Found 21 Articles for Hadoop

Introduction Big Data is a term that has been making rounds in the world of technology and business for quite some time now. It refers to the massive volume of structured and unstructured data that is generated every day. With the rise of digitalization and the internet, the amount of data being generated has increased exponentially. This data, when analyzed correctly, can provide valuable insights that can help organizations make better decisions and improve their operations. In this article, we will delve into the characteristics of Big Data and the different types that exist. We will also provide real-life examples ... Read More

Data was previously stored in relational data management systems when Hadoop and big data concepts were not available. After introducing Big Data concepts, it was essential to store the data more concisely and efficiently. However all data stored in the related data management system needs to be transferred to the Hadoop archive. With Sqoop, we can transfer this amount of personal data. Sqoop transfers data from a related database management system to a Hadoop server. Thus, it facilitates the transfer of large volumes of data from one source to another. Here are the basic features of Sqoop − Sqoop ... Read More

The Hadoop framework is open-source that has the ability to expand computation and storage. A spread environment across a host of computers lets you store and process big data. As an alternative, Spark is an open-source clustering technology. It was designed to speed up computing. This product enables whole program clusters that are fault tolerant and implicitly parallel. The prime characteristic of Spark is in-memory cluster computing, which improves an application's speed. These technologies have some similarities and differences, so let's briefly discuss them. What is Hadoop? In the year of 2006, Hadoop began as a Yahoo project. ... Read More

Hadoop was built to store and analyze large volumes of data across several computer clusters. It's a group of software programs that construct a data processing framework. This Java-based framework can process enormous amounts of data quickly and cheaply.Hadoop's core elements include HDFS, MapReduce, and the Hadoop ecosystem. The Hadoop ecosystem is made up of many modules that help with system coding, cluster management, data storage, and analytical operations. Hadoop MapReduce helps analyze enormous amounts of organized and unstructured data. Hadoop's parallel processing uses MapReduce, while Hadoop is an Apache Software Foundation trademark.Millions of people use MongoDB, an open-source NoSQL ... Read More

Elasticsearch debuted on February 8, 2010. Programmers primarily utilize Java. Elasticsearch has an HTTP web interface and JavaScript Object Notation documents. Shay Banon created "Compass" in 2004 as a precursor to Elasticsearch. Shay Banon renamed Compass Elasticsearch and created a common interface called JavaScript Object Notation (HTTP). JSON is a better programming language than Java.On April 1, 2006, Doug Cutting and Mike Cafarella created Hadoop. It is an open-source software developed by Apache Software Foundation. Hadoop's core has two parts. First is the processing part, then storage. Hadoop's storage and processing segments are HDFS and MapReduce, respectively. Hadoop divides huge ... Read More

Kafka and Flume both are used for real time event processing system. They both are developed by Apache. Kafka is a publish-subscribe model messaging system. It can be used to communicate between publisher and subscriber using topic. One of the best features of Kafka is, it is highly available and resilient to node failures and supports automatic recovery.On the other hand, flume is mainly designed for Hadoop and it is a part of Hadoop ecosystem. It is used to collect data from different sources and transfer data to the centralized data store. Flume was mainly designed in order to collect ... Read More

Big Data is basically a term that covers large and complex data sets. To handle it, one requires use of different data processing applications when compared with traditional types.While there are various applications that allow handling and processing of big data, the base framework has always been that of Apache Hadoop.What is Apache Hadoop?Hadoop is an open-source software framework written in Java and comprises of two parts, which are the storage part and the other being the data processing part. The storage part is called the Hadoop Distributed File System (HDFS) and the processing part is called MapReduce.We now look ... Read More

As we know that in order to maintain the Big data and to get the corresponding reports in different ways from this data we use Hadoop which is an Open Source framework from Apache Software Foundation based on Java Programming Language.Now Apache introduces the next version of Hadoop which named as Hadoop 2 so as this post is focusing on differences between both of these versions.Following are the main differences between Hadoop 1 and Hadoop 2.Sr. No.KeyHadoop 1Hadoop 21New Components and APIAs Hadoop 1 introduced prior to Hadoop 2 so has some less components and APIs as compare to that ... Read More

A stack and a heap are used for memory allocation in Java. However, the stack is used for primitive data types, temporary variables, object addresses etc. The heap is used for storing objects in memory.Stacks and heaps in Java are explained in more detail as follows −Stack in JavaStacks are used to store temporary variables, primitive data types etc. A block in the stack exists for a variable only as long as the variable exists. After that, the block data is erased and it can be used for storing another variable.

The history of data models had three generations of DBMS −Hierarchical System was the first generation of DBMS. The first generation also came with the CODASYL system. Both of them introduced in 1960s.The second generation includes the Relational Model. Dr. E.F.Codd introduced it in 1970.The third generation includes Object-Relational DBMS and Object-Oriented DBMS.The history timeline of databases is shown below −File based systemsFile based systems came in 1960s and was widely used. It stores information and organize it into storage devices like a hard disk, a CD-ROM, USB, SSD, floppy disk, etc.Relational ModelRelational Model introduced by E.F.Codd in 1969. The ... Read More