Developing Solutions for Apache Hadoop on Windows
Students will learn to develop applications and analyze big data stored in Apache Hadoop running on Microsoft Windows. Students will learn the details of the Hadoop Distributed File System (HDFS™) architecture and MapReduce framework, as well as learn how to develop applications on Hadoop® using tools like C#, Pig™, Hive™, HCatalog, Sqoop, Oozie and Microsoft Excel.
Hortonworks certification identifies you as an expert in the Apache Hadoop ecosystem.
Big Data Partnership is able to provide both public open-enrolment and custom, on-site Hadoop training courses for organisations across the region that prefer to train groups of staff together.
Benefits of onsite training include:
Key features of Hortonworks instructor-led Hadoop training courses:
- Understanding Big Data and Hadoop
- The Hadoop Distributed File System (HDFS)
- Inputting Data into HDFS
- The MapReduce Framework
- Developing .NET MapReduce application in C#
- Introduction to Pig
- Advanced Pig Programming
- Hive Programming
- Using HCatalog
- The Hive ODBC Driver
- Defining Workflow with Oozie
- .NET Developers and Data Analysts responsible for developing applications and performing analysis on big data using the Hortonworks Data Platform for Windows. Students should have programming experience, preferably with Visual Studio and SQL, as well as familiarity with the Windows Server operating system. No prior Hadoop knowledge is required.
- The Developing Solutions for Apache Hadoop on Windows class is a four-day course
- Please read the Developing Solutions for Apache™ Hadoop® on Windows data sheet for additional course details.
- For more information around pricing and private training opportunities contact us at
Please contact us for any questions on Developing Solutions for Apache™ Hadoop® on Windows.
Hortonworks University – 2013 Developer Classes
Developing Solutions for Apache™ Hadoop® on Windows
Tuesday, November 19, 2013 at 9:30 AM – Friday, November 22, 2013 at 5:30 PM (BST)
London, United Kingdom
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