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Understanding functional and technical aspects of Oracle Big Data 2017 Certification Implementation Specialist Certification Exam 1z0-449 Core Hadoop
The following will be discussed here:
- Load Hadoop Distributed File System Data into an Oracle database
- Describe Hadoop High Availability
- Explain the Hadoop Ecosystem
- Use Flume in the Hadoop Distributed File System
- Set up Hive with formatters and SerDe
- Create a database and table from a Hadoop Distributed File System file in Hive
- Explain the Oracle Loader for Hadoop
- Describe the differences between MapReduce and YARN
- Move data from a database to a Hadoop Distributed File System using Sqoop
- Configure the online and offline options for the Oracle Loader for Hadoop
- Core Hadoop
- Describe the importance of Namenode, Datanode, JobTracker, TaskTracker in Hadoop
- Describe the Impala real-time query and explain how it differs from Hive
- Cloudera Enterprise Hadoop Distribution
- Identify the benefits of the Hadoop Distributed File System (HDFS)
- Implement the Oracle Table Access for a Hadoop Connector
- Describe the architectural components of MapReduce
- Implement the Hadoop Distributed File System
- Execute a Hive query
- Implement the data flow mechanism used in Flume
- Use Pig Latin to query data in HDFS
- Describe the Hive architecture
- Oracle Loader for Hadoop
Reference: https://education.oracle.com/oracle-big-data-2017-implementation-essentials/pexam_1Z0-449