Bøger af Ted Malaska

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 typesUse guidelines to evaluate and select data management solutionsReduce risk related to technology, your team, and vague requirementsExplore system interface design using APIs, REST, and pub/sub systemsChoose the right distributed storage system for your big data systemPlan and implement metadata collections for your data architectureUse data pipelines to ensure data integrity from source to final storageEvaluate the attributes of various engines for processing the data you collect

Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case.To reinforce those lessons, the books second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether youre designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process.This book covers:Factors to consider when using Hadoop to store and model dataBest practices for moving data in and out of the systemData processing frameworks, including MapReduce, Spark, and HiveCommon Hadoop processing patterns, such as removing duplicate records and using windowing analyticsGiraph, GraphX, and other tools for large graph processing on HadoopUsing workflow orchestration and scheduling tools such as Apache OozieNear-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache FlumeArchitecture examples for clickstream analysis, fraud detection, and data warehousing