Data Analytics with Hadoop : An Introduction for Data Scientists
Language: English
Published by O'reilly Media Jul 2016, 2016
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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 handleand actually requirehuge 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.…
Seller Inventory # 9781491913703
- Title
- Data Analytics with Hadoop : An Introduction for Data Scientists
- Author
- Benjamin Bengfort
- Publisher
- O'reilly Media Jul 2016
- Publication year
- 2016
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1491913703
- ISBN 13
- 9781491913703
- Item weight
- 502 grams
- Dimensions
- 231x177x20 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
Jenny Kim is an experienced big data engineer who works in both commercial software efforts as well as in academia. She has significant experience in working with large scale data, machine learning, and Hadoop implementations in production and research environments. Jenny (with Benjamin Bengfort) previously built a large scale recommender system that used a web crawler to gather ontological information about apparel products and produce recommendations from transactions. Currently, she is working with the Hue team at Cloudera, to help build intuitive interfaces for analyzing big data with Hadoop.
"About the title" may belong to another edition of this title.
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