Hadoop with Python

Introduction

Hadoop with Python is an important requirement in Big Data. It is the finest solution for storing and processing Big Data as Hadoop stores enormous files. Those are kept in the shape of the Hadoop distributed file system (HDFS) without identifying any schema.

Hadoop is very scalable as one number of nodes may be added to improve presentation. Data is vastly accessible in Hadoop if there is any hardware fiasco that happens.

Nowadays, the first choice of Data Scientist is the Python programming language. Hadoop provides Python APIs that deliver processing of the Big Data. It similarly, enables easy access to big data platforms.

In this post, we are going to discuss Hadoop with Python and its two key components in detail.

Description

The framework of Hadoop is written in Java language. Though, Hadoop programs may be coded in Python and C++ language. We can write programs corresponding to MapReduce in Python language. However, not the obligation for translating the code into Java jar files.

Modules of Hadoop:

There are generally two modules of Hadoop:

  • HDFS (Hadoop Distributed File System)
  • MapReduce

Hadoop Distributed file system(HDFS)

Hadoop Distributed file system (HDFS) is intended to store scores of information. It is normally found in petabytes, gigabytes, and terabytes. This is completed by using a block-structured file system. Distinct files are divided into fixed-size blocks. That is kept on machines through the cluster. Files prepared with many blocks usually do not have all of their blocks stored on a single machine.

HDFS makes sure dependability by replicating blocks and allocating the replicas across the cluster. The default replication factor is 3. It means that every block occurs three times on the cluster. Block-level replication allows data obtainability even when machines fail.

Hadoop file system was designed to consist of the distributed file system model. It is tremendously fault-tolerant and developed using cheap hardware. The data servers of the name node and knowledge node ease consumers to just check the status of the cluster.

Hadoop Streaming performances like a bridge between the Python code and the Java-based HDFS. It allows us to seamlessly contact Hadoop clusters and perform MapReduce tasks.

HDFS makes available file permissions and authentication.

Hadoop Distributed file system(HDFS)

MapReduce

This is a programming model. It is related to the application of processing and producing big data sets. That can be obtained with the help of equivalent, circulated algorithmic rules on a cluster.

  • A MapReduce program contains a mapping procedure.
  • That executes filtering and sorting.
  • It is also a decrease technique, which does an outline operation.
  • The MapReduce program could be a data processing framework.
  • That is helpful to process data on the cluster.

MapReduce

  • Map and reduce are two uninterrupted phases.
  • Every map job functions on separate parts of data.
  • The reducer does the job on the data produced by the mapper on distributed data nodes after the map.
  • MapReduce used Disk IN or OUT to do operations on data.

Example of Map Reduce Program

Mapper.py

Mapper.py

Reducer.py

Reducer.py

Conclusion

  • We learned in this post how Python can develop a good and able tool for Big Data Processing.
  • We may assimilate all the Big Data tools with Python.
  • Python makes data processing stress-free and faster.
  • Python has developed an appropriate choice not only for Data Science then similarly for Big Data processing.