Book Image

Mastering MongoDB 6.x - Third Edition

By : Alex Giamas
Book Image

Mastering MongoDB 6.x - Third Edition

By: Alex Giamas

Overview of this book

MongoDB is a leading non-relational database. This book covers all the major features of MongoDB including the latest version 6. MongoDB 6.x adds many new features and expands on existing ones such as aggregation, indexing, replication, sharding and MongoDB Atlas tools. Some of the MongoDB Atlas tools that you will master include Atlas dedicated clusters and Serverless, Atlas Search, Charts, Realm Application Services/Sync, Compass, Cloud Manager and Data Lake. By getting hands-on working with code using realistic use cases, you will master the art of modeling, shaping and querying your data and become the MongoDB oracle for the business. You will focus on broadly used and niche areas such as optimizing queries, configuring large-scale clusters, configuring your cluster for high performance and availability and many more. Later, you will become proficient in auditing, monitoring, and securing your clusters using a structured and organized approach. By the end of this book, you will have grasped all the practical understanding needed to design, develop, administer and scale MongoDB-based database applications both on premises and on the cloud.
Table of Contents (22 chapters)
1
Part 1 – Basic MongoDB – Design Goals and Architecture
4
Part 2 – Querying Effectively
11
Part 3 – Administration and Data Management
16
Part 4 – Scaling and High Availability

MongoDB Atlas Data Lake

A data lake is a centralized repository that can be used to store, query, and transform heterogeneous structured and unstructured data. As the name implies, it acts as a lake of data.

MongoDB Data Lake (https://www.mongodb.com/atlas/data-lake) is a service offered by MongoDB that can help us process datasets across multiple MongoDB Atlas clusters and Amazon Web Services (AWS) Simple Storage Service (S3) buckets.

A data lake can query data in multiple formats such as JSON, BSON, comma-separated values (CSV), tab-separated values (TSV), Avro, Optimized Row Columnar (ORC), and Parquet. We can query the datasets using any driver, graphical user interface (GUI) tools such as MongoDB Compass, or the MongoDB shell using the standard MongoDB Query Language (MQL).

We can use x509 or SCRAM-SHA authentication methods. MongoDB Data Lake does not support Lightweight Directory Access Protocol (LDAP).

A data lake can be used for a variety of big data use cases...