MapReduce (MR) is one of the core features of the Hadoop ecosystem which works in accordance with YARN (Yet Another Resource Negotiator). This is an out-of-the-box solution inbuilt in Hadoop to distribute the processing of data across multiple clusters. MR divides the data, saves it into multiple partitions and then processes it. The Mapping transforms […]
Hadoop Distributed File System (HDFS) is the file system on which Hadoop stores its data. This is the underlying technology that helps the data to be stored in the distributed manner across the cluster. It helps applications to get access to the data for fast mining/analyzing and users can be assured that the data that […]
Apache Ambari is a web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters. Ambari provides Restful APIs and a web-based management interface. Ambari started as a sub-project of Hadoop but has now become a fully-fledged tool used by many Hadoop developers and administrators. The latest version available is Ambari 2.5.1 System requirements for Ambari: […]
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning is a method by which knowledge is gained through learning the patterns and the […]
The Gearman is a extension which is used to distribute work load to different processes or machines to optimize them. It is an anagram for a manager who can efficiently delegate tasks. Gearman is an opensource application framework designed to distribute appropriate computer tasks to multiple computers, so large tasks can be done more quickly. This was developed […]
The secret of a successful mobile/web application depends largely on the user experience and its value to end-users. By and large, industry experts agree that for a product to be accessible by end-users, the app or website must completely capture their attention.