What Is Big Data? 10 Most Popular Big Data Tools

What Is Big Data?

Big data refers to the large volume of structured and unstructured data that is generated and collected at a rapid rate, making it difficult to process using traditional data processing tools. These large data sets can come from various sources such as social media, sensor data, and transaction records. The data is analyzed to uncover insights and make better decisions.

Big data generally includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data “size” is a constantly moving target, as of now a data set is considered big data if it ranges from a few dozen terabytes to many petabytes of data. The three main characteristics of big data are volume, velocity, and variety.

Volume refers to the amount of data generated, which can be in petabytes or exabytes. This data can come from various sources such as social media, sensor data, and transaction records, and it can be structured or unstructured.

Velocity refers to the speed at which the data is generated and needs to be processed. This data is generated in real-time, and it needs to be analyzed and processed quickly to be useful.

Variety refers to the different types of data that are generated, such as text, images, audio, and video. This data can be structured, semi-structured, or unstructured, and it requires specialized tools and techniques to process and analyze.

Big data is used in various industries such as finance, healthcare, retail, and transportation to gain insights and make better decisions. Advanced analytics, such as machine learning and artificial intelligence, are often used to analyze big data to uncover hidden patterns, trends, and insights.

Some examples of big data

  1. Social media data, such as tweets, Facebook posts, and Instagram photos, which can provide insights into consumer sentiment and behavior.
  2. Sensor data, such as data collected from IoT devices, which can provide insights into the performance of equipment and the condition of the environment.
  3. Financial data, such as stock prices and trading volumes, which can provide insights into market trends and investment opportunities.
  4. Healthcare data, such as electronic medical records and genomics data, which can provide insights into patient health and help with the development of new treatments.
  5. Retail data, such as sales data and customer purchase history, which can provide insights into consumer buying behavior and help with inventory management.
  6. Transportation data, such as GPS data from vehicles and traffic data, which can provide insights into traffic patterns and help with route optimization.
  7. Log data from web servers, which can provide insights into user behavior and help with website optimization.
  8. Genomic data, which can provide insights into genetic predisposition to disease and help with personalized medicine.

These are just a few examples of the many sources of big data that are being generated and collected today. The insights that can be gained from big data can be used to improve efficiency, optimize operations, and drive business growth.

Types Of Big Data

  1. Structured data: This type of data is organized in a specific format, such as in a relational database. Examples of structured data include financial transactions, customer records, and sensor data.
  2. Semi-structured data: This type of data has some structure to it, but not as much as structured data. Examples of semi-structured data include email, social media posts, and log files.
  3. Unstructured data: This type of data has no predefined structure and can come in various forms such as text, images, audio, and video. Examples of unstructured data include images, videos, audio, and text documents.
  4. Streaming data: This type of data is generated and processed in real-time, and requires specialized tools and techniques to process and analyze. Examples of streaming data include social media data, sensor data, and financial market data.
  5. Dark data: This type of data is data that an organization collects, processes, and stores, but never uses. Dark data can be unstructured and can be found in various forms such as emails, social media posts, and log files.
  6. Public Data: This type of data is generated by government organizations, research institutions and other entities that make data available to the public. Public data can be used for research, and to improve public services.

Each of these types of data has its own unique characteristics, and requires different tools and techniques to process and analyze. Understanding the different types of big data can help organizations make better decisions about how to manage, store, and analyze their data.

Advantages of Big Data

Big data processing has several advantages, including:

  1. Improved decision-making: By analyzing large amounts of data, organizations can uncover insights and patterns that would not be visible with traditional methods. This can lead to better decision-making and strategic planning.
  2. Increased efficiency: Big data processing can help organizations identify inefficiencies and optimize operations. For example, it can help with inventory management, supply chain optimization, and identifying and preventing fraud.
  3. New product development: Big data can be used to gain insights into consumer behavior, which can be used to develop new products and services.
  4. Personalization: Big data can be used to create personalized experiences for customers, such as personalized marketing campaigns, and recommendations for products and services.
  5. Cost savings: By identifying inefficiencies and optimizing operations, big data processing can help organizations save money.
  6. Fraud detection: Big data can be used to detect fraudulent activity, such as credit card fraud or insurance claims fraud.
  7. Predictive Maintenance: Big data can be used to predict when equipment is likely to fail, allowing organizations to schedule maintenance, reduce downtime, and increase efficiency.
  8. Predictive modeling: Big data can be used to build predictive models that can help organizations make predictions about future events, such as sales, customer behavior, and more.

Overall, big data processing can provide organizations with valuable insights and help them make better decisions, improve efficiency, and drive growth.

Top Big Data tools and software

#1 Apache Hadoop

Apache Hadoop is an open-source software that enables the distribution of large data sets across multiple computer clusters utilizing an easy-to-use programming interface.

  • Features:
    • Distributed storage and processing of large data sets
    • Scalability, as the system can be easily expanded by adding new nodes
    • Fault tolerance, as data is replicated across nodes
    • Support for a wide range of data formats and storage systems
    • High data throughput
    • Integration with other big data tools, such as Apache Spark and Apache Hive

Apache Hadoop Website

#2 Apache Spark

Apache Spark is an open-source, distributed computing system that can process large data sets quickly.

  • Features:
    • In-memory data processing for fast analysis
    • Capability to handle diverse types of data formats and storage systems.
    • Support for SQL, streaming, and machine learning
    • Integration with other big data tools, such as Apache Hadoop and Apache Kafka
    • Can run on a cluster or a single machine
    • High-level APIs for Java, Python, and Scala

Apache Spark Website

#3 Apache Kafka

Apache Kafka is an open-source, distributed event streaming platform that can handle high volume, high throughput, and low latency data streams.

  • Features:
    • High-throughput, fault-tolerant data streaming
    • Support for real-time data processing
    • Scalability, as the system can be easily expanded by adding new nodes
    • Support for a wide range of data formats and storage systems
    • Integration with other big data tools, such as Apache Storm and Apache Hadoop

Apache Kafka Website

#4 Elasticsearch

Elasticsearch is a search engine based on the Lucene library, which can be used for full-text search, performance analysis and logging.

  • Features:
    • Real-time search and analytics
    • Scalability, as the system can be easily expanded by adding new nodes
    • Capability to handle diverse types of data formats and storage systems.
    • Advanced search functionality, including faceted search and geospatial search
    • Integration with other big data tools, such as Logstash and Kibana

Elasticsearch Website

#5 Tableau

Tableau is a business intelligence and data visualization software that can connect to a wide range of data sources and create interactive visualizations and dashboards.

  • Features:
    • Drag-and-drop interface for creating visualizations
    • Support for a wide range of data sources, including big data platforms
    • Interactivity and collaboration features, such as the ability to share visualizations and dashboards
    • Advanced analytics, such as forecasting and statistical modeling
    • Integration with other big data tools, such as R and Python

Tableau Website

#6 Apache Storm

Apache Storm is a real-time, distributed computing system that can process streams of data in real-time.

  • Features:
    • Real-time data processing
    • Scalability, as the system can be easily expanded by adding new nodes
    • Capability to handle diverse types of data formats and storage systems.
    • Support for multiple programming languages, including Java, Python, and Ruby
    • Integration with other big data tools, such as Apache Kafka and Apache Hadoop

Apache Storm Website

#7 Cloudera

Cloudera is a distribution of Apache Hadoop that includes additional tools and services for big data management and analysis.

  • Features:
    • Distributed storage and processing of large data sets
    • Scalability, as the system can be easily expanded by adding new nodes
    • Capability to handle diverse types of data formats and storage systems.
    • Advanced analytics, such as machine learning and SQL
    • Integration with other big data tools, such as Apache Spark and Apache Kafka
    • Available as both open-source and enterprise versions

Cloudera Website

#8 MongoDB

MongoDB is a NoSQL document-oriented database that can handle large amounts of unstructured data.

  • Features:
    • Support for JSON-like documents
    • Support for horizontal scaling
    • Support for rich query language
    • Support for real-time analytics
    • Integration with other big data tools, such as Apache Spark and Apache Hadoop
    • Available as both open-source and enterprise versions

MongoDB Website

#9 Databricks

Databricks is a cloud-based platform for data engineering, machine learning, and analytics.

  • Features:
    • Support for Apache Spark
    • Scalability, as the system can be easily expanded by adding new nodes
    • Capability to handle diverse types of data formats and storage systems
    • Advanced analytics, such as machine learning and SQL
    • Integration with other big data tools, such as Apache Kafka and Elasticsearch
    • Available as both open-source and enterprise versions

Databricks Website

#10 Talend

Talend is a big data integration tool that allows for the integration and management of big data from various sources.

  • Features:
    • Capability to handle diverse types of data formats and storage systems
    • Support for multiple programming languages, including Java, Python, and Ruby
    • Support for real-time data processing
    • Support for data quality and data governance
    • Integration with other big data tools, such as Apache Hadoop, Apache Spark, and MongoDB
    • Available as both open-source and enterprise versions

Talend Website

These are some of the most popular big data tools and software currently available, but there are many other options as well. It’s worth noting that many of these tools have specific use cases and it’s important to pick the right tool for the job.

A Brief Overview On Data Management Platforms

With the growing complexity of digital media and the increasing creation and collection of user data, agencies, publishers, and markers need to find more effective ways of managing, selling, and buying audience information beyond standard analytic tools. But how can you capture the potentially valuable data, transform it into actionable insights, and achieve the desired outcome?

The answer is through the use of data management platforms. A DMP is essentially a unifying platform used to activate, organize, and collect audience data from various sources, including mobile, online, offline, and more. It’s the foundation of data-driven advertising and enables businesses to acquire unique consumer insights to draw and connect with prospects.

Big data is undoubtedly instrumental for effective digital marketing campaigns, but raw information can’t be used unless it’s transformed into usable forms. And this is why a data management platform is essential.

How does a DMP work?

Digital management platforms are generally used to gather unstructured data through different sources, such as social media, mobile apps, web analytics, television, and other channels. Therefore, all true DMPs must be able to collect user data deeper than the surface level, going beyond keyword information and URL to gather essential information.

Once the desired data is gathered, it’s then organized into segments referred to as hierarchies, which can and will change depending on the business model of the end-user. Sizeable publisher networks may have these hierarchies divided into buckets derived from every website they own. Agencies can have individual accounts for their different advertiser clients. Marketers can also manage other data separately but still have a holistic view.

When properly organized, data can help businesses understand their audiences, create more effective requests for proposal responses, enrich the target market, and extend your reach to meet campaign commitments. In other words, all data is gathered in a single place for easy and quick understanding of the intended audience, what content they’ll respond to favorably, and how to connect with them.

  • Organization. The DMP organizes the collected audience data and puts them into taxonomies and categories specified by the end-user, who defines how to organize the information. This means that the end-user needs to define and understand what they’re looking to get out of the data before the platform’s deployment.
  • Audience building and segmenting. After the organization of the data, the information can be used to support a marketing campaign. A retailer, for example, might target a specific demographic of women from ages 14 to 34. At the same time, another might concentrate on males who shop for shoes frequently over the World Wide Web. Regardless of the market, audience segmentation can power campaigns driven by data.
  • Insight and activation. When all data is classified, it can be analyzed to identify consumer intent, trends, and patterns. With its integration with other platforms like open APIs, the data can be activated and used to guide actions.


It’s not surprising that DMPs are increasingly being used in business. After all, it can help generate and transform data to ensure that companies are able to make informed decisions that will help them draw in sales, increasing their chances of succeeding in the process.

Grow With Customer Data Analytics

Growing your website is critical to achieving your digital marketing objectives and expanding your business.

Luckily, technological advancements have made it simple to collect and analyze client data for better decision-making. Continue reading to learn about customer data analytics and how you can utilize it to build your website.

What Is the Definition of Customer Data Analytics?

Customer data analytics, usually known as customer analytics, is the act of gathering and analyzing customer data in order to make informed decisions. It gives answers to innumerable queries, and you may gain all the insights you want by monitoring the appropriate metrics based on your objectives. Some of the information you may gain from consumer analytics are listed below.

  • Users and revenue avenues that are the most profitable
  • Channels of the common customer journey
  • Clients are mostly driven by the channels
  • Customer retention, including where you lose consumers and why
  • Customer engagement and experience, such as the features that your customers prefer.

Customer data analytics findings may be used to drive business sales, product development, and marketing.

How to Use Customer Data Analytics to Grow Your Website

You may expand your website by employing the findings of consumer data analytics and making the required changes in the manner listed below.

1. Refresh Your Website

High website bounce rates may sometimes be attributed to the structure of your website, which delivers a terrible user experience. Customer analytics may help you understand why your customers leave pages as soon as they click on them or why they spend less time on your website. To expand your website, improve your website structure and encourage visitors to stay longer and explore your website.

When people stay on your sites for a longer period of time, search engines see that your website offers quality material that keeps users interested. One method to update your website structure is to make navigation easier and to guide the client’s journey toward your goals. These might entail registering for emails or purchasing your goods. Another option to clean up your website is to incorporate a compelling call-to-action related to the page, update your metadata, and improve page loading times.

2. Make Your Website Responsive to Mobile Devices

Customer analytics can reveal how frequently your customers utilize their mobile phones or mobile apps to access your website. It is important to note that search engines consider a website’s mobile-friendliness when ranking it, and those that are suitable with mobile devices receive higher ranks. According to statistics, mobile devices account for 54.8 percent of global website traffic. As a result, in order to reach a larger audience and increase traffic, you must modify your website for mobile devices.

Optimize your website for mobile devices and increase page loading rates to provide a superior experience. Data analytics tools can assist you in determining the loading speeds of each page on your website and making advice on how to improve them for mobile. You may also split your customer analytics to look only at mobile devices to acquire the data you need on how you should improve.

3. Add Relevant Keywords to Your Website

Keyword utilization is one of the most important factors of website growth. Customer analysis may assist you in identifying phrases that your customers use while searching for your products or services online. You may next investigate the keywords to learn more about them, such as how frequently people use them and how competitive they are.

Include the focus keyword in your heading, meta title, meta descriptions, and the body of your article to get the most out of them. By adding keywords to your website, you make it more searchable to internet searchers.

4. Segmentation of Customers

Customer analysis is observing your customer’s behavior, which provides you with knowledge about their interests and attributes. Rather than classifying your consumers solely on demographics, you may segment them based on their characteristics and interests. Once you’ve segmented your consumers, you may customize your website content to each of them. This enhances website traffic and visibility, as well as makes your material more shareable.

For example, you might concentrate on additional promotional content targeted at raising awareness and attracting new customers. Furthermore, if a specific portion of your client base is the most profitable, you may supply more material to them. In this manner, you provide greater value to both the website and your company as a whole.

Customer analytics, thanks to advances in technology, can assist you in growing your business by providing you with the information you require to drive more visitors and achieve more conversions. With the proper consumer data analytics solutions, you can gain the precise insights you need to make informed data-driven decisions.

How To Use Data Science In Business?

We all know that knowledge is power. That is, with the rise of the newest technology, data became the essential resource companies can have. Gaining insights through data science methods is extremely valuable for all businesses. Data science uses algorithms, scientific methods, and systems to take out information from data and use it to take and facilitate significant business decisions.

Taking an approach that is based on analytics, numbers and statistics can bring reasonable solutions that would not be possible without the use of data science. Because of the power data science holds, more and more businesses tend to use data science services to make crucial business decisions, improve relations with customers, optimize operations, and even train employees. Below, you will find possible benefits of data science in business:

The improved decision-making process

It is estimated that around 80% of data in the world is unstructured. This means that the majority of companies own huge volumes of information that needs to be analyzed so that insights from it can be gained. The data science tools can help you to create predictive business models that will stimulate risks and possibilities resulting from certain situations.

With the use of properly structured data, companies can decide which solutions are the most profitable for their companies. They can also adjust operational strategies according to predicted trends and conditions. Increased efficiency in the decision-making process positively contributes to a company’s overall performance and its position on the market.

Better products

Data science technologies enable the exploration of historical data, comparing your information with the competition, and detailed analysis of the market. It can also make recommendations on time and places when and where your products are the most likely to gain interest. This kind of insight helps improve and adjust your business processes according to the current situation.

Deep analytics and understanding of the market response to your products, services, and brand as such is the key to success in surpassing the competition. In order to become a leader in your industry, you should take a hard look at information about your product usage so that you can rethink a company’s business model and ensure that customers get the most out of your offered services. 

Efficient recruitment

Each company owner wants to have unique talents in their team. However, seeking the most talented professionals can be an exhausting challenge. Luckily, data science can make this process more accurate and quicker. Social media, corporate databases, and job sites provide companies with data points that can be used to find candidates who will fit the best into your organization.

Moreover, you can use data science tools to check the social media profiles of the chosen candidates. Such a solution can be used to check if all the information provided in the resume is consistent. The AI-based algorithms can also search for signs of activity inconsistent with the company’s policy, for example, racism. This is especially important for companies hiring remote workers and co-workers.

Staff training

Data science helps you to gain insights that can be of great help to your employees. Hard data, statistics, and facts are useful for your teams in finding a way to improve results and achieve more ambitious goals. Data holds a lot of information that may be crucial in the performance of daily tasks. If you make use of it, everyone will benefit from it: Both your organization and individual employees.

Finding your audience

Every day, we create around 2.5 billion GB of data. And the number is constantly growing. That is why the collection of information that is important for your business and customers can be a struggle. Each piece of data your company receives from customers via interaction on social media, email surveys, website lists, etc., should be analyzed to gain a better understanding of your consumer group.

Thanks to the information provided by customers, you can create data points that will help you to define and target your audience. This way, your products, services, and marketing strategies will be tailored to a particular group’s needs and preferences. Finding new correlations between different factors such as age and income will help you create offers and promotions dedicated to groups for which your products were not accessible before.

To sum up, data science in business will be beneficial for your enterprise in various ways, including the more efficient decision-making process, recruitment processes, marketing, and staff training.

Thanks to data science, all your business decisions will be backed up with actual information that enables companies to foster and grow. However, discovering data science opportunities by yourself can be challenging, especially if you lack the technological experience. That is why we recommend you find a reliable AI consulting company.

More information: https://addepto.com/data-science-consulting-services/

The Influence Of Big Data On Lending Practices

With the big issues that emerged during and after the financial crisis of 2008, there were problems with lending that led to many people turning to online lenders for loans. Many people didn’t trust the banks in the same ways they once did, and they wanted another option for loans for business or personal finances.

But how has big data influenced lending practices? Well, let’s take a closer look at this situation to find out.

Turn to Online Lenders

So many people were unhappy with how the banks handled situations in the past, but one thing makes people turn to online lenders: not having to go to a bank’s branch office. Many consumers don’t like having to talk to a lending officer at a bank and justify their business or personal needs. With the use of online lenders, potential loan recipients can handle all of the processes online without going into a local branch or seeing another human being in the process.

This makes them feel more comfortable, and investors who poured a lot of money into online lending found that this is a big payday for them. However, there are some setbacks to this switch. The biggest is that you have to be nearly perfect with your borrowing and credit history.

Issues with Online Lending

Though there seem to be many positives that come with a new boost of online lenders, this doesn’t mean that it is always the best option. With very expensive start-up costs and no good ways to get that money, banks tend to have the upper hand because they already have the capital and a reputation for handling loans.

Even with a tainted reputation after the financial crisis, many people typically feel better going into a bank to get a loan to ensure that the transaction is safe and secure. It also makes them feel like the meeting was official and that they will receive the money they need. Online transactions don’t come with that same confidence in customers.

Benefits of Online Lending

However, there are also some good things that come with it. One of the main benefits is how they use their big data. Online lenders use their big data to underwrite the higher credit risk in several ways that credit scores wouldn’t normally do. This allows them to better predict a default loan probability from information other than a credit score.

It also makes them able to take on customers that would normally not be able to get a loan from a traditional bank. Because of this, they have a large number of people who are taking out loans, which means they are making money off of interest from each of those loans. They can also use this data to help get their advertisements to people who would want their services.

This means that online lenders can gain a longer reach to entice those who would not usually think to go to a bank to get a loan, but would consider doing it online. This ensures that more people can get the business loan they need with less work on their part to boost credit score before they can start making money.

Differences with Big Data

As you can see, with big data being used to tell if the person looking for a new loan would default without using just their FICO score, this has made it possible for many more people to be able to get their businesses off the ground within less time.

With banks offering vague borrowing terms, which tend to lead to a longer process for receiving the money, many people were frustrated with the way that things were going. With the offering of online lenders being able to use big data to tell if someone was a good risk and being able to get you your money faster, it has changed the way that many businesses get new loans.

Plus, with many people still recovering from the crisis, there are some who have yet to boost their credit score high enough to qualify. However, getting the money they need to start their business, they could be in a much better financial place. Websites like Become.co, people can get the money they need for their business through an online platform that uses big data to calculate if you’re qualified.

Final Thoughts

Big data is not something that everyone understands, but in the technological world we live in today, there are ways to use it within lending practices. As we look through how the use of big data has influenced lending practices, online lending has become much more prevalent, but it has also caused banks to step up to keep up with these websites and offer better options for customers too.

So, the use of big data has had a direct and indirect influence on both traditional banks and online lenders, but with all signs pointing toward giving customers what they need, it all looks uphill from here.

What will you get in the Big Data and Hadoop Training Course?

The Big Data and Hadoop course will help you get a basic knowledge of various Big Data frameworks. You will also get hands-on practical experience which will help you convert the theoretical knowledge into practical competence. You will get to work on projects related to various sectors: government sectors, e-commerce businesses, and banking and financial sectors. Moreover, you will also get to learn about the methods that are used to extract relevant information from a large pool of data. 

You will be trained to use Hadoop frameworks such as Pig and Hive. Finally, you will also get to perform real-world analytics to get practical experience at the end of the training. Big Data and Hadoop Training is a great way to upgrade your skills. This course would be highly beneficial to you if you want to make a career in the field of data management.  

Why is Big Data and Hadoop important?

As the amount of data produced in a day is rising each day, the equipment that is used to process this data has to be powerful and efficient. Without good processing power, analysis, and understanding of big data would not be possible. Therefore, Hadoop by Apache is a great tool to deal with large amounts of data. Through the Hadoop frameworks, large volumes of data can easily be organized, structured, and understood. This data can then be used to provide significant insights which will in turn help in the growth of the business.  

What are the benefits of earning Big Data and Hadoop certification?

There are a lot of benefits to pursuing a Big Data and Hadoop course and earning the certification. 

  • Professionals who are trained and have the practical skills to handle big data are in demand these days. Companies want individuals who have practical skills and the qualifications to show for those skills. As every organization relies on data that is gathered from the customers to take business decisions, the growth of the company is dependent upon the accuracy of the insights provided by the data. Therefore, there is a huge demand for people who are trained to be data specialists. 
  • By completing this course, you will get a chance to enhance your career opportunities significantly. If you are a fresher, you will be able to secure a good job. If you are a working professional, you will improve your chances of moving up the ladder in your organization and being more successful in your career. This course is great for people who want to switch their fields while they are already working somewhere. The Big Data and Hadoop training will allow you to showcase your skills and work in an industry of your choosing. 
  • As a student, you can pursue this certification course during your studies. Recruiters are always looking for candidates who have done something different and unique from the rest of their competitors. Therefore, by pursuing this course, you can significantly improve your chances of getting hired by companies like Google, Cisco, and Microsoft.  
  • One of the best benefits of pursuing the Big Data and Hadoop course is the potential increase in your salary capacity. By completing this course and earning your certification, you can expect that you will be able to increase your earning potential. Your new skill set can help you get a promotion or secure a job. 

What are the career opportunities you can pursue after completing the Big Data and Hadoop Course?

After completing the Big Data and Hadoop course, you can pursue a career in any field you want. This course can help you secure a job in a good company such as Microsoft, Google, and Cisco. There would be endless career opportunities after completing this training. Companies can hire you as:

  • Data Architects
  • Data Scientists
  • Data Analysts
  • Developers
  • BI Analysts
  • BI Developers
  • SAS Developers
  • Consultants for Hadoop Projects
  • Software Engineers

What is unique about the Big Data and Hadoop Course?

To ensure that all the skills and training that you received in a theoretical manner are properly understood and implemented, you will be required to complete some projects which will ensure that you are ready to use the skills you learned. This course is worth it because not only do you get theoretical knowledge but you also get to try your skills on projects which are real. The projects offered in this course are unique and will help you develop your practical skills. Towards the end of the course, you will be asked to use the frameworks you learned to analyze different types of data:

  • Analysis of Aadhar: Aadhar is one of the biggest and the largest biometric databases all around the world. The amount of data that is stored in this database is enormous and has to be continuously deciphered, structured, and separated according to different parameters such as state of residence, age, etc. Through this project, you will be able to learn how to deal with large data sets. 
  • E-commerce Website based Analytics (Clickstream Analysis): Data is of paramount importance for e-commerce websites. They use clickstream analysis to record and analyze user data based on timestamp, destination URL, IP address of visitor, device information, web browser information, visitor identification number, referral page information among others. The Hadoop ecosystem is used to simplify the process and this project can help you understand the process of data analysis on e-commerce websites. 
  • Analyzing the Banking Sector (CITI Bank): Financial and Banking sector is one of the top sectors that use data analysis insights and Hadoop frameworks to gather important information that helps them make strategic business decisions. The CITI group of banks has adopted an approach that is completely based on the data that is collected. To ensure that the insights are accurate, and the decisions correct, they use frameworks by Hadoop to process their data. This project will familiarize you with the inner working of the Financial and Banking Sector and will allow you to learn the finer process of data analysis particularly in this sector.

Top Tools For Data Science

Data Science is related with extracting, manipulating, processing and generating predictions out of data. In order to perform these tasks, we need various statistical tools and programming languages. In this article, we are going to share some of the well known Data Science Tools used by Data Scientists to carry out their data operations. We will try to understand the main features of the tools, benefits they can provide.

Brief Introduction To Data Science

Data Science has emerged out as one of the most popular fields of computer world. Companies are hiring Data Scientists to help them gain insights about the market and to improve their products. Data Scientists work as decision makers and are largely responsible for analyzing and processing a large amount of unstructured and structured data. In order to do so, he requires various specially designed tools and programming languages for Data Science to perform the task in the way he wants. Data scientists uses these data science tools to analyze and generate predictions.

Top Data Science Tools

Here is the list of best data science tools that most of the data scientists used.

1. SAS

SAS is one of those data science tools which are specifically designed for heavy statistical operations. It is a closed source proprietary software that is used by large organizations to analyze data these days. SAS uses base SAS programming language which for performing statistical modeling. It is widely used by data science professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that a Data Scientist can use for modeling and organizing their huge data. It is highly reliable and has strong support from the company that is why it is highly expensive and is only used by larger industries. Also, SAS pales in comparison with some modern open-source tools. SAS has several libraries and packages but dome are not available in the base pack and can require an expensive upgradation.

2. Apache Spark

Apache Spark or simply Spark is an all-powerful tool with analytics engine and it is one of the most used Data Science tool around the globe. Spark is specifically designed to handle batch processing and Stream Processing. It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.

Spark does better than other Big Data Platforms in its ability to handle streaming data. This means that Spark can process real-time data as compared to other analytical tools that process only historical data in batches. Spark offers various APIs that are programmable in Python, Java, and R. But the most powerful conjunction of Spark is with Scala programming language which is based on Java Virtual Machine and is cross-platform in nature.

Spark is highly efficient in cluster management which makes it much better than Hadoop as the latter is only used for storage. It is this cluster management system that allows Spark to process application at a high speed.

3. BigML

It is another tool widely used by Data Science professionals. BigML provides a great and fully intractable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. It provides a standardized software using cloud computing for industry requirements. Through it, companies can use Machine Learning algorithms across various parts of their company. For example, it can use this one software across for sales forecasting, risk analytics, and product innovation. BigML specializes in predictive modeling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc.

BigML provides an easy to use web-interface using Rest APIs and you can create a free account or a premium account based on your data needs. It allows interactive visualizations of data and provides you with the ability to export visual charts on your mobile or IOT devices.

Furthermore, BigML comes with various automation methods that can help you to automate the tuning of hyperparameter models and even automate the workflow of reusable scripts.

4. D3.js

Well known “Javascript” is mainly used as a client-side scripting language. D3.js, a Javascript library allows you to make interactive and great visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser. Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.

You can combine this with CSS to create illustrious and transitory visualizations that will help you to implement customized graphs on web-pages. Overall, it can be a very useful tool for Data Scientists who are working on IOT based devices that require client-side interaction for visualization and data processing.


MATLAB is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modeling of data. MATLAB is most widely used in several scientific disciplines.

In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing. This makes it a very versatile tool for Data Scientists as they can tackle all the problems, from data cleaning and analysis to more advanced Deep Learning algorithms.

Furthermore, MATLAB’s easy integration for enterprise applications and embedded systems make it an ideal Data Science tool. It also helps in automating various tasks ranging from extraction of data to re-use of scripts for decision making. However, it suffers from the limitation of being a closed-source proprietary software.

6. Excel

Probably Excel the most widely used tool for Data Analysis. Microsoft developed Excel specially for spreadsheet calculations but today, it is also used for data processing, visualization, and complex calculations. Excel is a robust analytical tool for Data Science.

Excel comes with various predefined formulas, tables, filters etc. You can also create your own custom functions and formulas using Excel. Excel is not for calculating the huge amount of Data like other tools, but still an ideal choice for creating powerful data visualizations and spreadsheets. You can also connect SQL with Excel and can use it to manipulate and analyze your data. So many Data Scientists are using Excel for data manipulation as it provides an easy and intractable GUI environment to pre-process information easily.

Google Sheets: Google sheet is another example of great data analysis tool. Its almost like MS excel. It is very useful for day to day use. The main benefit of this tool that it is cloud based, free, it works across devices and there is also some add-on for it. For example this free leave tracker was made by Google Sheets. You can check your file online and can edit from anywhere you want which can’t be done by excel without a shared drive.

7. ggplot2

ggplot2 is an advanced software for data visualization for the R programming language. The developers created this tool to replace the native graphics package of R language. It uses powerful commands to create great illustrious visualizations. It is the widely used library that Data Scientists use for creating appealing visualizations from analyzed data.
Ggplot2 is part of tidyverse, a package in R that is designed for Data Science. One way in which ggplot2 is much better than the rest of the data visualizations is aesthetics. With ggplot2, Data Scientists can create customized visualizations in order to engage in enhanced storytelling. Using ggplot2, you can annotate your data in visualizations, add text labels to data points and boost intractability of your graphs. You can also create various styles of maps such as choropleths, cartograms, hexbins, etc. It is the most used data science tool.

8. Tableau

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive and appealing visualizations. It is focused on needs of industries working in the field of business intelligence. The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau has the ability to visualize geographical data and for plotting longitudes and latitudes in maps.

Along with creating visualizations, you can also use its analytics tool to analyze data. Tableau comes with an active community and you can share your findings on the online platform with other users. While Tableau is enterprise software, it comes with a free version called Tableau Public.

9. Jupyter

Project Jupyter is a IPython based open-source tool for helping developers in making open-source software and experiences interactive computing. Jupyter has support for multiple languages like Julia, Python, and R. It is one the best web-application tool used for writing live code, visualizations, and presentations. Jupyter is a widely popular tool that is designed to address the requirements of Data Science.

It is an interactable environment through which Data Scientists can perform all of their responsibilities. It is also a powerful tool for storytelling as various presentation features are present in it. Using Jupyter Notebooks, one can perform data cleaning, statistical computation, visualization and create predictive machine learning models. It is 100% open-source and is, therefore, free of cost. There is an online Jupyter environment called Collaboratory which runs on the cloud and stores the data in Google Drive.

10. Matplotlib

Matplotlib is a plotting and visualization library developed for Python. It is the most popular choice of data scientists for generating graphs with the analyzed data. It is mainly used for plotting complex graphs using simple lines of code. Using this, one can generate bar plots, histograms, scatterplots etc. Matplotlib has several essential modules. One of the most widely used modules is pyplot. It offers a MATLAB like an interface. Pyplot is also an open-source alternative to MATLAB’s graphic modules.

Matplotlib is a preferred tool for data visualizations and is used by Data Scientists over other contemporary tools. As a matter of fact, NASA used Matplotlib for illustrating data visualizations during the landing of Phoenix Spacecraft. It is also an ideal tool for beginners in learning data visualization with Python.

11. SolarWinds Loggly

SolarWinds Loggly is a cloud-based log aggregation to manage all your logs over a single web dashboard with ease. With the help of this tool, you can log more without wasting your time and resources.

You can get higher data volumes and retention rates at better TCO with this tool. Managing Loggly is simple and doesn’t require complex configuration. It also supports logs from a range of sources including Lucene, MongoDB, AWS Scripts, Fluentd, Hadoop and more.


Data science requires a vast variety of tools. The tools for data science are for analyzing data, creating aesthetic and interactive attractive visualizations and creating robust predictive models using machine learning algorithms. Most of the data science tools mentioned above, deliver complex data science operations in one place. This makes it easier for the user or data scientist to implement functionalities of data science without having to write their code from scratch.