Hadoop 2.0 Developer Certification
The Certified Apache Hadoop 2.0 Developer certification is intended for developers who design, develop and architect Hadoop-based solutions, consultants who create Hadoop project proposals and Hadoop development instructors. Candidates for this exam are Data Analysts, BI Analysts, BI Developers, SAS Developers and other types of analysts who need to answer questions and analyze Big Data stored in a Hadoop cluster. Those certified are recognized as having a high level of skill in Apache Hadoop development.
The Certified Apache Hadoop 2.x exam consists of 50 open response and multiple-choice questions. The exam is delivered in English.
Certification candidates may take two practice exams at no charge. Register at the certification site.
This exam is administered through Kryterion, Inc. The exam can be sat at authorized testing center or via remote proctoring. For additional information and to register please visit our certification site.
The time allotted for the exam is 90 minutes.
A passing score is 75%.
If a candidate does not pass an exam on the first attempt, he or she may register and sit the exam as soon as the final score is delivered. After the second attempt a candidate must wait 7 calendar days from their original appointment time before he or she can register to retake the exam. Should a candidate need to retake the exam again there will be a 10 day waiting period. Once the exam is Passed, a candidate may not make any further attempts.
Hortonworks offers certification for Administrators and Hadoop Developers who are developing in Java.
Courses to Prepare
The following courses can help prepare a certification candidate for the Hadoop 2.0 Developer Certification. Course participation is encouraged but not required. Any student who attends these courses will receive a voucher to cover the cost of one certification attempt:
Core Topic Areas
Objective 1.1 – HDFS and Hadoop 2.0
- Explain Hadoop 2.0 and YARN
- Explain how HDFS Federation works in Hadoop 2.0
- Explain the various tools and frameworks in the Hadoop 2.0 ecosystem
- Use the Hadoop client to input data into HDFS
- Using HDFS commands
Objective 2.1 – MapReduce and YARN
- Explain the architecture of MapReduce
- Run a MapReduce job on Hadoop
- Monitor a MapReduce job
Objective 3.1 – Pig
- Write a Pig script to explore and transform data in HDFS
- Define advanced Pig relations
- Use Pig to apply structure to unstructured Big Data
- Invoke a Pig User-Defined Function
- Compute Quantiles with Pig
- Explore data with Pig
- Split a dataset with Pig
- Join datasets with Pig
- UsePig to prepare data for Hive
Objective 4.1 – Hive and hCatalog
- Write a Hive query
- Understand how Hive tables are defined and implemented
- Use Hive to run SQL-like queries to perform data analysis
- Perform a multi-table select in Hive
- Design a proper schema for Hive
- Explain the uses and purpose of HCatalog ™
- Use HCatalog with Pig and Hive
- Computing ngrams with Hive
- Analyzing Big Data with Hive
- Understanding MapReduce in Hive
- Joining datasets with Hive
- Streaming data with Hive and Python
Objective 5.1 – Hadoop Tools
- Use Sqoop to transfer data between Hadoop and a relational database
- Using Sqoop to transfer data between HDFS and a RDBMS
- Using HCatalog with Pig
- Define a workflow using Oozie