Begin your journey toward efficient data manipulation with this robust technical guide and enhance your aggregation skills while building efficient pipelines for a variety of tasks

Key Features

Build effective aggregation pipelines for increased productivity and performance

Solve common data manipulation and analysis problems with the help of practical examples

Learn essential strategies to aggregate time series data in financial datasets and IoT

Book Description

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.

What you will learn

• Develop dynamic aggregation pipelines tailored to changing business requirements
• Master essential techniques to optimize aggregation pipelines for rapid data processing
• Achieve optimal efficiency for applying aggregations to vast datasets with effective sharding strategies
• Eliminate the performance penalties of processing data externally by filtering, grouping, and calculating aggregated values directly within the database
• Use pipelines to help you secure your data access and distribution

Who this book is for

This book is for intermediate-level developers, architects, analysts, engineers, and data scientists who are interested in learning about aggregation capabilities in MongoDB. Working knowledge of MongoDB is needed to get the most out of this book.

Table of Contents

1. MongoDB Aggregations Explained
2. Optimizing Pipelines for Productivity
3. Optimizing Pipelines for Performance
4. Harnessing the Power of Expressions
5. Optimizing Pipelines for Sharded Clusters
6. Foundational Examples: Filtering, Grouping, and Unwinding
7. Joining Data Examples
8. Fixing and Generating Data Examples
9. Trend Analysis Examples
10. Securing Data Examples
11. Time-Series Examples
12. Array Manipulation Examples
13. Full-Text Search Examples

Review

“This book is an indispensable resource for those navigating through the intricacies of aggregation pipelines in MongoDB. It deliberately dives deep, providing room to explore how to master pipeline construction, focusing on optimizing for performance, sharding, and adaptability to frequent requirement changes.

A challenging area of aggregations I've previously struggled with is manipulating arrays, which has always bothered me because dealing with arrays of sub-documents is such an essential aspect of using MongoDB. The book’s chapter on Harnessing the Power of Expressions has finally allowed me to master array handling in my pipelines. Moreover, the book's latter half, enriched with real-world examples, enabled me to solidify this newly acquired knowledge, ensuring I could apply the lessons I’d learned practically.”

Andrew Pearce, Head of Enterprise Architecture at Spicy Mango Technology (formerly employed at Formula 1, TiVo, Cisco, and BBC)

About the Author

Paul Done is one of two Distinguished Solutions Architects at MongoDB Inc., having been at MongoDB for 10 years. He has previously held roles in various software disciplines, including engineering, consulting, and pre-sales, at companies like Oracle, Novell, and BEA Systems. Paul specializes in databases and middleware, focusing on resiliency, scalability, transactions, event processing, and applying evolvable data model approaches. He spent most of the early 2000s building Java EE (J2EE) transactional systems on WebLogic, integrated with relational databases like Oracle RAC and messaging systems like MQ Series.