How to Prepare For Oracle Big Data 2017 Certification Implementation Specialist Certification Exam 1z0-449

Preparation Guide for Oracle Big Data 2017 Certification Implementation Specialist Certification Exam 1z0-449

Introduction

The Oracle Big Data 2017 Implementation Specialist certification is designed for individuals who possess a strong foundation and expertise in selling and implementing Oracle's Big Data solutions. This certification covers topics such as: Hadoop, Oracle NoSQL, Cloudera Technologies like Pig, Hive and Impala, and the Big Data Connectors. The Oracle Big Data 2017 Implementation Specialist certification differentiates candidates in the marketplace by providing a competitive edge through proven expertise. Up-to-date training and field experience are recommended. The certification is available to all candidates but is geared toward members of the Oracle PartnerNetwork. OPN members earning this certification will be recognized as OPN Certified Specialists.

The Oracle Big Data 2017 Implementation Specialist certification qualifies as competency criteria for the Oracle Big Data specialization.

The Oracle 1Z0-449 Certification Program certifies candidates on skills and knowledge related to Oracle Big Data X4-2 products and technologies. The Oracle Big Data 2017 Certification Implementation Specialist is granted based on a combination of passing exams, training and performance-based assignments, depending on the level of certification. Oracle 1Z0-449 certification is tangible benchmark of experience and expertise that help you stand out in a crowd among employers. To ensure success, Oracle recommends combining education courses, practice exam and hands-on experience to prepare for your Oracle Big Data 2017 Implementation Essentials certification exam as questions will test your ability to apply the knowledge you have gained in hands-on practice or professional experience.

Understanding functional and technical aspects of Oracle Big Data 2017 Certification Implementation Specialist Certification Exam 1z0-449 Oracle Data Integrator (ODI) and Hadoop

The following will be discussed here:

• Xquery for Hadoop Connector
• Oracle Data Integrator (ODI) and Hadoop
• Use ODI to move data from an Oracle database to a Hadoop Distributed File System using sqoop
• Describe how Big Data Appliance integrates with Exadata and Exalytics
• Describe the importance of Namenode, Datanode, JobTracker, TaskTracker in Hadoop
• Describe Hadoop High Availability
• Use Oracle Xquery with Hadoop-Hive to map an XML file into a Hive table
• Big Data Technical Overview - Describe the architectural components of the Big Data Appliance
• Identify and architect the services that run on each node in the Big Data Appliance, as it expands from single to multiple nodes
• Use Flume in the Hadoop Distributed File System
• Identify the benefits of the Hadoop Distributed File System (HDFS)
• Use ODI to move data from Hive to Oracle
• Implement the data flow mechanism used in Flume
• Perform a simple Xquery using Oracle XQuery for Hadoop
• Use ODI to transform data from Hive to Hive
• Configure the Oracle Data Integrator with Application Adaptor for Hadoop to interact with Hadoop
• Implement the Hadoop Distributed File System
• Describe the differences between MapReduce and YARN
• Set up Oracle Xquery for Hadoop connector
• Describe the Big Data Discovery and Big Data Spatial and Graph solutions
• Describe the architectural components of MapReduce
• Explain the business drivers behind Big Data and NoSQL versus Hadoop Core Hadoop - Explain the Hadoop Ecosystem

Reference: https://education.oracle.com/oracle-big-data-2017-implementation-essentials/pexam_1Z0-449

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:

• Use Pig Latin to query data in HDFS
• Oracle Loader for Hadoop
• Describe the importance of Namenode, Datanode, JobTracker, TaskTracker in Hadoop
• Explain the Oracle Loader for Hadoop
• Describe Hadoop High Availability
• Cloudera Enterprise Hadoop Distribution
• Move data from a database to a Hadoop Distributed File System using Sqoop
• Use Flume in the Hadoop Distributed File System
• Identify the benefits of the Hadoop Distributed File System (HDFS)
• Implement the data flow mechanism used in Flume
• Create a database and table from a Hadoop Distributed File System file in Hive
• Set up Hive with formatters and SerDe
• Explain the Hadoop Ecosystem
• Describe the Hive architecture
• Describe the Impala real-time query and explain how it differs from Hive
• Core Hadoop
• Load Hadoop Distributed File System Data into an Oracle database
• Implement the Hadoop Distributed File System
• Execute a Hive query
• Describe the differences between MapReduce and YARN
• Implement the Oracle Table Access for a Hadoop Connector
• Describe the architectural components of MapReduce
• Configure the online and offline options for the Oracle Loader for Hadoop

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