OVERVIEW

The developing interest and significance of data analytics have created numerous applications around the world. The top data analytics tools as increasingly open source, easier to understand and operate than the paid variant. There are many open source tools which don’t require much or any coding and convey preferable outcomes over paid variants for example – R programming in data mining and Tableau Public, Python in data visualization. In this white paper, I have created a rundown of twenty free, simple to-utilize, and incredible assets to help you with data extraction, analyzing and visualizing data, analyzing social networks and open source databases.

1. INTRODUCTION

Data analysis is the way understand data; to organize it effectively, clarifying it, making it respectable, and finding a conclusion from that information. It is a way to finding valuable information from large amounts of data and to use that information to settle on objective choices.

1.1 Data Analysis Methods

Data analysis methods are as follows:

i. Qualitative Analysis – It is done through interviews and observations.

ii. Quantitative Analysis - It is done through surveys and experiments.

1.2 Data Analytics Process

i. Data Analytics Process includes:

ii. Data Collection

iii. Working on data quality

iv. Building the model

v. Training model

vi. Running the model with full data

1.3 Difference between Data Analysis, Data Mining & Data Modeling

Data analysis is finished to discover answers to explicit inquiries. Data analytics techniques are like business investigation (analytics) and business knowledge (Intelligence).

Data Mining is tied in with finding the various examples in information. For this, different scientific and computational calculations are applied to information and new information will get produced.

Data Modeling is about how organizations sort out or deal with the information. Here, different approaches and systems are applied to information. Data analysis is required for data modeling.

2. DATA EXTRACTION TOOLS

2.1 What is Data Extraction?

In simple terms, data extraction is the process of extracting data captured within semi-structured and unstructured sources, such as emails, PDFs, PDF forms, text files, barcodes, and images. An enterprise-grade data extraction tool makes incoming business data from unstructured or semi-structured sources usable for analytics and reporting.

2.2 Types of Data Extraction Tools

Businesses, whether large or small, are leveraging different data extraction tools to scrape data and prepare it for business intelligence (BI) and analytics. Some of the common ones include:

On-Premise Data Extraction Tools: Such tools extract the incoming data from complex formats in either batches or real-time, validate it, and write it to the destination of choice.

Web Scraping Tools: Web scraping tools enable users to extract data from websites or web pages automatically and store the scraped data in a destination, such as a database, an Excel spreadsheet, etc.

Cloud-Based Tools: These tools leverage cloud computing to help a business extract data from different sources and ensure that structured data is made available for further processing or analysis

Types of Extraction Tools are as follows:

i. Octoparse

ii. Import.io

iii. PareseHub

iv. Content Grabber

(i) Octoparse

Octoparse is present day visual web data extraction programming. Both experienced and unpracticed clients would think that it’s simple to utilize Octoparse to mass concentrate data from sites, for the greater part of scratching errands no coding required. Octoparse makes it simpler and quicker for you to get information from the web without having you to code. It will consequently concentrate content from practically any site and enables you to spare it as spotless organized information in a configuration of your decision.

Features:

• Point-and-click interface
• Deal with almost all the websites - dynamic or static
• Extract data from sites precisely
• Store or save your data
• Cloud service (Paid editions)
• Ad Blocking technique feature helps you to extract data from Ad-heavy pages

(ii) Import.io

This web scraping tool helps you to form your datasets by importing the data from a specific web page and exporting the data to CSV. It allows you to integrate data into applications using APIs and webhooks.

Features:

• Easy interaction with web forms/logins
• Schedule data extraction
• You can store and access data by using Import.io cloud
• Gain insights with reports, charts, and visualizations
• Automate web interaction and workflows

(iii) PareseHub

ParseHub is a free web scraping tool. This advanced web scraper allows extracting data is as easy as clicking the data you need. It allows you to download your scraped data in any format for analysis.

Features:

• Clean text & HTML before downloading data
• The easy to use graphical interface
• Helps you to collect and store data on servers automatically

(iv) Content Grabber

The content grabber is a powerful big data solution for reliable web data extraction. It allows you to scale your organization. It offers easy to use features like visual point and clicks editor.

Features:

• Extract web data faster and faster way compares to other solution
• Help you to build web apps with the dedicated web API that allow you to execute web data directly from your website
• Helps you move between various platforms

3. OPEN SOURCE DATA TOOLS

3.1 What is Open Source Data Tools?

Open source tools is an expression used to mean a program - or device - that plays out a quite certain errand, where the source code is transparently distributed for use as well as alteration from its unique plan, complimentary. Open source tools are typically “created as a collaborative effort in which programmers improve upon the code and share the changes within the community, and is usually available at no charge under a license defined by the Open Source Initiative”.

3.2 Types of Open Source Data Tools

i. KNIME

ii. OpenRefine

iii. R-Programming

iv. RapidMiner

(i) KNIME

KNIME gives an open source data analysis tool. With the assistance of this tool, you can make data science applications and administrations.

It empowers you to fabricate AI models. For this, you can utilize propelled calculations like “deep learning, tree-based methods, and logistic regression”. Software provided by KNIME includes KNIME Analytics platform, KNIME Server, KNIME Extensions, and KNIME Integrations.

Features:

• Drag-and-drop facility
• No need for coding skills.
• It allows you to blend the tools from different domains like scripting in R and Python, connectors to Apache Spark, and machine learning.
• Guidance for building workflows.
• Multi-threaded data processing.
• In-memory processing.
• Data visualization through advanced charts.
• It allows you to customize charts as per your requirement.

(ii) OpenRefine

OpenRefine (formerly Google Refine) is free and open source data analysis software. It is a powerful tool to work with messy data: “cleaning, transforming, and dataset linking”. With its group features, you can normalize the data at ease.

Features:

• You will be able to work with large data sets easily.
• It allows you to link and extend the data using web services.
• For some services, you can upload the data to a central database through OpenRefine.
• You can clean and transform the data.
• It allows you to import CSV, TSV, XML, RDF, JSON, Google Spreadsheets, and Google Fusion Tables.
• You can export the data in TSV, CSV, HTML table, and Microsoft Excel.

(iii) R-Programming

R is a programming language. It gives a product domain to free. It is utilized for statistical computing and graphics. It tends to be utilized on Windows, Mac, and UNIX.

It will enable you to interface C, C++, and FORTRAN code. It supports object-oriented programming highlights. R is called as a interpreted language as guidelines are executed straightforwardly by numerous individuals of its usage.

Features:

• Provides linear and non-linear modeling techniques.
• Classification and Clustering
• It can be extended through functions and extensions.
• It can perform time-series analysis.
• Most of the standard functions are written in R language.

(iv) RapidMiner

RapidMiner is a software platform for data preparation, AI, deep learning, text mining, and predictive model deployment. It gives all information prep capacities.

The tool will help information researchers and examiners in improving their efficiency through mechanized AI. You won't need to compose the code, to do the information examination with the assistance of RapidMiner Radoop.

Features:

• Built-in security controls.
• Radoop eliminates the need to write the code.
• Visual workflow designer for Hadoop and Sparx
• Radoop enables you to use large datasets for training in Hadoop.
• Centralized workflow management.
• It provides support for Kerberos, Hadoop impersonation, and sentry/ranger.
• It groups the requests and reuses Spark containers for smart optimization of processes.
• Team Collaboration.

4. DATA VISUALIZATION TOOLS

4.1 What is Data Visualization?

Data visualization is the graphical portrayal of data and information. By utilizing visual components like charts, graphs, and maps, data visualization tools give an available method to see and get patterns, anomalies, and examples in information.

In the realm of Big Data, information perception devices and advancements are fundamental to dissect huge measures of data and settle on information driven choices.

4.2 Types of Data Visualization Tools

i. Tableau Public

ii. Google Fusion Tables

iii. Qlik

iv. Infogram

(i) Tableau Public

Tableau Public will assist you with creating charts, graphs, applications, dashboards, and maps. It enables you to share and distribute every one of your manifestations. It very well may be utilized on Windows and Mac working frameworks.

It gives answers for work area and server and has an online arrangement as well. Scene Online will enable you to associate with any information, from anyplace. Scene Public gives six items, which incorporate Tableau Desktop, Tableau Server, Tableau Online, Tableau Prep, Tableau Public, and Tableau Reader.

Features:

• It provides automatic phone and tablet layouts.
• It enables you to customize these layouts.
• You can create transparent filters, parameters, and highlighters.
• You can see the preview of the dashboard zones.
• It allows you to join datasets, based on location.
• With the help of Tableau Online, you can connect with cloud databases, Amazon Redshift, and Google BigQuery.
• Tableau Prep provides features like immediate results, which will allow you to directly select and edit the values.

(ii) Google Fusion Tables

It is a web application which will assist you with gathering, picture, and offer the data in information tables. It can work with enormous informational indexes. You can channel the information from a large number of lines. You can imagine the information through outlines, maps, and system charts.

Features:

• Automatically saves the data to Google Drive.
• You can search and view public fusion tables.
• Data tables can be uploaded from spreadsheets, CSV, and KML.
• Using Fusion Tables API, you can insert, update, and delete the data programmatically as well.
• Data can be exported in CSV or KML file formats.
• It allows you to publish your data and the published data will always show the real-time data values.

(iii) Qlik

Qlik is a self-served data analysis and visualization tool. The visualized dashboards, which help the company “understand” business performance at ease.

Features:

• drag-and-drop functionality
• smart search
• provides real-time analytics anytime and anywhere.

(iv) Infogram

Infogram provides over 35 interactive charts and more than 500 maps to help you visualize the data. Along with a variety of charts, including column, bar, pie, or word cloud, it is not hard to impress your audience with innovative infographics.

Features:

• One million images and icons
• Easy drag-and-drop editor
• Import your data with ease
• Interactive charts, maps and reports

5. SENTIMENT ANALYSIS TOOLS

5.1 What is Sentiment Analysis?

Sentiment analysis is logical mining of content which distinguishes and concentrates emotional data in source material, and helping a business to comprehend the social notion of their image, item or administration while checking on the web discussions.

In either case, picking the best assumption examination apparatus for your organization commonly incorporates thinking about the accompanying:

• Volume of material
• Test the software
•  Total features
• Pricing vs. assumed value

5.2 Types of Sentiment Analysis Tools

i. HubSpot's ServiceHub

ii. Semantria

iii. SAS sentiment analysis

iv. Trackur

(i) HubSpot's ServiceHub

It has a customer feedback tool which collects customer’s feedbacks and reviews. Then they analyze the languages using NLP to clarify the positive and negative intention. It visualizes the results with graphs and charts on the dashboards. Besides, you can connect HubSpot's ServiceHub to CRM system.

Features:

• Ticketing                                                      
• Knowledge base
• Customer feedback
• Live chat
• Conversations dashboard
• Conversational bots
• Automation & routing
• Email scheduling
• Email templates
• Email tracking & notifications
• Meeting scheduling

(ii) Semantria

Semantria is a tool that can collect posts, tweets, and comments from social media channels. It uses natural language processing to parse the text and analyzes customers' attitude. This way, companies can gain actionable insights and come up with better ideas to improve your products and service.

Features:

• Distributed, Scalable and Flexible
• Highly tunable NLP with broad language support

(iii) SAS sentiment analysis

SAS sentiment analysis is comprehensive software. For most challenging part of web text analysis is misspelling. SAS can proofread and conduct clustering analysis at ease. With its rule-based Natural Language Processing, SAS grades and categories the messages efficiently.

Features:

• Sophisticated mix of linguistics

(iv) Trackur

Trackur’s social media monitoring tool which can track the mentions from different sources. It scraps tons of webpages, including videos, blogs, forums, and images to search relevant messages. You can guard your reputation with its sophisticated functionality.

Features:

• Affordable Monitoring
• Powerful Tools 
• Trusted Experts

6. OPEN SOURCE DATABASE

6.1 What is Open Source Database?

The basic code of the database, similar to some other open source programming, is uninhibitedly visible, modifiable, or redistributable by any invested individual, rather than an exclusive one that is controlled under copyright laws. Instances of open source databases are MySQL, Firebird, and MaxDB. 

6.2 Types of Open Source Database

i. MariaDB

ii. PostgresSQL

iii. Airtable

iv. Improvado

(i) MariaDB

MariaDB is a drop-in replacement for MySQL. Uncertain about MySQL’s future with Oracle, many users have migrated to MariaDB. Support subscriptions are available from Mariadb.com.

Features:

• It includes a wide selection of storage engines, including high-performance storage engines, for working with other RDBMS data sources.
• It uses a standard and popular querying language.
• It runs on a number of operating systems and supports a wide variety of programming languages.
• It offers support for PHP, one of the most popular web development languages.
• It offers Galera cluster technology.
• MySQL, and eliminates/replaces features impacting performance negatively.

(ii) PostgresSQL

PostgresSQL has a strong reputation for reliability and data integrity. It’s feature-rich and is more robust and better performing than MySQL. The community edition is free.

Features:

• User-defined types
• Table inheritance
• Sophisticated locking mechanism
• Foreign key referential integrity
• Views, rules, subquery
• Nested transactions (savepoints)
• Multi-version concurrency control (MVCC)
• Asynchronous replication
• Native Microsoft Windows Server version
• Tablespaces
• Point-in-time recovery

(iii) Airtable

It is cloud-based database software that has extensive capabilities of a data table for capturing and information display. I also have a spreadsheet and built-in calendar to track task at ease. It is easy to get hands-on with its starter templates on lead management, bug tracking, and applicant tracking.

Features:

• Organize anything, with anyone, from anywhere
• Unique field types for your content
• Configure the perfect view
• Link related content intelligently
• Integrate with all your apps

(iv) Improvado

Improvado is a tool built for marketers to get all their data into one place, in real-time, with automated dashboards and reports. You can choose to view your data inside the Improvado dashboard or pipe it into a data warehouse or visualization tool of your choice like Tableau, Looker, Excel, etc. Brands, agencies, and universities all love using Improvado because it saves them thousands of hours of manual reporting time and millions of dollars in marketing.

Features:

• ROI tracking
• Website analytics
• Customer journey mapping
• Multi-touch attribution
• Cross-channel attribution
• Attribution modeling
• ETL - Extract / Transfer / Load
• Ad channels report
• Attribution models
• Cross-channel alerts
• Client portal

7. CONCLUSION

Big Data Analytics software is widely used in providing meaningful analysis of a large set of data. This software helps in finding current market trends, customer preferences, and other information. Every great data visualization starts with good and clean data. Most people believe that collecting big data would be a tough job, but it’s simply not true. There are thousands of free datasets available online, ready to be analyzed and visualized by anyone. In this article I listed 20 free and open source tools for data extraction, open source data tools, visualization data tools, social networks tools, and open source database tools.

ABOUT THE AUTHOR

Dr.S.Balakrishnan, (CSI Membership I1505405) is a Professor and Head, Department of Computer Science and Business Systems at Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India. He has 17 years of experience in teaching, research and administration. He has published over 15 books, 3 Book Chapters, 15 Technical articles in CSI Communications Magazine, 1 article in Electronics for You (EFY) magazine, 3 articles in Open Source for You Magazine and over 100 publications in highly cited Journals and Conferences. Some of his professional awards include: MTC Global Outstanding Researcher Award, Contributors Competition Winner July 2019 and August 2019, by DataScience Foundation, with cash prize of £100, 100 Inspiring Authors of India, Deloitte Innovation Award - Cash Prize Rs.10,000/- from Deloittee for Smart India Hackathon 2018, Patent Published Award, Impactful Author of the Year 2017-18. His research interests are Artificial Intelligence, Cloud Computing and IoT. He has delivered several guest lectures, seminars and chaired a session for various Conferences. He is serving as a Reviewer and Editorial Board Member of many reputed Journals and acted as Session chair and Technical Program Committee member of National conferences and International Conferences at Vietnam, China, America and Bangkok. He has published more than 10 Patents on IoT Applications.