| Title : | Data analytics with Hadoop : an introduction for data scientists |
| Material Type: | printed text |
| Authors: | Benjamin Bengfort, Author |
| Publisher: | Beijing : O'Reilly |
| Publication Date: | 2016 |
| Pagination: | xvi, 268 p. |
| Size: | 24 cm |
| ISBN (or other code): | 978-1-491-91370-3 |
| General note: | Includes bibliographical references and index |
| Languages : | English (eng) Original Language : English (eng) |
| Descriptors: | Apache Hadoop.
Big data.
Data mining
Electronic data processing - Distributed processing
File organization (Computer science)
|
| Class number: | 006.312 |
| Abstract: | "Data analytics with Hadoop "- 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 |
| Contents note: | The age of the data product; An operating system for big data; A framework for Python and Hadoop streaming; In-memory computing with Spark; Distributed analysis and patterns; Data mining and warehousing; Data ingestion; Analytics with higher-level APIs; Machine learning; Summary : doing distributed data science; Index |
| Record link: | https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17989 |
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