Foundations for architecting data solutions : managing successful data projects / Ted Malaska and Jonathan Seidman.Publisher: Sebastopol, CA : O'Reilly Media, 2018Copyright date: 2018Edition: First editionDescription: xii, 173 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 1492038741; 9781492038740Other title: Managing successful data projectsSubject(s): Big data -- Management | Database management | Database managementDDC classification: 005.7068 LOC classification: QA76.9.B45 | M353 2018Other classification: COM048000 | COM021040 | COM021030 | COM018000
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Key data project types and considerations -- Evaluating and selecting data management solutions -- Managing risk in data projects -- Interface design -- Distributed storage systems -- The meta of enterprise data -- Ensuring data integrity -- Data processing.
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 architectureUse data pipelines to ensure data integrity from source to final storage. Evaluate the attributes of various engines for processing the data you collect.