Exploring Hadoop and MapReduce

What is Hadoop primarily used for?

• Hadoop is primarily used for graphic design.

• Hadoop is primarily used for mobile app development.

• Hadoop is primarily used for big data storage and processing.

• Hadoop is primarily used for web hosting.

Explain the role of HDFS in Hadoop.

• HDFS is a programming model for data processing in Hadoop.

• HDFS is used for real-time data analytics in Hadoop.

• HDFS is a web interface for managing Hadoop clusters.

• HDFS is the primary storage system of Hadoop, providing distributed storage, fault tolerance, and high throughput access to large datasets.

What is the function of the MapReduce framework?

• To visualize data in real-time dashboards.

• To store large data sets in a single location.

• To perform data encryption and security management.

• The function of the MapReduce framework is to enable distributed processing of large data sets.

Describe the difference between the Mapper and Reducer in MapReduce.

• The Mapper is responsible for outputting final results, while the Reducer handles input data.

• The Mapper sorts data and the Reducer filters it based on conditions.

• The Mapper aggregates data while the Reducer processes key-value pairs.

• The Mapper processes input data into key-value pairs; the Reducer aggregates these pairs by key to produce final results.

What programming languages can be used with Hadoop?

• Java, Python, R, Scala

• Go

• C++

• Ruby

How does Hadoop achieve fault tolerance?

• Hadoop achieves fault tolerance by compressing data to save space.

• Hadoop achieves fault tolerance by replicating data across multiple nodes.

• Hadoop achieves fault tolerance by deleting old data regularly.

• Hadoop achieves fault tolerance by using a single master node.

What is a Hadoop cluster?

• A Hadoop cluster is a single powerful computer used for data processing.

• A Hadoop cluster is a group of interconnected computers that work together to process and store large datasets using the Hadoop framework.

• A Hadoop cluster is a type of database management system.

• A Hadoop cluster is a cloud service for storing small files.

Define the term 'data locality' in the context of Hadoop.

• Data locality is the process of transferring data to a remote server for analysis.

• Data locality means duplicating data across multiple nodes for redundancy.

• Data locality is the principle of processing data on the same node where it is stored in Hadoop.

• Data locality refers to the ability to store data in a centralized location.

What is the purpose of the JobTracker in MapReduce?

• The JobTracker optimizes network bandwidth during data transfer.

• The JobTracker is responsible for data storage in HDFS.

• The JobTracker coordinates and manages the execution of MapReduce jobs.

• The JobTracker handles user authentication for the cluster.

Explain the concept of 'shuffling' in MapReduce.

• Shuffling is the final step where results are displayed to the user.

• Shuffling refers to the initial data input stage in MapReduce.

• Shuffling is the process of merging data from multiple sources into a single output.

• Shuffling is the process of redistributing and sorting data between the Map and Reduce phases in MapReduce.