Isbn: 9781491913703 - data analytics with hadoop: an introduction for data scientists (32 resultados)

Paperback. Condición: Good. Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job.

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Paperback or Softback. Condición: New. Data Analytics with Hadoop: An Introduction for Data Scientists. Book.

Paperback. Condición: New. Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You'll also learn about the analytical processes and data systems available to build and empower data products that can handle-and actually require-huge amounts of data.Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlib.…

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

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Paperback. Condición: Brand New. 150 pages. 9.25x7.25x0.75 inches. In Stock.

Condición: New. "Data Analytics with Hadoop" introduces data scientists to the Hadoop ecosystem. It will introduce them to the world of clustered computing and analytics with Hadoop, as they might use and experience it. Num Pages: 230 pages. BIC Classification: UNC. Category: (XV) Technical / Manuals. Dimension: 250 x 150 x 15. Weight in Grams: 666. . 2016. 1st Edition. Paperback. . . . . …

Condición: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Condición: New. In English.

Condición: New. 150.

Condición: New. 150.

Condición: New. "Data Analytics with Hadoop" introduces data scientists to the Hadoop ecosystem. It will introduce them to the world of clustered computing and analytics with Hadoop, as they might use and experience it. Num Pages: 230 pages. BIC Classification: UNC. Category: (XV) Technical / Manuals. Dimension: 250 x 150 x 15. Weight in Grams: 666. . 2016. 1st Edition. Paperback. . . . . Books ship from the US and Ireland. …

Paperback. Condición: New. Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You'll also learn about the analytical processes and data systems available to build and empower data products that can handle-and actually require-huge amounts of data.Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlib.…