Book Image

Mastering MongoDB 7.0 - Fourth Edition

By : Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer
5 (2)
Book Image

Mastering MongoDB 7.0 - Fourth Edition

5 (2)
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)
4
Chapter 4: Connecting to MongoDB

Atlas Data Lake

MongoDB Atlas Data Lake is an analytics-optimized object storage service designed for extracted data. It provides an analytic storage service optimized for both flat and nested data, ensuring low-latency query performance.

Essentially, the data lake capability enables you to run a single query that will route to either object storage or a database. This allows for more advantageous data storage use cases, including the ability to handle data stored in various formats outside of JSON and BSON, such as CSV, TSV, Parquet files, and the like.

Atlas Data Lake requires a paid tier cluster usage with backup enabled. It supports collection snapshots from Atlas clusters as a data source for extracted data. The service automatically ingests data from the snapshots, partitions it, and stores it in an analytics-optimized format.

Data storage and optimization

Atlas Data Lake stores data in Parquet files, an analytic-oriented format based on open source standards, with...