Cassandra Data Modeling and Analysis

• Build professional data models in Cassandra using CQL and appropriate indexes
• Grasp the Model-By-Query techniques through working examples
• Step-by-step tutorial of a stock market technical analysis application

If you are interested in Cassandra and want to develop real-world analysis applications, then this book is perfect for you. It would be helpful to have prior knowledge of NoSQL database.

• Discover the unique way of query-driven data modeling in Cassandra
• Explore the differences between a data model of a relational database and that of Cassandra
• Master the correct uses of the primary index, composite key, compound key, and secondary index
• Design a high-performance Cassandra data model
• Develop a complete, real-world technical-analysis application for the stock market
• Grasp the techniques of evolving a data model in production
• Determine effective performance tuning, replication, and system-monitoring strategies

Starting with a quick introduction to Cassandra, this book flows through various aspects such as fundamental data modeling approaches, selection of data types, designing a data model, choosing suitable keys and indexes through to a real-world application, all the while applying the best practices covered in this book.

Although the application is small, you will be involved in the full development life cycle. You will go through the design considerations of coming up with a flexible and sustainable data model for a stock market technical-analysis application written in Python. As business changes continually and so does a data model, you will also learn the techniques of evolving a data model to address new business requirements. Running a web-scale Cassandra cluster requires many careful considerations such as evolving a data model, performance tuning, and system monitoring. This book is an invaluable tutorial for anyone who wants to adopt Cassandra.