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
