January 2021 Summaries

The journey from surviving with legacy systems to thriving with MongoDB involves four steps: Analysis, Scaffolding, Coding, and Innovation. In the analysis phase, companies assess their existing solution landscape and identify areas for improvement. The scaffolding phase involves building a bridge between the legacy system and the new data platform, which can be achieved through microservices, streaming, or service-based solutions. During the coding phase, developers work with the new domain data model and MongoDB's flexible document model to unlock business requirements and improve application development. Finally, in the innovation phase, companies focus on integrating previously siloed data into real-time machine learning platforms, creating new applications and solutions, and enabling features that were previously unattainable.

MongoDB for VS Code is a popular extension that allows developers to quickly connect to MongoDB and Atlas and work with their data right inside the code editor. The extension has gained over 85,000 installations since its release and has been improved based on user feedback. It now supports features such as choosing database and collection, creating indexes, searching for documents, and updating documents directly in the editor. Additionally, it integrates well with native VS Code functionality and shortcuts. The extension also includes a feature to access document search results, which can be saved to a file or shared with colleagues. Furthermore, it allows developers to edit documents directly in the editor and uses MongoDB playgrounds as a shell replacement, providing access to the Node.js API and NPM modules. Users can try the extension for free by installing it from the marketplace, getting a free Atlas cluster, and connecting to it. The extension is constantly being improved based on user feedback and suggestions.

MongoDB and Accenture have partnered to provide a modernization tool that accelerates the process of migrating legacy and mainframe applications to MongoDB. The tool, called Data Modernizer, analyzes access logs and relational schema from Oracle and DB2 databases, providing critical insights before development activities begin. It combines modeling patterns, best practices, and confidence levels to predict the accuracy of suggested data models. This enables companies to modernize their operations more efficiently, with on average 50% faster data modeling and 80% data model accuracy. The tool is part of MongoDB's Modernization Initiative and Accenture's mainframe offload and cloud modernization efforts.

The Technical Services team at MongoDB is a global operation that provides "on-demand" subject matter expertise to customers, helping them overcome blockers and advise on best practices. The team has grown significantly since its inception over nine years ago and has become known for its collaborative culture, technical excellence, and ability to solve complex problems in high-pressure environments. With a diverse range of skills and experiences among its members, the team is well-positioned to tackle a wide range of customer issues, from scaling applications to resolving critical issues with video chat apps. The team's success is attributed to its commitment to technical excellence, intellectual horsepower, and adaptability, as well as its emphasis on career growth and development opportunities.

Spectro Cloud is an enterprise cloud-native infrastructure company that has raised $7.5 million in seed funding and has 36 team members, founded by three startup veterans who previously worked at Cisco. The company uses MongoDB for its database needs, citing the platform's performance and scalability as key reasons for its choice. Spectro Cloud's cluster profiles automate deployment and maintenance across the enterprise, helping operations prioritize application teams and simplify infrastructure administration. The company prioritizes a culture of autonomy and personal growth, encouraging team members to share their knowledge and expertise through blog posts and external engagement. With MongoDB, Spectro Cloud has been able to scale its database infrastructure without outages, leveraging features like monitoring and best practices for optimal performance.

MongoDB Charts has introduced a new feature that allows users to add interactivity to their embedded analytics with click events, enabling developers to create different functionality depending on where the user clicked. Click events can be used to open up another chart based on a user's click, filter other charts on a dashboard when a series or data point is clicked, or update data in real-time. To get started with click events, developers need to use the @mongodb-js/charts-embed-dom@beta tagged version of the SDK and familiarize themselves with the documentation and available examples, including sandboxes for testing and development. This new feature is currently in beta and will be released for additional chart types, but it has the potential to increase the interactivity of Charts and provide more insights for users and stakeholders.

Imposter Syndrome` is an anxiety that many people experience when speaking in front of an audience, particularly in the tech industry. Sven Peters, who has given hundreds of presentations, admits to feeling like a `fraud` every time he goes on stage. However, he has learned techniques to overcome this feeling and deliver successful talks with confidence. By being better at public speaking, being authentic, growing confidence through feedback, using the audience as a friend, and leveraging imposter syndrome to double down on learning, anyone can overcome their fears and become a more effective speaker. With practice and preparation, individuals can build confidence and deliver engaging presentations that inspire others.

Infosys Data Mesh is an architecture pattern that bridges the gap between analytics and application development teams within large enterprises, providing a decentralized data ownership approach with domain-first design principles. It aims to encapsulate data, its relationships, context, and access functionality into a data product with guaranteed quality, trust, and ease of use for business consumption. The platform requires flexible data structures, an API-driven construct, support for high-performance querying on large-scale data structures, and a shared, scalable infrastructure. MongoDB is the optimal platform for realizing the Infosys Data Mesh architecture due to its flexibility, scalability, and support for high-performance querying. By adopting Data Mesh, businesses can create industry-specific domain data products, enable self-service through trusted data products, speed up insights, and improve engagement leading to superior business results.

The Database Experience team at MongoDB plays a crucial role in making it easier for developers to innovate by building programming language-specific client libraries, known as drivers, that follow idiomatic practices and integrate seamlessly with popular frameworks. The team prioritizes building high-quality drivers that cater to various customer use cases, from Node.js to Swift and Rust. With a focus on innovation, the team constantly develops new drivers and stays up-to-date with the latest trends and technologies. Kaitlin Mahar, Lead Engineer on the DBX team, shares her experience of joining MongoDB as an intern, participating in the rotation program, and eventually becoming a Senior Engineer, where she oversees multiple drivers and engineers. She emphasizes the importance of collaboration, technical writing, empathy towards users, and pragmatic decision-making skills for success on the team. The team collaborates using various tools and follows a collaborative design process to ensure consistency across language teams. With growth opportunities, flexible working arrangements, and a supportive culture, MongoDB provides an attractive environment for individuals to build their careers and engage with the open source community.

The text discusses legacy modernization using MongoDB and Confluent, highlighting the challenges of integrating with outdated systems and the benefits of an Operational Data Layer (ODL) approach. This allows for gradual transition from legacy systems to modernized data platforms while preserving existing applications and workloads. The use of Confluent Platform and MongoDB Atlas enables real-time data synchronization between legacy data sources and modern data platforms, facilitating seamless integration and modernization. By leveraging these tools, businesses can unlock innovation and insight, ultimately driving business growth.

Coursedog, a Y Combinator-backed curriculum success platform for higher education, was founded by Nicholas Diao and Justin Wenig while they were still college students. The founders discovered that universities often lack software to manage class scheduling complexities and decided to build a solution to address this issue. Coursedog uses the MEVN stack, primarily MongoDB, to modernize the way universities propose, schedule, and publish classes to students. With over $4.2M in funding and partnerships with more than 70 universities, Coursedog has grown significantly since its initial prototype was built using simple HTML, CSS, JavaScript, and Node.JS server with a MongoDB database. The platform's founders attribute their success to the flexibility and scalability of MongoDB, which enabled them to build complex features iteratively and navigate scaling challenges. They offer advice for college students aspiring to build their own company, emphasizing the importance of collaboration and starting small to solve local problems.