October 2018 Summaries

[`How to Integrate Azure Functions with MongoDB | MongoDB Blog`] Azure Functions are the core building blocks of Microsoft's serverless technologies, allowing developers to write and execute code without worrying about provisioning and managing virtual machines and operating systems. The tutorial integrates Microsoft Azure Functions with MongoDB Atlas using C# as the programming language. It covers setting up a Visual Studio development environment, creating an Azure function within Visual Studio, writing a transactional query with the .NET MongoDB driver, connecting to MongoDB Atlas, deploying and testing the Azure function, and configuring IP whitelisting for secure connections. The tutorial also showcases the benefits of using Azure Functions and MongoDB Atlas together, including faster application deployment and improved database performance. The integration enables developers to create serverless applications that can scale quickly without worrying about underlying infrastructure management.

Causal Guarantees Are Anything but Casual` discusses the importance of causal consistency in distributed systems and how MongoDB implements a hybrid logical clock to establish a global partial ordering of events across replica sets and sharded clusters. The article highlights the tradeoffs between consistency guarantees, durability, and performance, and provides examples of different combinations of read and write concerns that can be used to achieve specific goals. It emphasizes the importance of considering these tradeoffs in application design and development, and recommends using both `read concern majority` and `write concern majority` to preserve causal guarantees and durability across all failure scenarios.

Every database on the planet is a time-series database`, MongoDB suggests, implying that any database capable of storing integers and values can be used for time-series data. The author argues that specialized time-series databases are unnecessary, citing examples such as CSV files, SQL Server, and Oracle. To make informed decisions about time-series databases, it's essential to consider the credibility of the source and conduct one's own proof-of-concept testing with real-world data. MongoDB is presented as a general-purpose database well-suited for operational workloads, including time-series data, thanks to its flexible document model, transactional consistency, horizontal scaling, native datetime support, and real-time analytics capabilities. The author invites readers to explore MongoDB resources, such as webinars, to learn more about designing effective time-series database applications.

Building Intelligent Apps with MongoDB and Google Cloud - Part 1` discusses the challenges of building data analytics tools that provide rich insights, decision support, and continuous learning capabilities. The authors created a simple e-commerce application, `MongoDB SwagStore`, using React and MongoDB Stitch, which saved them hundreds of lines of code and enabled their app to be ready in days. They then integrated Google Cloud ML and TensorFlow to build a product recommendation engine, which uses collaborative filtering algorithms to suggest personalized products to users. The integration was facilitated by MongoDB Stitch, allowing the authors to quickly and easily deploy the recommendation system into their operational app. By leveraging cloud services and APIs, developers can build intelligent apps that deliver insights and intelligence to customers with minimal development effort.

Creating a Data Enabled API in 10 Minutes with MongoDB Stitch` is an article that guides developers through the process of creating a data-enabled API using MongoDB Stitch, a serverless platform provided by MongoDB. The article explains how to create an HTTP Service and add a Webhook, which enables access to the service from clients. It then shows how to define a function that will be executed when the webhook is contacted with a GET request, allowing developers to retrieve data from a MongoDB database. The article concludes by providing examples of how to use Postman to test the API endpoint and encouraging readers to try it out for themselves.

MongoDB Connector for Apache Spark is now officially certified by Cloudera`, a certification that further validates the compatibility and performance of this connector, allowing users to easily integrate Spark with MongoDB for advanced analytics and machine learning. This certification enables users to run Spark jobs from their managed clusters against both `MongoDB Atlas` and self-managed `MongoDB instances`, reducing operational overhead and simplifying architecture. The combination of `Apache Spark` and `MongoDB` is a potent analytics solution, offering flexibility, secondary indexing, aggregation pipelines, and workload isolation, making it efficient to process data from multiple sources into a single database without impact on other business-critical operations. This certification also highlights the benefits of using `MongoDB Atlas`, a fully-managed cloud database service for MongoDB, which eliminates operational overhead and allows users to focus on delivering actionable insights quickly.