Big Data and Apache Hadoop Practice Exam

Big Data refers to huge volume of structured and unstructured data whichis generated at high speeds usually by social media, and IoT devices. The RDBMS are unable to handle big data hence, Apache Hadoop is used, which is an open-source software to store and manage Big Data by using a distributed storage system (HDFS) and parallel processing with the MapReduce.

Certification in Big Data and Apache Hadoop verifies your skills and knowledge in storing, managing and processing big datasets using Hadoop. This certification assess you in HDFS, MapReduce, Hive, Pig, and Spark.

Why is Big Data and Apache Hadoop certification important?

• Enhances career prospects in data-centric roles.
• Validates hands-on expertise in Big Data tools and techniques.
• Demonstrates proficiency in distributed computing and data analysis.
• Builds credibility in handling large-scale data solutions.
• Aligns with industry demand for Big Data professionals.

Who should take the Big Data and Apache Hadoop Exam?

• Data Engineers
• Data Scientists
• Big Data Architects
• Machine Learning Engineers
• ETL Developers
• Business Intelligence Analysts
• System Administrators
• Hadoop Administrators

Skills Evaluated

Candidates taking the certification exam on the Big Data and Apache Hadoop is evaluated for the following skills:

• Understanding of Hadoop ecosystem components (HDFS, MapReduce, Hive, Pig, Spark).
• Designing and implementing data pipelines.
• Writing and optimizing MapReduce programs.
• Working with distributed storage systems.
• Querying and analyzing data using Hive and Pig.
• Integrating Hadoop with other tools and platforms.
• Managing and maintaining Hadoop clusters.

Big Data and Apache Hadoop Certification Course Outline
The course outline for Big Data and Apache Hadoop certification is as below -

 

Domain 1. Introduction to Big Data

• Characteristics of Big Data (Volume, Velocity, Variety).

• Use cases and applications.

Domain 2. Hadoop Basics

• HDFS architecture.

• MapReduce programming model.

Domain 3. Hadoop Ecosystem

• Hive: Query language and data modeling.

• Pig: Scripting and data transformation.

• Sqoop: Data import/export.

• HBase: NoSQL database.

• Spark: Real-time processing.

Domain 4. Data Processing with Hadoop

• Writing and debugging MapReduce programs.

• Working with Hive and Pig.

Domain 5. Hadoop Administration

• Cluster setup and configuration.

• Resource management with YARN.

• Monitoring and troubleshooting.

Domain 6. Advanced Topics

• Hadoop integration with cloud platforms.

• Security and access control in Hadoop.