Hadoop Ecosystem Quiz

What best describes the role of Apache Hive in the Hadoop ecosystem?

• A framework for transferring bulk data between Hadoop and relational databases

• A high-level scripting language for processing large datasets

• A distributed NoSQL database for structured and unstructured data

• A data warehouse infrastructure with an SQL-like interface for querying data in HDFS

Which features contribute to Hadoop’s efficiency in handling large-scale distributed data?

• Scalability

• Fault tolerance

• Data locality

• Real-time processing

• Proprietary software

Which component is responsible for distributed storage and fault tolerance in Hadoop?

• Hive

• MapReduce

• YARN

• HDFS

What marked Hadoop’s transition from an experimental project to a full open-source framework?

• Evolution from Nutch to Apache Hadoop

• Separation of HDFS and MapReduce

• Introduction of YARN

• Implementation of erasure coding

Which principles are fundamental to Hadoop’s architecture?

• Collocating processing and storage

• Scaling up with more CPU cores

• Scaling out with more servers

• Breaking the disk read barrier

Which projects are integral to the Hadoop ecosystem?

• Apache Ambari

• Apache Zookeeper

• Apache Spark

• Apache Pig

• Apache Cassandra

What was the primary motivation behind Hadoop’s creation?

• Unified cloud-based architecture

• Storage and processing of unstructured data from web crawlers

• Real-time analysis of streaming data

• Fault-tolerant distributed relational database

What is the primary function of YARN in Hadoop?

• SQL-like interface for querying data

• Data transformation and processing

• Resource management and job scheduling

• Distributed data storage

What is a key advantage of using commodity hardware in Hadoop?

• Simplifies software development

• Enables faster computation

• Reduces operational costs with scalable fault-tolerant platform

• Enhances data security with secure hardware

Which features were introduced in Hadoop 3?

• Native cloud platform support

• Support for erasure coding

• Introduction of MapReduce

• Enhancements in application development and resource management