Cassandra Data Modeling and Analysis (Anglais) Broché – 23 décembre 2014
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Description du produit
Présentation de l'éditeur
About This Book
- 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
Who This Book Is For
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.
What You Will Learn
- 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.
Biographie de l'auteur
C.Y. Kan is an expert in system integration and has over 20 years of IT experience, which includes 15 years of project management and an architect role in many large-scale international projects. He received a Bachelor's degree from the University of Hong Kong and later a Master's degree from the University of Technology, Sydney. He holds many professional qualifications such as PMP, PRINCE2 Practitioner, PMI-ACP, Scrum Master, CISSP, TOGAF9, and is a Certified SOA Architect. Mr. Kan is an Assistant Vice President now working for PCCW Solutions Limited, Hong Kong. He has expertise and interests in software technologies and development methodologies, which includes Enterprise architecture, Service-oriented architecture, Java-related technologies, traditional and NoSQL database technologies, Cloud computing, Big Data, mobile application development, agile software development, and various kinds of project management methodologies. Mr. Kan is often invited by Project Management Institute Hong Kong Chapter to teach courses on cloud computing, Big Data, service-oriented architecture, business process management, and agile software development. He is also the author of a video e-learning course, Cassandra Administration,Packt Publishing, which was published last year.
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Commentaires client les plus utiles sur Amazon.com
- provides an entry-level overview overview into how to lay out your data in Cassandra
- is written for a very recent version of Cassandra
- quick read (~160 pages)
- one single, overly simplistic use case/example problem
- "analysis" portion was very thin
- not enough detail on best practices, tradeoffs, etc.
With a title like this, I expected a book that went into much more depth as to how to model different types of problems, walk the user through different approaches to solving those problems, discuss tradeoffs of those different approaches, provide a thorough set of best practices when modeling data using Cassandra, etc. Instead, this book is written at a very basic introductory level. It walks the reader through a single example (a stock analysis tool), and really only considers a single use case (big data analysis). Additionally, too much of it's (short) length is spent discussing Python libraries, stock market analysis, etc.
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