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Practical MongoDB Aggregations

Practical MongoDB Aggregations

By : Paul Done
5 (17)
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Practical MongoDB Aggregations

Practical MongoDB Aggregations

5 (17)
By: Paul Done

Overview of this book

Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you’ll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.
Table of Contents (20 chapters)
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2
Part 1: Guiding Tips and Principles
7
Part 2: Aggregations by Example
16
Afterword

Converting incomplete date strings

Sometimes, you will encounter datasets with dates stored as strings and lacking critical details such as the century and time zone. As with the prior example, this poses challenges for database users. The next example will demonstrate how to amend these dates and add the missing information.

Scenario

An application is ingesting payment documents into a MongoDB collection where each document's payment date field contains a string looking vaguely like a date-time, such as "01-JAN-20 01.01.01.123000000". When aggregating the payments, you want to convert each payment date into a valid BSON (BSON is a binary encoding for JSON data types, making it easier and more performant for MongoDB to process and enabling support for more data types than the JSON standard) date type. However, the payment date fields contain only some of the information required to determine the exact date-time accurately. Therefore, you cannot use the date operator...

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