Foundations for Architecting Data Solutions
Language: English
Published by O'Reilly Media, US, 2018
- Softcover
- New

Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
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Condition: New
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Add to basketItem description from seller
Big Data Solution Architecture provides everyone from CIOs and COOs to lead architects and lead developers with the fundamental concepts of big data development. Authors Ted Malaska and Jonathan Seidman guide you through all the major components necessary to start, architect, and develop successful big data projects. This practical book covers a variety of different big data architectures and applications, from massive data pipelines to web scale applications. Each chapter addresses a different part of the software development life cycle and identifies patterns that build on one another to maximize success throughout the life of your project. You'll learn how to: Build a Big Data center of excellence in your company for the first time Identify and manage risk in your data project Retain and motivate teams to increase engagement and innovation Maximize Big Data ROI and align cost structure to help your company attain success.
Seller Inventory # LU-9781492038740
- Title
- Foundations for Architecting Data Solutions
- Author
- Ted Malaska, Jonathan Seidman
- Publisher
- O'Reilly Media, US
- Publication year
- 2018
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1492038741
- ISBN 13
- 9781492038740
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
"Synopsis" may belong to another edition of this title.
About the Author
Jonathan is a software engineer on the Cloud team at Cloudera. Prior to that, he was a solutions architect at Cloudera working with partners to integrate their solutions with Cloudera’s software stack. Previously, he was a technical lead on the big data team at Orbitz Worldwide, helping to manage the Hadoop clusters for one of the most heavily trafficked sites on the internet. He's also a co-founder of the Chicago Hadoop User Group and Chicago Big Data, co-author of Hadoop Application Architectures, technical editor for Hadoop in Practice, and has spoken at a number of industry conferences on Hadoop and big data,
"About the title" may belong to another edition of this title.
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