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Please contact us
for GSA pricing
and CMAS pricing.
Contract #
GS-35F-0307T
Contract #
3-09-70-2645A
Recent Training Venues Accelebrate has recently trained for clients in the following cities:
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Hadoop Training: Introduction to Apache Hadoop
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Course Number: SRV-130
GSA/Previous Course Number: 263
Duration: 2 days
view class outline
Hadoop Training Overview
Accelebrate's Apache Hadoop training teaches Java developers how to use Apache Hadoop to build data-intensive, distributed applications. Hadoop is used by Facebook, IBM, LinkedIn, the New York Times, Yahoo, and many more of the world's most popular web sites and web applications. Location and Pricing
Most Accelebrate courses are taught on-site at our clients' locations worldwide for groups of 3 or more attendees and are customized to their specific needs. Please visit our client list to see organizations for whom we have recently delivered training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote private training at your site or online, please contact us.
In addition, some courses are available as live, online classes for individuals. To see a schedule of online courses, please visit http://www.accelebrate.com/online_training/java.htm.
Hadoop Training Prerequisites
All attendees should have a solid foundation in Java programming.
Hands-on/Lecture Ratio
This Hadoop training class is 50% hands-on, 50% lecture/discussion, with the longest lecture/discussion segments lasting 30 minutes.
Hadoop Training Materials
All students receive comprehensive courseware and a related textbook.
Software Needed on Each Student PC
A detailed setup sheet for this Hadoop training course is available upon request.
Hadoop Training Objectives
- Learn about the origins of the Hadoop framework
- Understand the computing problems best solved with Hadoop
- Set up a basic Hadoop cluster
- Review the networking topology that will affect performance
- Reveal and mitigate the weak points in a Hadoop cluster setup
- Write a MapReduce functor in Java
- Load the HDFS with bulk flat file data
- Build a real-world schema for HBase
- Import data from a traditional SQL source
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Hadoop Training Outline
- A world of petabyte-sized datasets
- Motivations behind Hadoop
- Today's volume of data generation
- Reasons for using Hadoop
- Who's using Hadoop
- Case study of Facebook
- Case study of CERN
- Cast study of Yahoo
- Type of problems being solved on MapReduce frameworks
- Search engines
- Data mining
- Sorting
- Image processing
- MapReduce
- Divide and conquer
- Advantages
- Automatic parallelism
- Massive scalability
- Code travels to the data, not the other way around
- Functor Capabilities
- Writing a map functor
- Thinking in small bites
- Supported languages
- A Java example
- RDMBS systems as a data source
- Job scheduling
- Configuration
- Supported OS platforms
- SSH
- Master nodes
- Worker/slave nodes
- Cluster topology
- Hardware purchase considerations
- Handling hardware failures
- HDFS
- Structured data vs unstructured data
- Comparison to UNIX file systems
- Variations on standard UNIX filesystem commands
- A file system over TCP/IP?
- Node types and purposes
- HDFS operations
- Formatting the file system
- Navigating the file system
- Home directories
- Shared namespace
- Importing existing flat files
- Block size
- Configuration
- Defaults and their impact
- Unique advantages
- How to leverage near-infinite storage
- Replication
- Self-healing
- HBase
- Semi-structured data
- Comparison to RDBMS systems
- SQL import
- RDMBS systems as a data source
- Sqoop
- Migrating SQL table structures
- Importing SQL table rows
- Pig
- Analytic queries
- Scripting language
- Applications for non-programmers
- Loading files into HDFS with Pig
- Running a MapReduce job with Pig
- Pig on EC2
- Hive
- Facebook origins of the project
- SQL for Hadoop
- Data warehousing
- Hive as an ETL tool
- High level language
- Using the web interface
- Hive tables
- Dealing with high latency response
- ZooKeeper
- Features
- Group services
- Configuration repository
- Synchronization helper
- Distributed, just like its Hadoop foundation
- Languages supported
- What are Znodes?
- High throughput, low latency, small data
- Chukwa
- Log collection tool
- Aids in analysis specific to log files
- Aggregation of disparate log information
- Amazon EC2
- Support for Hadoop
- Pay by the hour
- EBS support for HDFS storage
- SimpleDB support for HBase storage
- Conclusion
This course outline is copyright ©2010 Ambient Ideas, LLc. |
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Java® and all Java-based marks are trademarks or registered trademarks of Sun Microsystems, Inc. in the U.S. and other countries.JBoss® and Hibernate® are registered trademarks of Red Hat, Inc. Accelebrate, Inc. has no affiliation with Red Hat, Inc. and no courses offered by Accelebrate, Inc. are endorsed by Red Hat, Inc. in any way.
WebSphere® is a registered trademark of IBM. Accelebrate, Inc. has no affiliation with IBM. |
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Accelebrate®
Focuses on You! |
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Accelebrate’s courses are taught for private groups of 3 or more people at your site or online anywhere worldwide.
Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.
For pricing and to learn more, please contact us via information request form or phone, or email us at info@accelebrate.com today.
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