June 2020 Summaries

In this blog, the author demonstrates how to set up and configure the MongoDB Connector for Apache Kafka using Azure Event Hubs as an alternative to running a Kafka cluster. The author creates an Azure Event Hub and uses Docker to spin up a Kafka Connect container with the MongoDB Connector for Apache Kafka. They then define the source and sink connectors to push data from a MongoDB collection into the Azure Event Hub and back to another MongoDB collection, respectively. The author uses Python to insert data into the source collection and read data from the sink collection, demonstrating a round-trip of data sent through Azure Event Hubs using the MongoDB Connector for Apache Kafka.

MongoDB's Summer Internship Program in Sydney, Australia is a program that provides young talent with the opportunity to work on real projects and learn from experienced engineers. The interns worked on various teams, including Charts, Storage Engines, and Technical Services Engineering, and were able to contribute to product development and solve customer issues. The interview process was described as enthusiastic and culture-focused, with an emphasis on core values that align with the company's mission. The interns learned about new technologies, such as Vega and D3.js, and gained insight into a professional engineering team's workflow. They also appreciated the supportive work environment, regular one-on-one meetings, and opportunities to engage in office activities. Overall, the internship experience was rewarding, motivating, and helped shape their career goals as software engineers.

The tutorial "Exercise Tracker: MERN" provides a comprehensive guide to building a single-page application using the MERN stack, which includes MongoDB, Express, React, and Node.js. It walks through setting up a cloud-hosted MongoDB using MongoDB Atlas and Google Cloud Platform, though the instructions are adaptable to other services like AWS or Azure. The document explains the transition from relational databases to MongoDB's document model and outlines the creation of a back-end server using Node.js and Express, with a front-end developed in React. The tutorial also covers setting up a RESTful API with endpoints for CRUD operations on exercises and users, and integrates the front and back ends using Axios for HTTP requests. Additionally, it guides through the creation of React components and the use of libraries like Bootstrap and DatePicker to enhance the user interface, culminating in a fully functional exercise tracker application.

There’s a New Compass and It Comes With a Shell! | MongoDB Blog` Compass 1.22 Beta introduces an integrated MongoDB Shell, allowing users to run database commands and scripts directly from the UI. This new feature enables users to quickly access the shell, type commands, and see results without having to connect to their server or cluster again. The integrated shell includes syntax highlighting and autocomplete features to enhance readability and save time. It also supports scripting for bulk operations and executing MongoDB commands in JavaScript. The next step is a deeper integration between the shell and the rest of the UI, with user feedback valuable for shaping this feature.

DataGrip` is a professional database IDE that supports MongoDB and Atlas, allowing users to easily explore and analyze their data. The tool includes a database console built on top of the `MongoDB Shell`, providing a 100% consistent experience with the terminal or other developer tools. With `DataGrip`, users can connect to their MongoDB and Atlas clusters, run queries and commands, and utilize features like search capabilities and export tools. The integration is particularly useful for users who already work with other JetBrains commercial products, such as IntelliJ, PhpStorm, or PyCharm, by allowing them to access MongoDB support through a single plugin. By combining the power of `DataGrip` with the scalability and flexibility of `MongoDB Atlas`, developers can efficiently manage and analyze their data in a variety of use cases, including those involving large datasets stored in `Atlas Data Lake`.

Segware, a company that provides security management platforms for hundreds of monitoring companies worldwide, was facing challenges in managing its growing data needs, particularly with regards to storing and querying large volumes of event data. To address these challenges, the company turned to MongoDB's Atlas Data Federation solution, which allows it to federate its data across multiple storage systems, including hot and cold data. With Atlas Data Federation, Segware can efficiently access both frequently accessed "hot" data and less frequently accessed "cold" data, reducing storage costs and improving query performance. The solution also enables Segware to easily query data from different storage systems using a familiar MongoDB Query Language (MQL), without having to rewrite queries. As a result, Segware has seen improved cost-effectiveness and increased scalability, allowing it to manage its growing data volumes in a healthy way.

### Cloud Native Enterprise Data Management with Tardigrade for MongoDB` Cloud Native Enterprise Data Management with Tardigrade for MongoDB is a collaboration between MongoDB and Storj Labs to demonstrate managing data backups and migrations across multiple computing and storage platforms with enterprise security and SLAs. The solution combines tech from both companies to support variable requirements for security, high-availability, and disaster recovery scenarios. Tardigrade provides end-to-end encryption by default, cross-geography redundancy, and lower costs compared to centralized cloud storage services. The solution is implemented using Kubernetes and MongoDB Ops Manager, with a demonstration setup available online.

In the latest part of the blog post on using Keyhole to analyze MongoDB clusters, Ken Chen introduces the Full Time Diagnostic Data Capture (FTDC) Assessment panel, which provides visualized presentation of FTDC data and scoring features to identify potential problems quickly. The panel uses scoring algorithms to evaluate metrics, with scores ranging from 0 to 100, where higher scores indicate better performance. Watermarks are used to set low and high usage thresholds for certain metrics, allowing for more accurate scoring. Keyhole also provides examples of how to calculate scores for metrics with known behavior or derived values. The blog post highlights several bottleneck patterns, including "Lost in Space," "Dream Weaver," "Vikings Attack," and "New York, New York," which illustrate common issues that can arise when using MongoDB, such as improper indexes, inadequate data access use cases, and excessive resource utilization. Chen emphasizes the importance of proper schema design and resource provisioning to avoid these issues.

The MongoDB Innovation Awards recognize outstanding projects and individuals who leverage data to build innovative applications and expand technology boundaries with MongoDB. The 2020 winners include Google Cloud for their partnership with MongoDB, dacadoo for its Life & Health insurance platform technology solutions, Spathe Systems for its innovative Cyber Security and IT network infrastructure solutions, Nationwide Building Society for speeding up access to transaction data, 7-Eleven for deploying a custom mobile device to manage inventory, Toyota Connected North America for leveraging big data from vehicles with MongoDB, Pizza Hut for creating a central platform for collecting customer data, Zinc for selecting MongoDB as the data component for its core API, and Michael Schreier, Certified Professional of the Year, who has advanced his career and given back to the community through MongoDB expertise. These winners demonstrate creative use of data and technology to build compelling applications and expand industry limits with MongoDB.

MongoDB partnered with WildAid, a conservation group, to build an open-source application using MongoDB's unified data foundation. The resulting application, O-FISH, is designed to help fisheries enforcement officers access existing information about vessels before boarding them, and digitize and synchronize individual records for officer use. The application features a mobile app that allows officers to write boarding reports directly on a mobile device, as well as a Realm serverless web application that visualizes and extracts information from the data stored in MongoDB Atlas. The application uses MongoDB's unified data foundation, which saves time and money for WildAid while also providing a secure and efficient way to manage data. The O-FISH project is open-source and allows users to build and contribute to the code, with the goal of protecting our oceans.

MongoDB Atlas has introduced "Online Archive", a new paradigm for data tiering that allows users to seamlessly archive data across clusters and cloud object stores, enabling cost-effective storage and querying capabilities. With Online Archive, users can define archiving rules based on specific fields, choose commonly queried fields, and confirm archiving initiation. The feature is suitable for various use cases such as insert-only workloads, historical log keeping, time-series datasets, and storing data that would have been deleted using TTL indexes. After setting up an online archive, users can manage it, query live and archived data with a single connection string, and right-size their Atlas clusters by moving "cold" data to cheaper storage tiers.

Seamlessly integrating data between Confluent Cloud and MongoDB Atlas is now easier than ever with the introduction of the MongoDB for Apache Kafka connector in Confluent Cloud. This connector enables organizations to quickly build scalable streaming solutions that leverage best-of-breed technologies without needing to manage core infrastructure. With the MongoDB Atlas Source, organizations can push data into a Kafka topic, and the MongoDB Atlas Sink can selectively pull data from a Kafka topic into a MongoDB Atlas cluster for advanced querying and reporting purposes.

The new MongoDB Shell, a standalone product developed separately from the MongoDB Server, is being introduced with a modern user experience that prioritizes syntax highlighting, intelligent autocomplete, contextual help, and error messages. The shell is designed to be extensible, embeddable, and open-source under the Apache 2 license. Its development is actively ongoing, with a focus on user feedback during the beta period.

MongoDB is looking for drive, coachability, and intelligence in candidates for their Corporate Sales role. The company values diversity and looks for well-rounded individuals who have gained experience outside of corporate sales. They also prioritize career development, with a comprehensive sales enablement training program and opportunities for growth and promotion. The company's culture is driven by its Account Executives and emphasizes employee wellbeing, personal growth, professional growth, leadership, product-market opportunity, and fun. With over 50% year-over-year growth, MongoDB offers a challenging job that can take your career to the next level.