Big Data and Hadoop
In a world where data is fueling the growth of organizations, it won’t be wrong to assume that companies ingest raw data in large volumes from numerous sources. But, how can they identify the data which is both useful and insightful? This is where Big Data comes to play. Hadoop is an open-source framework that is used to process Big Data. The average salary of a Big Data analyst in the US is around $61,000.
Watch this video on Big Data Hadoop before going further with this Hadoop tutorial:
Big Data and Hadoop Tutorial covers Introduction to Big Data,Overview of Apache Hadoop,The Intended Audience and Prerequisites, The Ultimate Goal of this Tutorial, The Challenges at Scale and the Scope of Hadoop, Comparison to Existing Database Technologies,The Hadoop Architecture & Module, Introduction to Hadoop Distributed File System, Hadoop Multi Node Clusters, HDFS Installation and Shell Commands, Hadoop MapReduce – Key Features & Highlights, Hadoop YARN Technology, Introduction to Pig, Sqoop and Hive.
Some of the exciting facts about Big Data are as follows:
This clearly specifies the kind of potential the field of Big Data has. After learning these facts you must be curious to know about Big Data. Let’s now check out the applications of Big Data briefly.
||Big Data applications
||Big Data helps understanding customers and target them in a personalized fashion.
|Science and Research
||Big Data helps make machines smarter. For example, Google’s self-driving cars
||Big Data is used to keep track of the terrorists and anti-national agencies
||Big Data algorithms are used to analyze market and trading opportunities
Big Data and Hadoop Tutorial Video:
After reading this tutorial, you as an individual will have enough working knowledge and proficiency in the following:
- Apache Hadoop framework
- Hadoop Distributed File System
- Visualizing of Data using MS Excel, Zoomdata or Zeppelin
- Apache MapReduce programming
- Apache Spark ecosystem
- Ambari administration
- Deploying Apache Hive, Pig, and Sqoop
- Knowledge of the Hadoop 2.x Architecture
- Data analytics using Hadoop YARN
- Deploying MapReduce and HBase integration
- Setting up of Hadoop Cluster
- Proficiency in Hadoop Development
- Working with Spark RDD
- Job scheduling using Oozie
The ultimate goal of this Tutorial is to help you become a professional in the field of Big Data and Hadoop and ensuring you have enough skills to work in an industrial environment and solve real-world problems to come up with solutions that make a difference to this world.