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 book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether you’re 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 data

Best practices for moving data in and out of the system

Data processing frameworks, including MapReduce, Spark, and Hive

Common Hadoop processing patterns, such as removing duplicate records and using windowing analytics

Giraph, GraphX, and other tools for large graph processing on Hadoop

Using workflow orchestration and scheduling tools such as Apache Oozie

Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume

Architecture examples for clickstream analysis, fraud detection, and data warehousing

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