Big Data Certification focuses on the core concepts of Big Data Analytics including Hadoop, MapReduce, Yarn, Pig, Hive, Spark and much more. The exam is designed by our experts to certify individuals who have comprehensive hands-on knowledge of Big Data analytics.
As Big Data has taken the business world by storm, there are millions of career opportunities in this domain. This exam aspires individuals to become credible for a successful career in big data analytics domain.
Apache Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab.
Data is stored in a distributed manner in HDFS. There are two components of HDFS - name node and data node. While there is only one name node, there can be multiple data nodes. HDFS is specially designed for storing huge datasets in commodity hardware.
Features of HDFS
Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.
Apache Hadoop YARN is the resource management and job scheduling technology in the open source Hadoop distributed processing framework. One of Apache Hadoop's core components, YARN is responsible for allocating system resources to the various applications running in a Hadoop cluster and scheduling tasks to be executed on different cluster nodes. YARN stands for Yet Another Resource Negotiator
Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads.
Apache Pig is an open-source technology that offers a high-level mechanism for the parallel programming of MapReduce jobs to be executed on Hadoop clusters. Pig enables developers to create query execution routines for analyzing large, distributed data sets without having to do low-level work in MapReduce, much like the way the Apache Hive data warehouse software provides a SQL-like interface for Hadoop that doesn't require direct MapReduce programming,
Apache Hive is a data warehouse system built on top of Apache Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in various databases and file systems that integrate with Hadoop, including the MapR Data Platform with MapR XD and MapR Database. Hive offers a simple way to apply structure to large amounts of unstructured data and then perform batch SQL-like queries on that data. Hive easily integrates with traditional data center technologies using the familiar JDBC/ODBC interface.