Big data
Big data analytics using Python with Mongodb, postgress, cassandra, and hadoop

Big Data


Service Offerings:-
  • Analysis of users comments on Facebook, Twitter
  • Customer perception analysis for brand, products and shopping trends
  • Financial Report analysis
  • Log file monitoring
  • Discover Churn Patterns

The term "Big Data" is used for the collection of data sets that are very large and complex. And it becomes difficult to handle such data using simple database management tools. Python is flexible and open source language having powerful libraries like PyDoop and SciPy for big data analytics. So, it is easy to use for analytical computing.

Zealous System have experienced python programmers who are capable of manipulating, processing, cleaning and crunching big data in python using Hadoop and MongoDb. Both Hadoop and MongoDb have different benefits and they are used according to the type of problem.


Hadoop is used for large scale data processing. It has two main components:-
  • Hadoop Distributed File System-HDFS (Storage)
    It is distributed across nodes, natively redundant and track locations using NameNode.
  • MapReduce (Processing)
    It includes splitting task across processors and assembles result. It provides self-healing, high bandwidth and clustered storage.

Pydoop is a package that provides Python API for Hadoop MapReduce and HDFS. PyDoop has few advantages over Hadoop's built-in solutions for Python programming. One of the biggest advantage of PyDoop is it provides HDFS API which allows to connect to HDFS installation, read and write files, get information on files, directories and global file system properties. PyDoop also provides MapReduce API which allows to solve complex problems with less programming efforts. It also allows to implement advance MapReduce concepts like Counters and Record Readers.
SciPy is a Python based open source software for scientific computing. The SciPy stack includes packages like:-
  • NumPy for numerical computation
  • SciPy library provides collection of numerical algorithms and domain specific toolbox
  • Matplotlib provides 2D plotting and rudimentary 3D plotting
  • pandas provides high performance and easy to use data structure
  • SymPy is used for symbolic mathematics and computer algebra
  • IPython allows to quickly process data and test ideas

Benefits of using Hadoop:-
  • open source
  • scalable
  • cost effective
  • flexible
  • mapreduce implementation
  • can store very large files
  • manage and analyse any kind of data i.e from log files to video files
  • facility to analyse decentralised data across many storage system
  • for offline processing
  • process time measured in minutes or hours or days
Benefits of using MongoDb:-
  • more efficient
  • light weight
  • better performance
  • real time processing and data storage
  • great choice for mobile system
  • process time measured in milliseconds


Case Study



GPS tracking and Viewpoint python script, for farming and seed management
Technology  Python Django framework

Project was completed on time

Mr. Gerard Shaw
CEO Listo ltd


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