The IBM Open Platform with Apache Hadoop training course provides an in-depth introduction to the main components of the ODP core --namely Apache Hadoop (inclusive of HDFS, YARN, and MapReduce) and Apache Ambari - as well as providing a treatment of the main open-source components that are generally made available with the ODP core in a production Hadoop cluster.
By attending IBM Open Platform with Apache Hadoop workshop, delegates will learn to:
- List and describe the major components of the open-source Apache Hadoop stack and the approach taken by the Open Data Foundation.
- Manage and monitor Hadoop clusters with Apache Ambari and related components
- Explore the Hadoop Distributed File System (HDFS) by running Hadoop commands.
- Understand the differences between Hadoop 1 (with MapReduce 1) and Hadoop 2 (with YARN and MapReduce 2).
- Create and run basic MapReduce jobs using command line.
- Explain how Spark integrates into the Hadoop ecosystem.
- Execute iterative algorithms using Spark's RDD.
- Explain the role of coordination, management, and governance in the Hadoop ecosystem using Apache Zookeeper, Apache Slider, and Apache Knox.
- Explore common methods for performing data movement
- Configure Flume for data loading of log files
- Move data into the HDFS from relational databases using Sqoop
- Understand when to use various data storage formats (flat files, CSV/delimited, Avro/Sequence files, Parquet, etc.).
- Review the differences between the available open-source programming languages typically used with Hadoop (Pig, Hive) and for Data Science (Python, R)
- Query data from Hive.
- Perform random access on data stored in HBase.
- Explore advanced concepts, including Oozie and Solr
- Knowledge of Linux would be beneficial.
The IBM Open Platform with Apache Hadoop class is meant for those who want a foundation of IBM BigInsights. This includes: Big data engineers, data scientist, developers or programmers, administrators who are interested in learning about IBM's Open Platform with Apache Hadoop.