Book Image

Mastering Apache Cassandra 3.x - Third Edition

By : Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj
Book Image

Mastering Apache Cassandra 3.x - Third Edition

By: Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj

Overview of this book

With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features. Once you’ve covered a brief recap of the basics, you’ll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You’ll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You’ll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apache Spark. By the end of this book, you’ll be able to analyse big data, and build and manage high-performance databases for your application.
Table of Contents (12 chapters)

Integration with Apache Spark

This chapter will provide different options for a SQL interface for the Apache Cassandra NoSQL database using Spark CLIs. With distributed, non-relational databases such as Apache Cassandra, it's really hard to run ad hoc analytical queries (that require data aggregation). These types of queries require both a relational interface and an aggregation capability, and there are out-of-the-box options, among which Spark is one. This chapter will provide an overview of Spark architecture with its installation and configuration, along with different CLIs to perform Create, Read, Update, and Delete (CRUD) operations using any relational queries. Additionally, there is a web UI for multiple components that are integrated into the Spark architecture to understand the in-depth working of all of the tasks behind the scenes for any query with visualization...