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Mastering MongoDB 7.0

Mastering MongoDB 7.0

By : Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer
5 (12)
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Mastering MongoDB 7.0

Mastering MongoDB 7.0

5 (12)
By: Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer

Overview of this book

Mastering MongoDB 7.0 explores the latest version of MongoDB, an exceptional NoSQL database solution that aligns with the needs of modern web applications. This book starts with an informative overview of MongoDB’s architecture and developer tools, guiding you through the process of connecting to databases seamlessly. This MongoDB book explores advanced queries in detail, including aggregation pipelines and multi-document ACID transactions. It delves into the capabilities of the MongoDB Atlas developer data platform and the latest features, such as Atlas Vector Search, and their role in AI applications, enabling developers to build applications with the scalability and performance that today’s organizations need. It also covers the creation of resilient search functionality using MongoDB Atlas Search. Mastering MongoDB 7.0’s deep coverage of advanced techniques encompasses everything from role-based access control (RBAC) to user management, auditing practices, and encryption across data, network, and storage layers. By the end of this book, you’ll have developed the skills necessary to create efficient, secure, and high-performing applications using MongoDB. You’ll have the confidence to undertake complex queries, integrate robust applications, and ensure data security to overcome modern data challenges.
Table of Contents (20 chapters)
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4
Chapter 4: Connecting to MongoDB

Design patterns and schema design

Schema design in MongoDB is crucial for optimizing performance and scalability. Through the understanding and application of distinct patterns, you can tailor your data model effectively. The following is an overview of various MongoDB design patterns:

  • Bucket pattern: This is a great solution for managing streaming data, such as time-series, real-time analytics, or Internet of Things (IoT) applications. It reduces the overall number of documents in a collection, simplifies data access, and improves index performance.

Consider an IoT application where a sensor sends temperature readings every minute. Instead of creating a new document for every reading, the data can be bucketed together in hourly intervals:

{
    "_id": ObjectId("50bf1fbbbcf86cd799439051"),
    "sensor_id": "S123",
    "start_date": ISODate("2023-10-03T08...

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