Foundations for Architecting Data Solutions: Managing Successful Data Projects

by Ted Malaska, Jonathan Seidman

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.

Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect

Genres: Computer Science, Technology, Technical, Software

192 pages, Paperback

Published November 2, 2018

Reviews

[Thanonchai · Rating 2 out of 5]

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.

[Bruno · Rating 4 out of 5]

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.

[Oliver · Rating 2 out of 5]

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.

[Nebogipfel · Rating 3 out of 5]

This "book" is more like a set of notes someone took while learning about various technologies.

[Balhau · Rating 4 out of 5]

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...

[Rodrigo Domínguez · Rating 3 out of 5]

Good handbook on data arch fundamentals.

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.

[Natu Lauchande · Rating 3 out of 5]

A couple of good recipes for the current trends in the area, nothing particular impressive.

[Mike Fowler · Rating 3 out of 5]

Reads something like a checklist of pointers with few specific recommendations. Useful for helping to think about non-functional requirements.

[Sireesha · Rating 3 out of 5]

Easy to read, simple book that highlights some key aspects.

[Adam · Rating 3 out of 5]

Not as inspiring as hoped

[Slava]

Так себе. Много воды