Basics Big Data and Hadoop Practice Exam

A certificate in Basics of Big Data and Hadoop provides foundational knowledge in managing and analyzing large datasets. This industry-recognized credential validates your understanding of core concepts like Hadoop architecture, HDFS (Hadoop Distributed File System), and MapReduce programming.

Who Should Take This Exam?

This exam is ideal for:

• Individuals with an interest in data analysis and data science.
• IT professionals seeking to expand their skillset into big data technologies.
• Anyone interested in pursuing a career in big data engineering or data warehousing.

Required Skills:

While no prior experience is mandatory, a basic understanding of computer science concepts and familiarity with databases will be helpful.

Why is This Exam Important?

The big data field is experiencing tremendous growth, and skilled professionals are in high demand. Earning this certificate demonstrates your foundational knowledge and prepares you for further big data learning or entry-level big data positions.

Exam Course Outline

• Big Data Fundamentals: Introduction to big data, its characteristics (volume, variety, velocity), challenges of big data management, and comparison with traditional data management.
• Hadoop Ecosystem: Understanding the core components of Hadoop - HDFS (Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce (programming framework for processing large datasets).
• HDFS (Hadoop Distributed File System): HDFS architecture, data storage principles (blocks, replicas), file management operations (read, write, delete).
• MapReduce Programming: Introduction to MapReduce paradigm, writing basic MapReduce jobs (mapper, reducer functions), understanding job scheduling and execution flow.
• Introduction to Big Data Tools: Exposure to commonly used big data tools like Hive (data warehousing), Pig (dataflow language), and Sqoop (data transfer between relational databases and Hadoop).