Big Data Analytics Quiz!

Apache Hadoop is distributed under the Apache License 2.0. This license is a permissive open-source license that allows users to freely use, modify, and distribute the software for any purpose. It also grants users the right to sublicense and distribute derivative works. The Apache License 2.0 ensures that users have the freedom to use Hadoop and its associated components without any significant restrictions, promoting collaboration and innovation within the open-source community.

Hadoop is capable of dealing with structured, semi-structured, and unstructured data. Structured data refers to data that is organized in a fixed format, such as data stored in relational databases. Semi-structured data refers to data that does not have a fixed format but contains some organizational elements, such as XML or JSON files. Unstructured data refers to data that does not have any specific organization or format, such as text documents, images, or videos. Hadoop's distributed processing framework allows it to handle and analyze all types of data, making it a versatile tool for big data processing.

All of the options mentioned (YARN, HDFS, MapReduce) are components of Hadoop. YARN (Yet Another Resource Negotiator) is the resource management layer of Hadoop, responsible for managing and allocating resources to applications. HDFS (Hadoop Distributed File System) is the distributed file system used by Hadoop to store and retrieve data. MapReduce is the programming model used by Hadoop for processing and analyzing large datasets in parallel across a cluster of computers. Therefore, all three options are correct components of Hadoop.

The correct answer is Java because Hadoop is a framework that is primarily written in Java. Java provides the necessary tools and libraries to handle large-scale data processing and distributed computing, which are the core functionalities of Hadoop. Additionally, Java's object-oriented nature and platform independence make it a suitable choice for developing a framework like Hadoop that can run on various operating systems and hardware configurations.

Apache Hadoop is a framework that is designed to run on various platforms, making it cross-platform. It is not limited to a specific operating system or hardware, allowing it to be deployed on different environments such as Windows, Linux, and macOS. This flexibility enables organizations to leverage Hadoop's capabilities regardless of their existing infrastructure, making it a popular choice for big data processing and analysis.

The correct answer is "All of the above" because in Hadoop, there are three main daemons: NameNode, Node Manager, and DataNode. The NameNode is responsible for managing the metadata of the Hadoop Distributed File System (HDFS). The Node Manager is responsible for managing resources and scheduling tasks on each individual node. The DataNode is responsible for storing and retrieving data in HDFS. Therefore, all three options mentioned are valid daemons in Hadoop.

The statement "All are true" means that all of the given options are true about Hadoop. This implies that Hadoop is indeed a distributed framework, it utilizes the Map Reduce algorithm as its main algorithm, and it is capable of running on commodity hardware.

The correct answer is .har.

Hadoop works in a master-slave fashion, where there is a single master node that manages and coordinates the overall operations, and multiple slave nodes that perform the actual data processing tasks. The master node assigns tasks to the slave nodes and collects the results from them. This architecture allows for distributed and parallel processing, making Hadoop a scalable and efficient framework for big data processing.

Apache Hadoop achieves reliability by replicating the data across multiple hosts and hence does not require RAID storage on hosts. RAID (Redundant Array of Independent Disks) is a data storage technology that combines multiple physical disk drives into a single logical unit to improve performance and data redundancy. However, Hadoop achieves reliability through data replication across multiple hosts, eliminating the need for RAID storage on individual hosts.

The property that gets configured on mapred-site.xml is the host and port where the MapReduce job runs. This configuration allows the system to know where to execute the MapReduce tasks and where to send the results back. It is important to correctly configure this property to ensure that the MapReduce jobs are executed on the desired hosts and ports.

Data locality refers to the practice of bringing the computation closer to the data it operates on, rather than moving the data to where the computation is happening. This approach improves performance and efficiency by reducing the amount of data transfer and network communication required. By moving the computation to the data, it avoids the overhead of moving large amounts of data across a network, which can be time-consuming and resource-intensive. Therefore, the correct statement is that data locality means moving computation to data instead of data to computation.

Flume is the correct answer because it is an Apache system specifically designed for ingesting streaming data to Hadoop. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data from various sources into Hadoop for analysis and processing. It provides a flexible and scalable architecture that allows data ingestion from multiple sources and delivers it to Hadoop in a reliable and efficient manner.

Hadoop is a framework that is known for its ability to process and store large amounts of data across a cluster of computers using commodity hardware. It is a part of the Apache project sponsored by the ASF, which means it is an open-source software developed by a community of contributors. However, Hadoop is not specifically designed for live streaming of data. While it can handle real-time data processing to some extent, there are other technologies like Apache Kafka or Apache Flink that are better suited for live streaming applications.

The command "jps" is used to check the status of all daemons running in the HDFS. Jps stands for Java Virtual Machine Process Status Tool, and it is used to list all Java processes running on a machine. By running the "jps" command, it will display the names and process IDs of all Java processes, including the HDFS daemons, such as the NameNode, DataNode, and SecondaryNameNode. Therefore, "jps" is the correct command to check the status of all daemons running in the HDFS.