Hadoop Training & Certification
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Big Data Hadoop Certification Training is designed by industry experts to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers the In-depth understanding of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce.
The Google authorized Android Development course is primarily designed for programmers who want to learn how to create mobile applications on the Android platform. As a part of this course, you will create widgets, Customize List view, Grid view, Spinners etc, create applications using audio, video and sqlite database and finally publish it on Google Play. This course will help you learn mobile app development from scratch and unlock new job opportunities for you in start-ups as well as large organizations. Master Android app development, learn how to set up Android Studio, understand Android architecture in detail, learn about integrating your mobile apps with Facebook, Twitter and other social media, Google Drive, Google Maps, SQLite and learn how to create and optimize app user experience.
The ‘Introduction to Big Data and Hadoop’ is an ideal course package for individuals who want to understand the basic concepts of Big Data and Hadoop. On completing this course, students will be able to interpret what goes behind the processing of huge masses of data as the industry switches over from Excel-based analytics to real-time analytics.The course focuses on the basics of Big Data and Hadoop. It further provides an overview of the commercial distributions of Hadoop as well as the elements of the Hadoop ecosystem.
MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. The map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers.
Apache Hive provides a SQL-like language called HiveQL, which transparently convert queries to MapReduce for execution on large datasets stored in Hadoop Distributed File System (HDFS). … Apache HBase is an open source NoSQL database that provides real-time, read and write access to large datasets.
Apache Spark is often referred to as data processing engine. Simply put, Spark is a cluster computing engine that made it easy to handle a wide range of workloads: ETL, SQL-like queries, machine learning and streaming. The amount of code you write is also minimized to a great extent compared to traditional MapReduce development. Also, it has been proven to be 10x faster than Apache Mahout.
Oozie is a workflow scheduler system to manage Apache Hadoop jobs.
Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions.
Oozie Coordinator jobs are recurrent Oozie Workflow jobs triggered by time (frequency) and data availability.
Oozie is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) as well as system-specific jobs (such as Java programs and shell scripts).
Oozie is a scalable, reliable and extensible system.