About Bigdata/Hadoop Testing
Big data is not a fad. We are just at the beginning of a revolution that will touch every business and every life on this planet — and data sets are large enough nowadays that traditional data processing technologies such as relational database management systems (RDBMS) or data centers are no longer enough.
What is big data testing? Well, most of us now know about big data. It refers to huge or big data which is difficult to manage by our traditional computing systems. These big data includes social media data, search engine data, stock exchange data and many more. Big data testing course will teach trainees, how to do testing of all these data through a platform called Hadoop. This is an open source software which processes huge data in various servers. This software can manage single server to various machines and can handle high level of errors.
Because of its usefulness, Hadoop is becoming a backbone for various industries and big data testing course is gaining popularity for the same.
Performance and functional testing are the two main parts of big data Hadoop testing. This testing involves data processing verification instead of testing various features of the product. In big data testing, verification of data is done using commodity cluster. This requires excellent testing skills as the processing procedure is quite fast. In big data testing, data quality also matters a lot. A tester should test the quality of data, as it is a part of database testing.
Hadoop or big data testing are mainly categorized into 3 steps:
- Data from all the sources are checked in order to ensure that correct data is going into system.
- Comparison of system data and the data sent to Hadoop system is done
- Verification is done to ensure that right data is extracted and loaded into suitable HDFS location
- This step works accurately and segregation rules are employed.
- Validation of data is done after MapReduce step.
- This step checks whether the transformation rules are appropriately applied or not.
- Validates the integrity of data and successful loading of data into the target system.
- Checks final data with HDFS system for validating data corruption.
McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
If you want to learn what we mean by Big Data, then you should start here.
Why should you learn Hadoop?
- Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research.
- Shortage of 1.4 -1.9 million Big Data Hadoop Analysts in the US alone by 2018–
- Hadoop Testing Professionals in the US can get a salary of $132,000 – com.
So there’s never been a better time to learn some of the technologies that can handle Big Data.
Are you interested in Hadoop testing course? If yes, then you are on right page! Get software services teach Hadoop testing in such a way that anyone can learn concepts of Hadoop without any difficulty. Our teaching efforts cover all the technical details of Hadoop for making our students, masters of Hadoop. Quality training is a must for learning Hadoop, and that is what we do. We heartily welcomes analytical minds who are interested in learning big data. Our experienced professionals strive to convert a beginner into an expert of big data.
Our Hadoop testing course includes real-time projects, thus making you aware of technical challenges of Hadoop. After completion of Hadoop testing course, we provide big data testing certification to students, which adds value to their career.
It is believed that in the coming 3 years, almost half of the data will migrate to Hadoop. This will generate a high demand for big data professionals in future. Our Hadoop testing course plays a pivotal role in filling the gap between demand and supply of Hadoop professionals. Our big data trainees will get hands-on experience on different components like Pig, HDFS, Impala, MapReduce, Flume and Sqoop. Our professionals provide complete big data testing training which is a perfect blend of technology and analytics. This ensures bright future of our aspirants who want to get big data testing jobs and make a good start in big data.
There is no prerequisite for big data testing training anyone with a technical mind can take advantage of this course.
Hadoop is now deployed in most of the sectors. With time the possibility of complexities in Hadoop big data is expected. Hence there is a need of Hadoop testers who can support Hadoop architects and developers in their projects. Our big data testing training strives to render right skills to trainees, in order to open the door the opportunities as a Hadoop tester for them. Gain practical knowledge on our big data testing tools and explore this new skill under the guidance of our experienced faculties.
Let’s take a deep dive and achieve a good knowledge of Hadoop ecosystem.
Hope the above information is sufficient for you to think about doing big data testing course. Enrol for this course and open the doors for Hadoop testing jobs.
Hadoop/Bigdata Training Syllabus
Introduction to Big Data and Hadoop.
Why we need Hadoop.
Hadoop distributions.
Technologies used for various components in Hadoop.
Hadoop Installation
Features of HDFS
Pillars of Hadoop – HDFS, MapReduce, and YARN.
Hadoop Architecture
MapReduce
MapReduce architecture
MapReduce example
Advantages of MapReduce
Introduction to YARN
Difference between MRV1 & MRV2
YARN Application workflow in MapReduce
Introduction to Hive
Hive Installation
Hive Basics
Primitive Datatypes
Collections_Arrays_Maps
Create Table
Insert Into Table
Alter Table
HDFS CLI – Interacting with HDFS
Built-in Functions
The Case-When statement, the Size function, the Cast function
The Explode function
Sub-Queries
Views
Partitioning
Bucketing
Windowing
Windowing – Example
HQL Primer – Select Statements
HQL Primer – Group By, Order By and Having
More Group By Examples
Order By
Having
HQL Primer – Joins
Spark Installation
APACHE SPARK (BigData Analytics) Introduction
Why Spark when Hadoop is already there
Difference between Hadoop vs Spark
Spark Overview
Using Hadoop through Spark
Spark Features
Spark ECO Systems
Spark Architecture(Spark Core)
Introduction to Scala
Platforms and editors
Running Spark on a Cluster
SparkSQL, DataFrames, and DataSets
Advanced Examples of Spark Programs
Machine Learning with MLLib
Introduction to Spark Streaming
Introduction to GraphX
Introduction to Hbase
Introduction to Sqoop
Introduction to OOzie
