Hortonworks Hadoop-PR000007 Exam Topics
Hortonworks Hadoop-PR000007 Exam Overview :
| Exam Name: | Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) |
| Exam Code: | Hadoop-PR000007 |
| Certifications: | Hortonworks Hadoop Developer Certification |
| Actual Exam Duration: | 120 minutes |
| Expected no. of Questions in Actual Exam: | 90 |
| See Expected Questions: | Hortonworks Hadoop-PR000007 Expected Questions in Actual Exam |
Hortonworks Hadoop-PR000007 Exam Objectives :
| Section | Objectives |
|---|---|
| Apache Hadoop Fundamentals | The Apache Hadoop Fundamentals section provides an overview of the Hadoop ecosystem, focusing on its core components such as HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce. It covers the fundamental principles of HDFS, including how data is stored across a distributed file system, the concept of data replication for fault tolerance, and the typical access patterns for large-scale data storage. Additionally, the role of YARN in managing and scheduling resources within a Hadoop cluster is discussed, highlighting how it orchestrates the execution of various applications and manages resource allocation. |
| Apache Hive | In the Apache Hive section, candidates are introduced to Hive, an important tool in the Hadoop ecosystem for querying and managing large datasets. This includes understanding Hive's architecture and the role of the Hive Metastore, which stores metadata about the data. The section delves into Hive Query Language (HQL), which is similar to SQL and allows for writing queries to perform operations such as SELECT, JOIN, GROUP BY, and ORDER BY. |
| Apache Pig | The Apache Pig section covers Pig, another key component of the Hadoop ecosystem, which is designed for processing and analyzing large datasets. The focus is on Pig's architecture and its scripting language, Pig Latin. Candidates learn how to write Pig Latin scripts to perform data transformations, including operations such as FILTER, FOREACH, JOIN, GROUP, and SPLIT. The section also addresses working with schemas in Pig and handling various data types. |
| Apache Hadoop Fundamentals | The Apache Hadoop Fundamentals section provides an overview of the Hadoop ecosystem, focusing on its core components such as HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce. It covers the fundamental principles of HDFS, including how data is stored across a distributed file system, the concept of data replication for fault tolerance, and the typical access patterns for large-scale data storage. |
| Data Analysis and Manipulation | In the Data Analysis and Manipulation section, candidates learn about various techniques for filtering and transforming data using both Hive and Pig. This includes performing aggregations and joining datasets to combine information from multiple sources. |
| Performance Tuning and Optimization | The Performance Tuning and Optimization section addresses techniques for enhancing the performance of Pig scripts and Hive queries. For Pig, this includes optimizing scripts through the use of combiners, parallel execution strategies, and minimizing data shuffling. |
| Use Cases and Practical Applications | The final section, Use Cases and Practical Applications, explores how Hive and Pig can be applied to real-world scenarios. This includes practical examples of data cleansing, ETL (Extract, Transform, Load) processes, and large-scale data analysis tasks. Candidates are expected to demonstrate their ability to apply Hive and Pig to solve complex data problems and manage data workflows effectively in various business contexts. |
Updates in the Hortonworks Hadoop-PR000007 Exam Topics:
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