Big Data Quiz For Students!

Velocity is one of the 3Vs of data. The 3Vs of data, also known as the three dimensions of big data, are Volume, Velocity, and Variety. Velocity refers to the speed at which data is generated, processed, and analyzed. In the context of big data, velocity represents the rapid rate at which data is being produced and the need to handle and analyze it in real-time.

Hadoop is a framework that is commonly used with various related tools. One of the common cohorts or combinations of tools that work with Hadoop includes MapReduce, Hive, and HBase. MapReduce is a programming model and software framework for processing large amounts of data in parallel, Hive is a data warehouse infrastructure that provides data summarization, query, and analysis, and HBase is a distributed, scalable, and consistent NoSQL database that is built on top of Hadoop. Together, these tools can be used to efficiently process and analyze big data.

Hadoop is accurately described as "Open source" because it is an open-source software framework used for distributed storage and processing of large datasets. It allows for the processing of big data across clusters of computers using simple programming models, making it accessible to a wide range of users. Being open-source means that the source code is freely available, allowing users to modify and customize it according to their needs.

The correct answer is Cutting's son's toy elephant. Hadoop was named after Doug Cutting's son's toy elephant. This suggests that the name "Hadoop" was chosen based on a personal connection and not related to any technical aspect or specific event during the development of Hadoop.

As companies become more experienced with Hadoop, they realize the need for additional capabilities. One of the key needs is improved security to protect their data from unauthorized access. Workload management is also important to ensure efficient resource allocation and prioritize critical tasks. Additionally, SQL support is crucial for companies to easily query and analyze their data using familiar language and tools. These capabilities help companies enhance the overall functionality and usability of their Hadoop systems.

MapReduce is a framework developed by Google. It is used for processing and generating large data sets in a distributed computing environment. The framework provides a programming model for parallel processing and a distributed file system for storing and accessing data. MapReduce has been widely adopted and is the basis for many big data processing systems, including Apache Hadoop.

Sqoop uses the Text File Format for importing the data from SQL to Hadoop. This format allows the data to be stored as plain text files, making it easy to read and process. Sqoop converts the SQL data into text format and imports it into Hadoop, where it can be further analyzed and processed using various tools and frameworks.

The question asks for a framework that is not built on Spark. GraphX, Millib, and SparkSQL are all frameworks that are built on top of Spark and provide additional functionalities. D-Streams, on the other hand, is not a framework built on Spark. D-Streams stands for Discretized Streams and it is a Spark component that provides support for processing real-time streaming data.

MapR is the correct answer because it is a technology that is specifically designed for batch data processing. MapR provides a distributed file system and a set of tools and frameworks that enable efficient and scalable batch processing of large volumes of data. It offers features like data replication, fault tolerance, and high availability, making it well-suited for handling batch data processing workloads. Hive, Storm, and Apache Zeppelin are also technologies used in big data processing, but they are more commonly associated with other types of data processing tasks such as data querying, real-time stream processing, and data visualization, respectively.

All of the above are important components of Big Data. YARN (Yet Another Resource Negotiator) is a key component of Hadoop that manages and allocates resources in a Hadoop cluster. It is used to schedule and manage resources for running data processing jobs. MapReduce is a programming model and processing framework for processing large datasets in parallel across a distributed cluster. It is one of the core components of the Hadoop ecosystem. HDFS (Hadoop Distributed File System) is the primary storage system used by Hadoop to store and manage large volumes of data across a distributed cluster.