13 Reasons Why You Should Choose Python For Big Data Analysis

Caroline Richards — Sat, 29 Jun 2019 06:59:14 +0000

Python For Big Data. This term becomes viral inside big data industry. There are several programming languages and big data tools to analyze the raw data with different tactics. But, why python is creating a hype in data analysis? Let us discuss the top 13 reasons why you should choose python for big data analysis

Introduction to Python:

Hope we all know Python - As by its official definition, it is an interpreted and general purpose programming language. Using python we can develop any kind of advanced desktop applications, web applications, websites, mobile apps and many more. Guido Van Rossum was the inventor of python. He has created python to overcome the flaws of ABC- The farmer programming language, developed by CWI(Centrum Wiskunde & Informatica), Netherlands. Python has several specialties like dynamic typing, dynamic binding in order to proceed with Rapid Application Development.

python provides better involvement in big data analytics with advanced benefits, results, time efficiency and ease of access than any other languages like R, Java and more.

Why Choose Python For Big Data?

Choosing python in big data analysis is highly project specific, and meets the project goals on time without big huddles. The most unavoidable risk of big data is industry, "migrating the entire project to another language".

Python brings higher efficiency and provides us an option to easily migrate any big data or data science projects into the desired programming language at any time. Many developers and experts point out that the Python is the most suitable programming language for technology projects like AI, IOT and more.

Python is not only favoring the developers alone, but also favoring businesses in terms of fulfilling the project goals on time. Likewise, we can list out N number of powerful use cases and benefits of python in big data.

Here is the top 13 benefits while using python for big data in detail below.

13 Reasons To Choose Python For Big Data Projects

Open source language
Multiple Library support
Unbelievable speed of processing
Scope in Various Platform
data processing support
Powerful Packages
Lesser codes
Increased Compatibility with Hadoop
Easy to Learn
Flexibility and Scalability
Support from a large community
Data Visualization
Dynamic data processing

Let us discuss each in detail here : Top 13 Reasons Why You Should Choose Python For Big Data

Big Data Tools 2019 That Every Developer Must Know

Caroline Richards — Mon, 17 Jun 2019 10:48:11 +0000

Every developers must know this big data tools 2019. Get a detailed knowledge and overall glimpse about the trending big data tools this year.

The following tools and their descriptions are referred from the original article "Top 20 Big Data Tools 2019"

Top 20 Big Data tools

1. Apache Hadoop

It is a library framework that allows us to proceed distributed processing of large data sets across various cluster of computers. It can be scaled up to handle thousands of server machines. It can detect the failures and handle them at the application layer.

Features

Users can easily write and test on distributed systems.
It automatically distribute the data across the machines and can utilize the parallelism of CPU core.

2 Apache Spark

By the definition, it is a fast, open source, general purpose cluster computing framework. API’ can be developed in JAVA, Scala, R and python languages. This framework supports to process large sets of data across various clusters of computers. It can be scaled up to manage and support single servers to large server machines.

Spark can cover large amount of work loads like interactive queries, streaming, batch applications, algorithm iteratives and more. It can reduce the burden of managing multiple tools.

3 Apache Storm

It is an open source real time big data computation system and also free to use. It can process unbounded streams of data in a distributed real time.

4 Tableau

Table is the powerful tool ever, it helps to simplify the raw data into an easily understandable data sets. Tableau work nature can be easily understandable by professionals who are in any level of an organization. It connects and extract the data from various sources.

5 Apache Cassandra

Effective management of large set of data can be done by apache cassandra, without compromising the performance it can provide you scalability and high ability. Cassandra is fault tolerant, decentralized, Scalable, High performer.

6 Flink

It is also an another open source, distributed Big data tool that can stream process the data with no hassles.

7 Cloudera

Faster, easier and highly secure modern big data platform. It allows user to get data from any environment within a single and scalable platform.

8 HPCC

Developed by LexisNexis Risk Solution. It delivers data processing on a single platform with a single programming language support.

9 Qubole

It is an autonomous big data platform. Wll be self managed, self- optimized, it allows businesses to focus on better outcomes.

11 CouchDB

It is the only big data tool that stores data in JSON Documents, It provides distributed scaling with ultra fault tolerant. It allows data accessing through couch replication tool.

12 Pentaho

This big data tool can be used to extract, prepare and blend the data. It provides both visualization and analytics for a business.

13 Openrefine

Openrefine is also another big data tool , it can help us to work with a large amount of messy data.

14 Rapidminer

It is also an another open source big data tool. Which is used for data prep, machine learning, and data model deployments.

15 Data Cleaner

It is a Data quality analysis tool, inside the data cleaner there is a strong data profiling technique.

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