By Mark Grover

Get specialist advice on architecting end-to-end info administration suggestions with Apache Hadoop. whereas many assets clarify how you can use numerous parts within the Hadoop environment, this sensible booklet takes you thru architectural concerns essential to tie these elements jointly right into a whole adapted program, in keeping with your specific use case.

To toughen these classes, the book’s moment part offers special examples of architectures utilized in essentially the most ordinarily stumbled on Hadoop purposes. no matter if you’re designing a brand new Hadoop software, or making plans to combine Hadoop into your latest facts infrastructure, Hadoop software Architectures will skillfully consultant you thru the process.

This ebook covers:

  • Factors to contemplate whilst utilizing Hadoop to shop and version data
  • Best practices for relocating info out and in of the system
  • Data processing frameworks, together with MapReduce, Spark, and Hive
  • Common Hadoop processing styles, similar to elimination reproduction documents and utilizing windowing analytics
  • Giraph, GraphX, and different instruments for giant graph processing on Hadoop
  • Using workflow orchestration and scheduling instruments reminiscent of Apache Oozie
  • Near-real-time flow processing with Apache hurricane, Apache Spark Streaming, and Apache Flume
  • Architecture examples for clickstream research, fraud detection, and information warehousing

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Chapter 6 discusses tying everything together with application orchestration and scheduling tools such as Apache Oozie. Chapter 7 discusses near-real-time processing on Hadoop. We discuss the relatively new class of tools that are intended to process streams of data such as Apache Storm and Apache Spark Streaming. In Part II, we cover the end-to-end implementations of some common applications with Hadoop. The purpose of these chapters is to provide concrete examples of how to use the components discussed in Part I to implement complete solutions with Hadoop: Chapter 8 provides an example of clickstream analysis with Hadoop.

We can meet our goal of having one interface to interact with our Avro and Parquet files, and we can have a block and columnar options for storing our data. Comparing Failure Behavior for Different File Formats An important aspect of the various file formats is failure handling; some formats handle corruption better than others: Columnar formats, while often efficient, do not work well in the event of failure, since this can lead to incomplete rows. Sequence files will be readable to the first failed row, but will not be recoverable after that row.

Chapter 10 provides a case study exploring another very common use case: using Hadoop to extend an existing enterprise data warehouse (EDW) environment. This includes using Hadoop as a complement to the EDW, as well as providing functionality traditionally performed by data warehouses. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

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