While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project.
Too high level and not much substance. Although the book is meant to be an overview on the topic, the authors assume you know or are familiar with some data storage or processing systems.
I'd recommend the book by Martin Kleppmann instead.
A good overview on all major decisions required to plan a data processing projects. As a data engineer I feel I'm not the main target reader for this book, but it's nonetheless useful for the architecture and management part.
Skimmed through this book looking for some golden nuggets. It’s very high level and in some ways aimed more at management than tech staff. The ToC has a good list of topics but not as much depth as I hoped for.
A very interesting book about the concepts and main problems an engineer must face and tackle in the world of Big Data Arquitectures. I did a more detailed review here for those interested http://codecorner.balhau.net/foundati...
The almost exclusive focus on open-source technologies was definitely a strength (as explained by the author) but also a weakness in the world of the public cloud, where very few of us will be designing solutions with Apache Flink.