Answers and Solutions

Answer

It is Apache Hadoop , Hadoop is a project of Apache first made for webcrawling and later came to limelight with its great ability to process huge data thus Big data defined.

And Big data Hadoop is a Concept behind the Big data infrastructure . Hadoop is a platform where you can perform the big-data activities.

Answer

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, technqiues and frameworks.

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Answer

Big Data is a term used for a collection of data sets that are large and complex, which is difficult to store and process using available database management tools or traditional data processing applications. The challenge includes capturing, curating, storing, searching, sharing, transferring, analyzing and visualization of this data.

It is characterized by 5 V’s.

VOLUME: Volume refers to the ‘amount of data’, which is growing day by day at a very fast pace.

VELOCITY: Velocity is defined as the pace at which different sources generate the data every day. This flow of data is massive and continuous.

VARIETY: As there are many sources which are contributing to Big Data, the type of data they are generating is different. It can be structured, semi-structured or unstructured.

VALUE: It is all well and good to have access to big data but unless we can turn it into value it is useless. Find insights in the data and make benefit out of it.

VERACITY: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness.

First understand Big Data and challenges associated with Big Data. So , that you can understand how Hadoop emerged as a solution to those Big Data problems. This What is Hadoop..

Then you should understand how Hadoop architecture works in respect of HDFS, YARN & MapReduce.

Further moving on you should install Hadoop on your system so that you can start working with Hadoop. This will help you in understanding the practical aspects in detail.

 

Further moving on take a deep dive into Hadoop Ecosystem and learn various tools inside Hadoop Ecosystem with their functionalities. So, that you will learn how to create a tailored solution according to your requirements.

 

Answer

Hadoop course stands for :- Big Data Hadoop training will make you an expert in HDFS, MapReduce, Hbase, Hive, Pig, Yarn, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain. You will get edureka Hadoop certification at the end of the course.

Answer

Data which are very large in size is called Big Data. Normally we work on data of size MB(WordDoc ,Excel) or maximum GB(Movies, Codes) but data in Peta bytes i.e. 10^15 byte size is called Big Data. It is stated that almost 90% of today's data has been generated in the past 3 years.

Answer

Hadoop is an open source Java based programming framework that supports processing of a extremely big data sets in a cloud computing environment. Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure. This approach lowers the risk of catastrophic system failure and unexpected data loss, even if a significant number of nodes become inoperative. Consequently, Hadoop quickly emerged as a foundation for big data processing tasks, such as scientific analytics, business and sales planning, and processing enormous volumes of sensor data, including from internet of things sensors. Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2006 to support distribution for the Nutch search engine. It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts, which are also called fragments or blocks, can be run on any node in the cluster. After years of development within the open source community, Hadoop 1.0 became publically available in November 2012 as part of the Apache project sponsored by the Apache Software Foundation. Hadoop modules and projects As a software framework, Hadoop is composed of numerous functional modules. At a minimum, Hadoop uses Hadoop Common as a kernel to provide the framework's essential libraries. Other components include Hadoop Distributed File System (HDFS), which is capable of storing data across thousands of commodity servers to achieve high bandwidth between nodes; Hadoop Yet Another Resource Negotiator (YARN), which provides resource management and scheduling for user applications; and Hadoop MapReduce, which provides the programming model used to tackle large distributed data processing -- mapping data and reducing it to a result. Hadoop also supports a range of related projects that can complement and extend Hadoop's basic capabilities. Complementary software packages include: Apache Flume: A tool used to collect, aggregate and move huge amounts of streaming data into HDFS. Apache HBase: An open source, nonrelational, distributed database; Apache Hive: A data warehouse that provides data summarization, query and analysis; Cloudera Impala: A massively parallel processing database for Hadoop, originally created by the software company Cloudera, but now released as open source software; Apache Oozie: A server-based workflow scheduling system to manage Hadoop jobs; Apache Phoenix: An open source, massively parallel processing, relational database engine for Hadoop that is based on Apache HBase; Apache Pig: A high-level platform for creating programs that run on Hadoop; Apache Sqoop: A tool to transfer bulk data between Hadoop and structured data stores, such as relational databases; Apache Spark: A fast engine for big data processing capable of streaming and supporting SQL, machine learning and graph processing; Apache Storm: An open source data processing system; and Apache ZooKeeper: An open source configuration, synchronization and naming registry service for large distributed systems.

Answer

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers.

Big Data is a collection of large datasets that cannot be processed using traditional computing techniques

Big Data is a concept and not a technology or a tool.