Foundations for Architecting Data Solutions: Managing Successful Data Projects

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