Hadoop Python Projects

Hadoop is used to store a large volume of processed data in clustered format. For instance: the MapReduce task can be coded in python where there is no need for java code translation using JAR files  One of the best frameworks to handle large-scale data is Hadoop. Although it is developed in Java, it can also be coded with C++ and python languages.. Before getting into Hadoop Python Projects, one should understand the role of python in Hadoop concepts!!!

Our developers have long-term practice in developing Hadoop Projects. So, if you are interested to do your research / final year project in Hadoop using python, then connect with us. We assure you that we make you understand in what way one can integrate python with the following tools, 

• MapReduce
• Pig Latin script
• Apache Spark
• HDFS
• Apache Pig Platform

From this article, you can know the overview of the latest Hadoop python projects with research updates!!! 

Hadoop Python Projects 

Python is enriched with several in-built characteristics and pre-defined packages. Therefore, it is capable to support both large and small-size data in an effective manner. Also, it allows both structured and unstructured data. All these reasons made python the most convenient language to develop big data applications using Hadoop. Here, we have given you the importance of python on using over big data applications. 

Big Data Python Projects 

• Easy Learning 
  • It is easy to learn similar to the English language
  • It has simple syntax to code which is readable for learners
  • It simplifies the code work than other languages
  • It is more popular due to its user-friendliness and ease
  • It is flexible to handle machine learning, data science, and other web applications
• Open Source
  • It is an open-source high-level language that has OSI approval
  • It has a distributable feature for general purposes
  • It uses an interpreter instead of the compiler which supports virtual python code execution in all systems
  • It is a developer-friendly language for easy code
• Spark and Hadoop Adaptability 
  • It is capable to embed with the Hadoop framework
  • It enables to write code in C++ and Python languages
  • It combines python API with Spark to produce PySpark (developed by Apache Spark group)
  • It incorporates RDDs using PySpark shell which connects spark and python API
• Flexibility / Scalability
  • It is extensively used for artificial intelligence and machine learning
  • It is easy to write code for ML / Artificial Intelligence with minimal knowledge of the distributed system. It supports cluster-based expandability.
• Sophisticated Data Processing Libraries
  • It supports all sorts of operations in data processing using extensive tools
  • It is gradually getting advanced in libraries and tools
  • It can also embed with other languages. For instance: Java
  • It improves the functionalities and libraries with the present structure
• Efficacy and Rapidness  
  • It is a robust interpreter language that supports OOPs
  • It is easy to code fast with an assurance of optimal results
  • It enhances the efficiency and throughput of developers
  • It always produces the best performance

Now, we can see about the Hadoop environmental setup for developing Hadoop python projects. Generally, it established distributed environment in the form of the cluster(s). As well, there are two major types of Hadoop setup where one is a multi-node cluster and the other is a single-node cluster. 

Our developers are professional experience in both these environmental setups. We have developed numerous real-time and non-real-time applications in these setups. Based on the project requirements, we help you to choose one among them for your project.  

Hadoop Setup for Python 

• Hadoop with Multi-Node Cluster 
  • Executes on Ubuntu Linux
  • Supports Hadoop Distributed File System (HDFS)
  • Set multiple nodes Hadoop cluster in distributed environ
• Hadoop with Single-Node Cluster 
  • Set single-node Hadoop cluster and pseudo-distributed platform 

What kind of Hardware is best for Hadoop?

In general, Hadoop executes on dual-core machines / dual-processor. Also, it supports ECC memory and 4 to 8 GB RAM. Further, based on the workflow of the project, the hardware setup will be decided. When you confirm your project topic with us, we provide the basic hardware and software requirements of your handpicked project with an implementation plan.

In addition, our developers have given you the three important input formats followed by network requirements. Once the Hadoop environment and hardware setup are established, then the following two requirements need to be decided. If you are new to this field, then we guide you appropriately based on project motives and needs.  

What are the input formats of Hadoop?

• SequenceFileInputFormat
• TextInputFormat (default format)
• KeyValueInputFormat 

Network requirements for Hadoop

• Establish server operations by Secure Shell (SSH)
• Provide SSH connectivity without passwords

Next, we can see core libraries that are extensively utilized in Hadoop python projects. Since it is the most important thing to know for implementing Hadoop projects in python. These libraries will be upgraded frequently to support all modern technologies in Hadoop. As well, it simplifies the heavy computing work while using complex conventional techniques. Moreover, this colossal collection of libraries plays a significant role in the fast growth of python over Hadoop projects. Our developers are proficient enough to handle all these libraries in a useful way through smart approaches. 

Hadoop Python Libraries 

• Luigi
  • It is introduced by Spotify as an alternative to python and it executes over PyPI
  • It allows challenging batch jobs pipelines for building and configuring
  • It enables to work with several Hadoop technologies along with visualization, dependency resolution, workflow control, etc.
  • Installation Command
    • • Method 1: using pip command

• • • • • $ pip install Luigi
      • Method 2: using source
        • $ git clone https://github.com/spotify/luigi
        • $ python setup.py install
• PySpark
  • It is a connection point of python API and Apache spark
  • It makes application through inter-responsive shell/python program
  • Installation Command
    • pip install pyspark
• HDFS CLI
  • It enables python bindings for HttpFS / WebHDFS API
  • It facilitates both insecure and secure clusters through command-line interface
  • It distributes files and begins a communicative client shell
  • It also includes aliases for namenode URL caching and extension for below operations,

• • • • Kerberos – To facilitate Kerberos validated clusters
      • Avro – To collect and create Avro files via HDFS
      • Dataframe – To load and store Pandas dataframes
    • Installation Command
      • $ pip install hdfs
• Mrjob
  • It is specially used for performing multi-step MapReduce tasks in Hadoop streaming
  • It is also called as MapReduce library which developed in python
  • It can be tested and executed on cloud (via Amazon EMR) and Hadoop cluster
• Installation commands

• • • Method 1: using pip command
      • $ pip install mrjob
    • Method 2: using source
      • $ python setup.py install
• Pydoop
  • It is a Hadoop-Python interface for accessing HDFS API
  • It uses code to write MapReduce pre-defined functions like Partitioner and RecordReader
  • It also accesses Mapreduce in the absence of Java
• Snakebite
  • It is introduced by soptify as the python client library
  • It enables to access HDFS through python scripts
  • It interacts with namenode through protobuf messages
  • It comprises a command-line interface for using HDFS
  • It needs python-protobuf 2.4.1 and above versions on python 2
  • It executes over PyPI
  • Installation Command

• • • • $ pip install snakebite
• libhdfs3 
  • It is now rising with Apache HAWQ which merely used as a C++ library
  • It is introduced by Pivotal Labs for HAWQ SQL-on-Hadoop
  • It is an alternative to libhdfs at the C API level

Furthermore, our developers have shared with you the most significant project topics for Hadoop python projects. Due to the vast improvement of Hadoop over big data, it widely spreads in many technological fields. Also, the open-source python makes Hadoop projects easier and developer-friendly by providing enriched libraries and packages. Our developers have in-depth skills in working with these libraries and packages.

Therefore, it attracts both the research interested people and final year students for their research / academic projects. Further, it is also used in industrial sectors for storing and analyzing bulk business information. We ensure you that our project topics are up-to-date which are collected from the latest Hadoop and big data research areas. Here, we have given you a few important Hadoop python project topics from our latest collections. 

Hadoop Python Projects [Interesting Research Topics]

• Online Criminal Activities Prediction
• Hadoop-based Text Mining and Classification
• Encryption Layer Design for NoSQL Databases
• Facebook Face Recognition and Analysis
• HDFS-based Cache-Control in Distributed Systems
• Traffic Design, Prediction, and Simulation
• Cyber Forensics for Fraud Detection
• Real-time Customer Products Recommenders
• NLP-based Knowledge Findings in Scanned Records
• Design of Stream Processing Engines (like Apache S4 and Apache Spark Streaming) 

Text mining using Hadoop and Python

From the above list of hadoop project ideas, we have handpicked “Hadoop-based Text Mining and Classification” as an example. In this, consider that you are dealing with the product reviews concept. For that, Hadoop is used to summarize, examine and classify the product review through sentiment analysis.

As well, the product grades are classified as bad, neutral, and good. Moreover, you can also include the review slang for opening mining and improve the application based on customer requirements. Below, we have mentioned to you the brief operations of this application.

• Access HTML data using command and shell language
• Store data in Hadoop Distributed File System (HDFS)
• Perform preprocessing of data using PySpark in Hadoop
• Execute initial Query using SQL assistant like Hue
• Use Tableau for data visualization

Overall, we provide end-to-end support on handling and processing large unstructured data using Hadoop technology in python language. For that, we have framed countless innovative project topics from all possible research areas. Further, we also support you in your desired research area and ideas of Hadoop. Our ultimate ambition is to support you from the latest project topic selection to project delivery. For add-on benefits, we also extend our service on providing project documentation/dissertation. Therefore, connect with us to create an incredible Hadoop python projects.