![]() ![]() For the operational application, accessing data is simple, high performance, and easy to scale with this approach. Most of the time all the data for a record tends to be located in a single document. MongoDB’s document data model is flexible and provides developers many options in terms of modeling their data. Work that would previously have needed to be done on the client side can now be performed by the database – freeing the developer to focus on new features. MongoDB 3.2 aims to extend the options for performing analytics on the live, operational database – ensuring that answers are delivered quickly, and reflect current data. For example, having access to real-time customer sentiment or fleet tracking is of little benefit unless the data can be analyzed and reported in real-time. With the emergence of new data sources such as social media, mobile applications and sensor-equipped “Internet of Things” networks, organizations can extend analytics to deliver real-time insight and discovery into such areas as operational performance, customer satisfaction, and competitor behavior. MongoDB’s plans may change and you should not rely on them for delivery of a specific feature at a specific time. MongoDB’s product plans are for informational purposes only. ![]() Finally, there’s a summary of some of the limitations of the Aggregation Framework and reasons why you might supplement it with a full visualization solution such as Tableau together with MongoDB’s Connector for BI (Business Intelligence) – also new in MongoDB 3.2. After that, we look at how geolocation data can be included as well as what to do when you reach the limit of what can be done using a single pipeline – including adding wrapper code. It then works through examples of building aggregation pipelines – including using the operators added in MongoDB 3.2. We then explain why joins are sometimes useful for MongoDB – in spite of the strengths of the document model – and how developers have been working without them. It starts with an introduction to analyzing data with MongoDB. The material was originally published in a MongoDB blog series. This post looks at the aggregation enhancements being introduced in MongoDB 3.2 – most notably $lookup which implements left-outer equi-joins in the MongoDB Aggregation Framework.
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