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The Ultimate Cheat Sheet On Hadoop

Top 20 frequently asked questions to test your Hadoop knowledge given in the below Hadoop cheat sheet. Try finding your own answers and match the answers given here.




Question #1 

You have written a MapReduce job that will process 500 million input records and generate 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reducers which is a potential bottleneck. A custom implementation of which of the following interfaces is most likely to reduce the amount of intermediate data transferred across the network?



A. Writable
B. WritableComparable
C. InputFormat
D. OutputFormat
E. Combiner
F. Partitioner
Ans: e




Question #2 

Where is Hive metastore stored by default ?


A. In HDFS
B. In client machine in the form of a flat file.
C. In client machine in a derby database
D. In lib directory of HADOOP_HOME, and requires HADOOP_CLASSPATH to be modified.
Ans: c




Question…

The best Multi purpose Language Python, why it is so useful

I have recently started learning Python. During my learning time my friends have asked since you are interested in analytics why you need to learn Python. I explained below reasons. This is one of the powerful languages after Java.

Python is similar to many programming languages that people generally know about: Python is very similar to JavaScript, Ruby, and PHP in many respects. 


Most programmers have a working knowledge of these programming languages and this makes it easier for programmers to learn Python. The basic features of these languages such as the use of arrays, anonymous functions etc., are also present in Python. 

 
Python has very good machine learning libraries: The variety of machine learning libraries that are available in Python is large. 


One can choose between Scikitlearn, Keras, Theano and Tensorflow. Many neural network libraries such as Keras, Theano etc., are exclusively available in Python. So, if you want to do cutting edge machine learning work, you must know Python. 

 
Python excels at handling text data: Unlike statistical software environments such as R, Python excels at handling text data. People who know Python can easily mine text corpus for useful insights. 



Python also provides support for Natural Language Processing through NLTK and sPacy
Python makes distributed computing very easy: Apache Spark has a Python API called PySpark. Using this piece of software, one can easily do distributed computing. PySpark has in recent times become the de-facto API for Spark. 


Extensive support for different data sources: It doesn’t matter if one needs to fetch data from an SQL server, a MongoDB database or JSON data from some web API; Python can easily support all these data sources with a very clean and elegant syntax. 


The benefits of Learning Python



Learning Python has many advantages – it gives a user many skills, one can fetch data from different sources, create machine learning models and do distributed computing seamlessly. For any programmer, learning Python will not be a difficult task. One can reap a lot of benefits by devoting time to learning Python.

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