Foundations architecting data solutions von malaska ted (8 Ergebnisse)

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Fair. A readable copy of the book which may include some defects such as highlighting and notes. Cover and pages may be creased and show discolouration.

Paperback. Zustand: Brand New. 225 pages. 9.25x7.00x0.50 inches. In Stock.

Zustand: New. pp. 190.

paperback. Zustand: Gut. 187 Seiten; 9781492038740.3 Gewicht in Gramm: 500.

Zustand: New. 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 t.

Taschenbuch. Zustand: Neu. Neuware - 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. …