Book Image

Mastering MongoDB 3.x

By : Alex Giamas
Book Image

Mastering MongoDB 3.x

By: Alex Giamas

Overview of this book

MongoDB has grown to become the de facto NoSQL database with millions of users—from small startups to Fortune 500 companies. Addressing the limitations of SQL schema-based databases, MongoDB pioneered a shift of focus for DevOps and offered sharding and replication maintainable by DevOps teams. The book is based on MongoDB 3.x and covers topics ranging from database querying using the shell, built in drivers, and popular ODM mappers to more advanced topics such as sharding, high availability, and integration with big data sources. You will get an overview of MongoDB and how to play to its strengths, with relevant use cases. After that, you will learn how to query MongoDB effectively and make use of indexes as much as possible. The next part deals with the administration of MongoDB installations on-premise or in the cloud. We deal with database internals in the next section, explaining storage systems and how they can affect performance. The last section of this book deals with replication and MongoDB scaling, along with integration with heterogeneous data sources. By the end this book, you will be equipped with all the required industry skills and knowledge to become a certified MongoDB developer and administrator.
Table of Contents (13 chapters)

What is big data?

The internet has grown over the last few years and is not showing any signs of slowing down. Just in the last five years, internet users have grown from a little under 2 billion to around 3.7 billion, accounting for 50% of Earth's total population (up from 30% just 5 years ago).

With more internet users and networks evolving, every year adds increasingly more data to existing datasets. In 2016, global internet traffic was 1.2 zettabytes (which is 1.2 billion terabytes) and it is expected to grow to 3.3 zettabytes by 2021.

This enormous amount of data generates increased needs for processing and analysis. This has generated the need for databases and data stores in general that can scale and efficiently process our data.

The term big data was first coined in the 1980's by John Mashey and mostly came into play in the past decade with the explosive growth...