy client-side insert-only workloads of structured & unstructured data, streams it to Atlas with automatic clean-up, and provides out-of-the-box synchronization allowing seamless and secure transport of data from the device to the cloud using Atlas Device Sync. Finally, MongoDB Atlas Search plays a crucial role in maximizing the value of Operational Data Stores (ODS). It enables users to explore, analyze, and gain valuable insights from their data by incorporating search capabilities into an ODS. This allows organizations to streamline data retrieval, accelerate development cycles, and enable users to interact with data in a more intuitive and efficient manner.

The text discusses the challenges of managing complex data architectures in organizations, particularly those relying on outdated systems. Operational Data Stores (ODS) have emerged as a solution to tackle these complexities by providing a centralized and organized way to store and manage enterprise data. An ODS serves as a secondary data store, holding data replicated from primary transactional systems, allowing organizations to operate on a more unified dataset. The adoption of an ODS typically follows an incremental approach, focusing on extracting data from one system into the Operational Data Store, gradually retiring legacy systems and eliminating intermediate data streams. This approach delivers value to the business by minimizing disruption and providing a complete overhaul. ODS are used to support businesses in three different ways: Data Access Layers, Operational Data Layer, and Developer ODL. Search capabilities, such as those provided by MongoDB Atlas Search, play a crucial role in maximizing the value of an ODS by empowering users to explore, analyze, and gain valuable insights from their data. By leveraging search functionality, organizations can streamline data retrieval, accelerate development cycles, and reduce developer training time, ultimately leading to improved data models and better system performance. The power of search within ODS lies in its ability to simplify data interaction, saving time and enhancing efficiency. Organizations that adopt an incremental approach and leverage search capabilities can unlock the true potential of their data, paving the way for a more productive and data-driven future.