As we come closer to the end of 2021, it is high time for the aspiring data analysts to know the best tools for performing data science tools and which tool they should pick up as a newcomer in data science.

I am sure you must have searched for the right tools at some point in your data science journey. Fortunately, there is an abundance of such tools on the internet. However, picking the right tool can be a tricky situation.

Let us know one thing; data science is a vast domain and each of its branches required handling of data in an interestingly unique way that makes data analysts / scientists confused. And if you are a business leader, you would come across crucial questions about the tools you and your company have a long-term impact.

In this article, I will try my utmost best to distinguish the tools according to their usage and strong points. So, let’s get the ball rolling.

Data Science Tools for Big Data

To truly grasp the meaning behind Big Data, we need to understand the three aspects which are associated with this important terminology. These are known as the 3 Vs of Big Data

  • Volume
  • Variety
  • Velocity       

Tools for Handling Volume

As the name suggests, it refers to the amount and scale of datasets at hand. To understand the scale of data, you need to understand that 90% of data in the world was created just around the last 2 years!

Over the decade, with the increase in data, technology has also become better. The decrease in the storage costs has also made it possible to store and collect data cheaply.

The volume of the data in front of you defines by itself that whether it qualifies as Big Data or not.

When we have data ranging from 1 GB to 10 GB, the traditional tools for managing such data are below:

  • Microsoft Excel – Microsoft Excel prevails as the easiest and the most popular tool for handling insignificant amounts of data. The maximum capacity to oversee the number of rows is just around 1 million and a worksheet can handle data having 16,380 columns. This ability is simply not enough to manage copious amounts of data.

Microsoft Access – It is a popular tool which is used as a cost-effective solution for data storage. Smaller databases of around 2GB can be overseen easily, however, when the amount starts to increase beyond that, it starts cracking up.

SQL – SQL is undoubtedly the most popular data storage technology that has been around since the mid 1970’s. It was the primary database solution for a few decades after that. SQL is still popular but there is a drawback about this technology – It becomes difficult to scale it as the database continues to grow beyond its handling ability.

These are a few of the basic tools that you should know and have a degree of familiarity and experience working on it. But, if the data in front of you is greater than 10 GB and goes all the way up to 10+ TB, you need to implement your analysis and solutions via the brilliant tools below:

  • Hadoop – It is an open-source distributed framework that manages not only storage, but also data processing of Big Data in many databases and other sources of datasets. You are more than likely to come across this tool whenever you build a Machine Learning project from scratch.

Hive – It is a data warehouse addon which is built on top of Hadoop which is described above. Hive provides functionality which has resemblance to SQL, and it is primarily used to query the Big Data stored in various databases and file systems that integrate with Hadoop.

Tools for Handling Variety

Variety refers to different types of data which are present in Big Data. The data types may also be structured, as well as unstructured.

Let’s go through some of the examples which come under the umbrella of structured and unstructured data

Please take some time to analyze these examples and think of some more examples from your real life.

As you might have noticed, structured data is present in a certain order and structure while unstructured data doesn’t have any predefined structure. Moreover, these types of data are huge and diverse.

Two most common solutions are SQL and NoSQL. SQL had market dominance until NoSQL entered the scene.

Some examples for SQL are Oracle, MySQL, while NoSQL consists of popular databases like MongoDB, Cassandra, etc. Interestingly, NoSQL is seeing high adaptation rate among the experienced data analysts due to the ability to scale and manage dynamic data.

Tools for Tackling Velocity

The third and final V stands for the velocity. This is the speed at which data is generated. This includes both the real-time and non-real-time data. We’ll be talking in depth about real-time data.

We have a lot of examples in our real world that capture and process real-time data.

Some examples of real-time data being collected are:

  • CCTV
  • Stock trading
  • Fraud detection for credit card transaction
  • Social Media

Now let’s discuss some of the most important tools that can handle real-time data

  • Apache Kafka – Kafka is an open-source tool from Apache. It is used for building real-time data pipelines related to the project. Kafka is fault-tolerant, quick, and is used within large organizations around the world.

Apache Storm – One wonderful thing about this tool is that it can be used by all major programming languages. It can process up to 1 million Tuples (rows) per second and it is also highly ascendable, which also makes it highly regarded as well.

Amazon Kinesis – This tool by Amazon is like Kafka but it comes under aa subscription cost. However, it is regarded as one of the polished and feature-rich tola out there.

Apache Flink – Flink is an added tool by Apache that we can use for real-time information. Some of the key features of this tool is that it has efficient memory management, high performance, and fault tolerance.

Widely Used Data Science Tools

If you are setting up a project, you will come across a lot of questions in your mind. This is valid according to your level – whether you are a Data Engineer, Data Analyst, Data Scientist, or in any other relevant position.

  • Which tools should I use in different domains within Data Science?
  • Should I opt for a subscription-based choice or a free open-source tool?

In this section, I will reveal the key tools which are used in different industry domains related to Data Science / Analysis.

Data Science spectrum is very vast, and it includes multiple functionalities and sections related to Data Science.

Let us briefly discuss the multiple points in the spectrum shown above

Reporting and Business Intelligence

Let us begin with the lower end of the spectrum. This function enables an organization to find trends and patterns to make businesses strategic decisions. These types of decisions range from MIS, data analytics, all the way to dashboarding.

The commonly used tools in these domains are:

  • Excel – One of the key tools for data analysis in Excel is pivot tables and charts. Due to the multiple functionalities this brilliant tool has, it is known as the Swiss knife of data science / analysis tools.
  • QlikView – It lets you search, join, wrangle, and visualize data in a few clicks. It is an easy and intuitive tool and that’s what makes it so popular.

Tableau – It is among the most popular data visualization tools today. It has a suite of applications, ranging from tools for ETL, visualization, collaboration, etc.

  • MicroStrategy – It is yet another tool that supports Dashboards, automated distributions, and other tasks.
  • Power BI – This is another popular reporting tool that comes under the ecosystem of mighty Microsoft. It was built to integrate with other Microsoft tools. So, if your organization has SQL server or SharePoint, it will be a fun way to do all your tasks.
  • Google Analytics – I know you might be wondering as to how Google made to this list? Well, digital marketing plays a key role in a project’s success. This is an excellent tool to analyze your digital marketing presence and effectiveness.

Predictive Analysis and Machine Learning Tools

Moving up the ladder, effectiveness of data in business and relevant complexity also increases as well for data analysts. This is the key domain from where bread and butter of data analysts come from. It involves working with statics, mathematics, algorithms, neural networks, and deep learning.

Most common tools are under:

  • Python – This is one of the most dominant programming languages as far as data science is concerned due to its ease of use, flexibility, and being an open-source language. It has gained acceptability and popularity in the ML community.
  • R – R is another popular tool used by data scientists. Unlike Python, R was developed only for analyzing and visualizing data through multiple statistical functions. Due to its little complexity, it is not as popular as Python in the industry today.

Apache Spark – Like Microsoft Excel, Apache Spark is also known as the Swiss Knife of big data analytics as it offers multiple advantages such as speed, flexibility, features, computational power, etc.

Julia – It is a new language and is considered as the successor of Python. It is still in its first stage, and it will be exciting to see how it works in the upcoming years.

Jupyter Notebooks – These notebooks are primarily used for Python, Julia, RStudio, etc.

Fortunately, the tools discussed above are free and are open source in nature. You can use, mold, and even change the configuration files as per your liking.

Now we will check out some of the subscription-based solutions which are available on the market and have been adapted by large corporations worldwide.

  • SAS – It is extremely popular software used in banks and other financial organizations. It has an extremely high share in organizations like American Express, JP Morgan, Royal Bank of Scotland, etc.

SPSS – It’s a short for Statistical Package for Social Sciences. SPSS was acquired by IBM in 2009. It offers advanced statistical analysis, a vast library of algorithms related to different branches of sciences.

MATLAB – MATLAB is a very underrated tool which is majorly used in academia and research divisions in organizations. Although it has lost quite a large ground when compared to Python, R, and SAS but still it is taught in many universities around the world.

Common Frameworks for Deep Learning

Deep learning requires high computational power and needs specially designed frameworks to use those resources effectively and efficiently. Due to these requirements, you need a good GPU or a TPU.

Let’s look at some of these frameworks:

  • TensorFlow – It is easily the most popular and used framework in the world today.
  • PyTorch – This is another competitive framework which is giving tough competition to TensorFlow. Facebook developed it.
  • Karas and Caffe are some of the other frameworks which are in use for deep learning applications.

Artificial Intelligence Tools

The era of Auto Machine Learning is upon us. If you haven’t heard about it, then it’s the right time to not only familiarize yourself with the tools and related literature related to it, but also have a somewhat command over a couple of tools in 2022.

Some of the most popular tools are AutoKeras, IBM Watson, Google Cloud AutoML, Amazon’s Lex, etc. AutoML is expected to be the next important thing in the data science world because it aims to cut the technicalities so that businesses can take effective decisions easily.


We have discussed only a small part of tools, and data collection engines in use for retrieval, processing, and storage of data. Data science consists of a large spectrum of domains and each domain has its own methodologies and tools to go along with it.

Picking up a set of tools and methodologies is often dependent on domain, project, and of course, your organization.


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