August 2023 Summaries

The challenges of processing streaming data, as seen through the example of a fictitious bank's credit card transactions, arise from the need to manage large volumes of event data in real-time while ensuring accuracy and security. To address these challenges, the bank adopts an event streaming platform like Apache Kafka to queue event data, but also realizes that querying the transactional event data as it flows into the database could help identify suspicious transactions. From a developer's perspective, building applications with streaming data requires consideration of serialization formats, schemas, late-arriving data, operational complexity, and security. Stream processing can help address these challenges and enable real-time use cases such as fraud detection, hyper-personalization, and predictive maintenance.

MongoDB.local Taipei 2023` is an annual conference hosted by `MongoDB` in Taiwan, featuring topics such as technology trends, customer cases, and product demonstrations. The event attracts industry experts, customers, and partners to learn about the latest technologies and best practices. This year's conference will take place on September 7th, 2023, and will cover four main areas: main stage talks, technical forums, MongoDB product showcases, and interactive sharing sessions. Various companies, including `WOO Network`, `Inovaare`, `Ventecon`, and `Lavender`, have been invited to share their experiences with `MongoDB` and its products. The conference aims to provide a platform for attendees to learn from each other and explore new technologies. Additionally, MongoDB has partnered with Google Cloud to offer integrations and solutions that help customers build innovative applications. The partnership has led to notable achievements, including the fourth consecutive `Google Cloud Partner of the Year Award` for MongoDB. Furthermore, MongoDB has launched an AI Innovators Program to support startups in developing their AI capabilities using its products. The program offers resources, credits, and partnerships to help companies like Inovaare, Ventecon, and Lavender build innovative solutions with AI.

MongoDB and Google Cloud have partnered since 2018 to enable customers to build innovative applications through deep integrations, and the two companies have been honored as Google Cloud's Technology Partner of the Year for the fourth consecutive year. The partnership has driven innovation on behalf of customers, with a surge in customers purchasing MongoDB on the Google Cloud Marketplace. The collaboration expanded into domains such as generative AI, with Google & MongoDB focusing on helping startups especially those focused on Generative AI. To support this joint innovation, MongoDB announced the AI Innovators Program, which provides additional resources and opportunities for early-stage companies. The partnership also includes seamless integration with BigQuery and Google Cloud Application Integration, making it easier for customers to choose MongoDB Atlas in the BigQuery Partner Center and simplify complex integrations. By working together, MongoDB and Google Cloud have made it easier for companies to achieve their goals and customers are taking notice of the benefits of this collaboration.

In the realm of artificial intelligence, companies like Inovaare are leveraging AI-powered compliance solutions to transform complex regulatory processes in the healthcare industry. Ventecon's AI Copilot supports product managers in crafting market-leading products and solutions, while Lavender is an AI sales email coach that assists users in writing better emails faster. These innovative applications demonstrate how MongoDB can be used to build AI-driven solutions for diverse industries, with the goal of improving regulatory compliance and driving business success. By partnering with MongoDB's AI Innovators Program, these companies are able to innovate at higher velocity and lower cost, while also exploring new use cases such as copilots for sales teams and product managers.

MongoDB is hosting a series of events called "MongoDB.local" in cities around the world, including Hong Kong, where a one-day conference will be held on September 5, featuring new product announcements, technical sessions, and customer success stories. The event aims to cater to the diverse needs of its developer community, covering topics such as data modelling, schema design, app-driven analytics, and mobile data sync. Attendees can engage with live demos at the MongoDB booth, network throughout the day, and hear breaking news on product updates and recent solutions for de-risking application migration and modernization. The keynote will feature announcements from MongoDB's executive leadership team regarding new features and updates to the database.

Imagoworks, a digital dental business innovation company using AI-based digital dentistry solutions, has automated the manual process of creating dental prosthetics with 3D scanners and cloud-based AI technology, reducing costs and time for dental professionals. The company uses MongoDB Atlas to provide a subscription-based model solution that enables seamless collaboration between dentists and dental clinics across borders. By utilizing MongoDB's document-based NoSQL database, Imagoworks has optimized its service development, data modeling flexibility, and global scalability, providing fast turnaround times and efficient data management. Additionally, the company leverages MongoDB's global cluster feature to ensure region-specific data access and disaster recovery capabilities, while using Azure as the underlying infrastructure for deployment in 11 regions worldwide, enabling efficient data management and secure data storage.

Boosting Performance and Insights with MongoDB Atlas and New Relic` is a collaboration between `MongoDB Atlas`, the leading database-as-a-service platform, and `New Relic`, the renowned monitoring and observability solution. This integration enables users to monitor, analyze, and optimize their MongoDB deployments with efficiency and ease, providing real-time visibility into performance metrics such as latency, throughput, and error rates. With intelligent alerting and notifications, powerful dashboarding and reporting capabilities, and comprehensive insights into database performance, teams can proactively address issues before they impact end-users, ensuring scalability and delivering exceptional user experiences.

Powerful Generative AI Innovation Accelerates Discovery of New Molecules is a collaboration between MongoDB and Google Cloud that leverages their strategic partnership to accelerate the discovery of new molecules in various industries. The integration uses generative AI, specifically Google's Foundation Models and MongoDB Atlas Vector Search, to make the process more environmentally friendly. This innovation empowers businesses to create applications at scale with unprecedented data richness, enabling personalized end-user experiences. Developers can now build intelligent applications powered by generative AI over any type of data, accelerating industries such as chemistry. The collaboration enables companies like Anupam Rasayan to use this technology for custom synthesis and manufacturing of specialty chemicals, aiming to reduce costs and push R&D to new heights.

The MongoDB Node.js driver has improved its support for the SCRAM-SHA-256 authentication mechanism, which is now the default when available. This change addresses an edge case where the external library saslprep was not loaded correctly, potentially causing issues with connection to a MongoDB cluster. The issue can be resolved by upgrading to a newer version of the driver or appending the authMechanism option to the connection string with a value of SCRAM-SHA-1.

In the competitive retail landscape, having a single view of inventory is crucial for achieving accurate demand forecasting, improving customer experiences, and boosting workforce efficiency. Without this centralized hub, retailers struggle to compete with new market disruptors offering customers omnichannel experiences, leading to significant challenges in managing stock levels, tracking inventory, and making data-driven decisions. Leveraging real-time data strategies and technologies like MongoDB's document data model can help retailers overcome these challenges by enabling them to track inventory in real-time, make informed decisions about stock allocation, and automate processes through event-driven architectures. By doing so, retailers can move to distributed supply chains, improve workforce efficiency, and gain a competitive edge in the retail industry.

MongoDB 7.0 introduces several features to improve developer productivity and performance, including compound wildcard indexes, bitwise operators in the aggregation pipeline, percentile operators, and role-based data access through the $$USER_ROLES variable. These new features enable developers to write more efficient and intuitive queries, reducing optimization time and enhancing the overall development experience. The introduction of these features represents a significant upgrade in MongoDB's ability to support complex applications and large-scale data analysis.

MongoDB 7.0 is a cutting-edge data platform technology that empowers automotive developers to build, iterate, and scale their applications faster and smarter than ever before. With its new features and capabilities, MongoDB enables developers to process and analyze large amounts of sensor data in real-time, unlock the potential of connected car applications, and create smarter, data-driven applications that deliver more relevant and accurate insights. The platform also addresses the challenges of legacy relational databases by assisting with migration to a NoSQL database, streamlining development, and optimizing performance. By embracing MongoDB's flexible document data model, developers can build innovative, scalable, and secure applications that drive the future of automotive innovation.

The rapid development of AI and machine learning (AI/ML) poses new challenges for the underlying data support, requiring databases to be more flexible and scalable. The author believes that the upcoming MongoDB User Day on August 29th and 31st in Beijing and Shenzhen will provide answers to this challenge, with experts sharing insights on how to build AI-driven applications and modernize data models. MongoDB 7.0 is expected to be released, bringing a unified platform with rich tools for developers to improve their experience and modernize application loads. The event will also feature industry partners sharing their experiences with MongoDB products, including Alibaba Cloud, Tencent Cloud, and Google Cloud.

The MongoDB team has released several updates to Atlas Charts in the summer 2023 season, including expanded chart customization options through the Charts Embedding SDK, an out-of-the-box billing dashboard that allows users to easily monitor their Atlas billing data, support for candlestick charts, and query execution improvements with extended timeout durations of up to 10 minutes.

Serverless architecture` offers a cost-effective way for developers to focus on building applications without worrying about infrastructure management. By abstracting away server management and scaling concerns, serverless promises increased development efficiency and potential cost savings. However, before adopting this paradigm shift, it's crucial to understand the tradeoffs and costs associated with serverless architecture. The primary cost factors for serverless solutions typically include compute resources, memory or storage allocation, and data transfer. Serverless can be cost-effective in certain scenarios, such as low and variable workloads, short-lived tasks, and high burst traffic, but may not be the optimal choice for predictable workloads with steady demand. To optimize costs in serverless architecture, developers must ensure proper optimizations are in place, considering best practices for implementation. Ultimately, understanding the cost model, comparing it with provisioned infrastructure, and implementing cost optimization strategies can help developers make an informed decision that aligns with their development goals and budget.

MongoDB recently held its local Auckland event, MongoDB.local Auckland, which highlighted the growing need for strong cloud strategies among data-driven businesses in New Zealand and around the world. Key announcements included Microsoft's commitment to build a new cloud region in New Zealand, Amazon's and Google's plans to do the same, and MongoDB's intention to fully support each of these regions on its Atlas platform, bringing a local presence to its leading cloud database platform. The event showcased MongoDB's capabilities supporting large enterprises, public sector customers, and startups, as well as its commitment to security, resilience, and compliance. Customer success stories were shared, including Canva, which supports 110 million monthly active users across more than 15 billion designs, highlighting the benefits of using MongoDB Atlas for scaling businesses globally. The event also featured presentations from Microsoft, AWS, GCP, and a MongoDB partner on their plans to invest in the New Zealand tech ecosystem, demonstrating global platforms' support for local innovation.

MongoDB has announced the first qualifiers for its AI Innovators Program, which provides access to expert technical advice, free MongoDB Atlas credits, co-marketing opportunities, and introductions to potential venture investors. The program has received applications from startups addressing various industries and use cases, including enterprise chatbots, personal wellbeing assistants, and news classifiers. Three startups that have qualified for the program are Cloneable, ExTrac, and CetoAI, which are using MongoDB Atlas to build innovative AI-powered solutions, such as deploying AI anywhere with Cloneable, predicting risks to public safety with ExTrac, and cutting maritime emissions and risk with CetoAI. The program is designed to help startups "build the next big thing" in AI by providing access to technical expertise, resources, and support.

The Talent Sourcing team at MongoDB is a centralized organization that operates globally, with specialized sourcing groups by business units. The team's strategy focuses on identifying and engaging relevant talent pools, reducing the time to and cost of hire. This team contributes nearly 25% of global hiring at MongoDB and attributes its success to each sourcer's industry knowledge, effective use of sourcing channels, and collaboration with recruiters and hiring managers. The team members share their experiences, highlighting the organization's unique work culture, collaborative environment, and opportunities for growth and learning. To succeed on the team, candidates need strong research skills, technical knowledge, and a drive to find great candidates, especially in niche roles and geographies.

MongoDB 7.0 has introduced significant improvements to performance, including enhanced handling of time series data and improved query performance. The release also streamlines developer experience with new features such as compound wildcard indexes and user role variables in aggregation pipelines. Additionally, MongoDB 7.0 strengthens security capabilities with Queryable Encryption, allowing developers to encrypt sensitive data and run equality queries on fully randomized encrypted data. Furthermore, the update provides smoother migrations with cluster-to-cluster sync enhancements, enabling more efficient data migration and reduced developer effort. Overall, MongoDB 7.0 offers a comprehensive suite of features designed to boost productivity and enhance security for organizations building modern distributed applications.

Queryable Encryption with Equality Query Type Support is a new technology announced by MongoDB that allows developers to query encrypted sensitive data in a simple and intuitive way, while preserving the ability to run equality queries on that encrypted data. This technology offers several key benefits, including the ability to meet strict data privacy requirements, faster application development cycle, reduced operational risk as sensitive workloads are protected on the cloud, strong technical controls for critical data privacy use cases, and uses NIST standards-based primitives to ensure confidentiality and integrity of data.

MongoDB has completed the Infosec Registered Assessors Program (IRAP) Assessment for its MongoDB Atlas platform, demonstrating adherence to the highest security standards and providing Australian government agencies with a trusted data platform for sensitive workloads. The completion of this assessment validates MongoDB's commitment to security and empowers developer teams worldwide to build next-generation applications using MongoDB Atlas. With advanced security features, data sovereignty capabilities, multi-cloud support, scalability, performance, operational efficiency, and innovative solutions, MongoDB Atlas enables Australian government agencies to modernize their applications, enhance citizen experiences, and secure sensitive data.

In their quest to enhance Jira's automation capabilities, Emet Ozar and Luke Bonanomi explored the use of ScriptRunner, a powerful plugin that utilizes the Groovy programming language, to address specific challenges faced by their documentation team. The team struggled with prioritizing work on documentation pages, as each ticket's value seemed equal, necessitating a system to rank URL pages based on a predefined list and trigger specific actions. By leveraging Jira's native automation features and ScriptRunner, they crafted a custom automation solution that updated ticket priorities, added labels, and provided clear rank visibility for URLs. This automation significantly improved the team's ability to prioritize high-impact documentation pages, embodying MongoDB's values of collaboration and impactful work. This case exemplifies the importance of tailored automation solutions in enhancing productivity, accuracy, and efficiency for program managers, systems administrators, and engineering teams, showcasing how specific automation use cases can lead to broader improvements in workflow management.

Best of Breed: commercetools on Building Composable Commerce on MongoDB` discusses the partnership between commercetools and MongoDB in building a modern, data-centric composable commerce platform. This platform is used by leading household brands to solve consumer demands in an increasingly AI-driven ecommerce landscape. According to Michael Scholz, VP of Product and Customer Marketing at commercetools, MongoDB powers their Composable Commerce platform with its high availability architecture, scalability, and cloud-native SaaS capabilities. The partnership enables commercetools to focus on value-added features for their product and company, reducing operational effort in managing data. MongoDB's flexible document model also helps commercetools build customized AI experiences for customers, such as AI-powered analytics, supply chain, and personalization. Through this partnership, commercetools has achieved huge success with its customers, including inspiring numbers like +35% average order value, +2X Sales Order Increase, and +40% Increase in Cross-Selling. The partnership is considered a perfect match for modern data-centric commerce, enabling innovation at speed and delivering outstanding shopping experiences while innovating fast to be ready for what's next.

The MongoDB Node.js driver version 6.0.0 is set to release in August 2023 and brings changes to the findOneAnd* APIs, which were previously returning a ModifyResult instead of the requested document. A new FindOneAnd*Options property called includeResultMetadata has been introduced, allowing users to control whether the returned result includes metadata or not. By setting this option to false, users can now access the requested document as expected. The Node.js driver version 5.7.0+ already supports this new behavior, and it will become the default in version 6.0.0, requiring users to set includeResultMetadata: true if they need to access the ModifyResult directly.

The most common developer skill gaps impacting businesses are aggregation, indexing and optimization, and security. These skills are critical for modern data platforms like MongoDB, but many developers lack the necessary knowledge to create efficient queries, manage database performance, and ensure data security. To address these gaps, organizations can use tools like MongoDB Skill Scanner to identify areas of improvement and provide targeted training programs, such as Instructor-Led Training, to equip developers with the skills they need to succeed. By closing these skill gaps, businesses can improve productivity, reduce errors and inefficiencies, and enhance their overall user experience.

The Realm C++ Preview is a new software development kit (SDK) announced by MongoDB, which provides developers with a comprehensive solution for building distributed applications that securely use data at the edge. The SDK offers capabilities such as network handling and conflict resolution, real-time use cases, and an object-oriented database without requiring a separate mapping layer or ORM. It also introduces an improved syntax for defining object models, automatic schema discovery, and support for Windows operating systems. This release is part of MongoDB's mission to provide developers with seamless technologies that make the development process easier.

The Aggregation Stage Wizard in MongoDB Compass 1.38 allows users to create aggregation stages based on their use case by clicking a wizard icon and dragging a corresponding use case to the pipeline, converting it into a valid aggregation stage written in MongoDB's Query API syntax through dropdowns and text boxes. This new feature addresses the common challenge of getting started with aggregation development, making it easier for users to craft aggregation stages without prior experience with MongoDB Query API syntax. The Aggregation Stage Wizard is part of Compass' powerful aggregation experience and provides an interactive way for users to learn how to develop aggregation stages, eventually becoming proficient in working directly in Query API syntax.

Exploring the various chart types available in MongoDB Atlas Charts, including Column and Bar Charts, Line and Area Charts, Combo Charts, Grid Charts, Circular Charts, Text Charts, Geospatial Charts, and Heatmaps, each with its unique strengths and purposes to help discover insights and communicate effectively through data visualization. These charts are designed to condense vast data into a coherent format, making information more consumable for a wide range of data consumers. By choosing the right chart type for each potential insight, users can create diverse and all-encompassing dashboards. The guide highlights common and less common chart types, including data tables, number charts, grouped column and bar charts, donut charts, line and area charts, stacked column charts, geospatial charts, and heatmaps, to enrich the user's data visualization toolkit.

The Great Data Divide` highlights the limitations of traditional organizational data management practices, where transactional and analytical data are handled by separate teams. This divide can hinder AI goals due to the increasing demand for real-time and historical data from machine learning models. The article proposes a new approach, treating data as a product with characteristics like state, age, and context, and unifying teams through Domain-Driven Design to manage data more efficiently. By erasing the line between transactional and analytics teams, organizations can increase their overall data processing proficiency and unlock the full potential of AI models.

In a rapidly evolving manufacturing landscape, digital twins of factory processes have emerged as a game-changing technology. Digital twins serve as virtual replicas of physical manufacturing processes, allowing organizations to simulate and analyze their operations in a virtual environment. Real-time data, especially inventory information, plays a crucial role in these virtual factories, providing up-to-the-minute insights for accurate simulations and dynamic adjustments. To implement a real-time computer vision inventory inference solution with MongoDB Atlas, the first task is transmitting data from the physical factory to MongoDB Atlas using the MQTT protocol, where images are sent as base64 encoded strings through AWS IoT Core. The model trained on these images uses object detection algorithms to predict the presence of specific pieces in the stock, and this prediction is stored in a collection for real-time inventory management. MongoDB Realm is then used to connect the digital twin with MongoDB Atlas, enabling seamless bidirectional communication and automatic conflict resolution. This setup enables transformative real-time inventory management for manufacturing companies, providing up-to-the-minute insights for accurate simulations and dynamic adjustments.