In the information age, everything is becoming digital. This variety of high volume digital data being created day by day need to be stored in a way that is fast, easy and cheap. Hadoop can store enormous amount of data on enormous cluster of computers. No data is too big if you handle it with Hadoop.
By understanding customer behavior, Hadoop makes it feasible to analyze and explore the large amount of data with related programming environments for various purposes. It can work across inexpensive servers that can both store and process data which is called as distributed processing and in Parallel Processing you can work on one or more servers at the same time. Hadoop is open source software which utilizes distributed parallel processing.
Before storing your data you don’t need to know how to analyze your data. Almost anything in any format such as emails, audio files, structured data, unstructured data can easily be managed using Hadoop. Hadoop will perform the same function which you will tell to perform irrespective of data size, format etc. Hadoop is flexible as data of any type or format can be stored.
Without changing the data formats, new data can be added whenever needed to cluster in the form of nodes. The system redirects the work to another location of the data if a node is lost. Hence the process continues. It is cost effective as there is a sizeable decrease in the cost per terabyte of storage. It does not require special parallel processing hardware which makes it affordable. Hadoop analyses large volume of data in comparatively less time.
Hadoop answers to the queries you could never think of. It makes all the available data meaningful and useable for better analysis, research and also for development work.
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You search for the course with great demand in market when you need to boost your career growth. The current wave of Hadoop has tremendous opportunities. Fortunately you have the Best Hadoop Training in Noida. Learn Hadoop from the domain experts at Quaatso Education in Noida. Companies now-a-days are hiring Java Developers with Hadoop skills. At Quaatso, the Big Data Online Course is advanced and the information they provide are as per market requirements. They also provide the Big Data Certification after course completion.
The course module for Online Hadoop Training at Quaatso Education is very interesting. It covers all the concepts with useful examples and videos that can help you get complete knowledge about the subject. They also provide with the study materials and class recordings which will making learning and recalling easy. After successful completion of Hadoop Training, Quaatso provides Big Data Hadoop Certification and Digital Badges to demonstrate your knowledge and achievements.
Quaatso has well defined course modules and training sessions for Hadoop Course Online. The online training at Quaatso includes 24×7 supports of Instructors for queries. At Quaatso training is conducted during day time classes, weekend classes, evening batch classes and fast track training classes.
|Section 1: Introduction to Big Data and Hadoop|
|What is Big Data?||02:00:00|
|History of Big Data||02:00:00|
|Challenges for processing Big Data||02:00:00|
|Technologies supporting Big Data||02:00:00|
|What is Hadoop?||02:00:00|
|History of Hadoop||02:00:00|
|Scope of Hadoop||02:00:00|
|Section 2: Hadoop Essentials|
|Installing and Set up of Hadoop||02:00:00|
|Core elements of Hadoop||02:00:00|
|Data management in Hadoop||02:00:00|
|Use cases of Hadoop||02:00:00|
|Hadoop Ecosystem Components||02:00:00|
|When to use Hadoop||02:00:00|
|Section 3: HDFS (Hadoop Distributed File System)|
|Introduction to HDFS||02:00:00|
|Role of HDFS in Hadoop||02:00:00|
|Features of HDFS||02:00:00|
|Daemons of Hadoop||02:00:00|
|Data Storage in HDFS||02:00:00|
|Section 4: MapReduce|
|Introduction to MapReduce||02:00:00|
|How MapReduce works||02:00:00|
|Creating Input and Output Formats in MapReduce Jobs||02:00:00|
|Data Localization in MapReduce||02:00:00|
|Combiner and Partitioner||02:00:00|
|Section 5: PIG|
|Introduction to Apache PIG||02:00:00|
|PIG Data Flow Engine||02:00:00|
|MapReduce Vs. PIG||02:00:00|
|Data Types in PIG||02:00:00|
|Modes of Execution in PIG||02:00:00|
|Operators/Transformations In PIG||02:00:00|
|When to use PIG||02:00:00|
|Section 6: HIVE|
|Introduction to HIVE||02:00:00|
|Data Types in HIVE||02:00:00|
|Partitions and buckets||02:00:00|
|Joins in HIVE||02:00:00|
|Section 7: HBASE|
|Introduction to HBASE||02:00:00|
|Fundamentals of HBASE||02:00:00|
|HBASE Data Model||02:00:00|
|HMaster and Region Servers||02:00:00|
|HBASE Vs. RDBMS||02:00:00|
|HBASE Designing Tables||02:00:00|
|HDFS Vs. HBASE||02:00:00|
|Section 8: Flume|
|Introduction to Flume||02:00:00|
|Uses of Flume||02:00:00|
|Section 9: SQOOP|
|Introduction to SQOOP||02:00:00|
|Use of SQOOP||02:00:00|
|Joins in SQOOP||02:00:00|
|Export to HBASE||02:00:00|