What Are the Tools and Techniques in Big Data?

Posted September 15th, 2021 in Misc. Tagged: .

Data is the new oil. It’s a phrase we’ve heard a lot in recent years, and it’s not hard to understand why. We’re generating more data every day than ever before, and companies are scrambling to find ways to store that information without running out of space. The rise of big data has led to the need for new tools and techniques designed specifically for handling large amounts of storage in mind; this blog post will cover some of those features and how they can help your business succeed!

Big Data

What is big data?

Big data is the collection of large, complex data sets that you can analyze to extract meaningful information to assist in decision-making. The term “big data” first appeared in a 1998 article written by industry analysts Doug Laney and Allen Koehne, who defined it as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Big data is an extension of the three Vs. of big data: Volume (amount), Velocity (speed), and Variety (type). However, some would argue that this definition has evolved into four additional V’s: Veracity (quality), Validity (correctness), Value (utility), and Visibility (findability).

Big data is typically analyzed using distributed systems and database management systems (DBMS). Businesses can extract value from the information stored in big data using these technologies as per RemoteDBA.

Examining huge volumes of information might be really difficult for most associations from various industry verticals. Huge information investigation can assist organizations with getting and valuable experiences from the present enormous broadened information sources. Cloud applications, online media, and machine sensor information are a couple of models. The idea of huge information has been around since the most recent couple of years and little and enormous organizations have effectively taken on cutting edge huge information investigation to uncover bits of knowledge and patterns to gain a competitive advantage.

Information delivered by associations have a particular design. Organizations need to put together the information to use it.

Big data analytics include assortment, association, and breaking down huge arrangements of information to extricate different sorts of valuable data from it. This cutting-edge innovation assists experts with recognizing various examples of information and comprehend the data contained inside it. This assists associations with settling on better choices.

In big data, there are many tools and techniques that you can use. There can be a large number of datasets or sources in a real-time environment. Popularly three types of tools are used – ETL, Machine Learning, and Visualization toolkits. These methods help us to get useful insight from the dataset or source.

Tools and Techniques

Here are some of the tools and techniques used in big data.


Extract Transform Load is an approach for populating Data Warehouses with data from various sources such as transactional systems (OLTP), operational data stores (ODS), and other databases according to business requirements. It can also transform this data into the required structure for Data Warehouse (DW). The tools for the ETL process include Informatica Powercenter, Talend Open Studio, etc.

Machine learning

This deals with tools that one can use to build models from the given datasets and get insights. These tools include R, Python, etc.

Machine learning incorporates programming that can gain from information. It enables PCs to learn without being expressly customized, and is centered around making expectations dependent on realized properties gained from sets of “preparing information”.

Visualization toolkits

Visualization is an image representation for a dataset that helps us explore more about it. Here, we also use various techniques, including BI tools such as Tableau, QlikSense, etc.


This deals with the process of classifying a dataset into different categories based on the features available in it. The classification algorithm provides the correct output and builds models based on this data. It also uses relationships among attributes in datasets to predict results. The classification process has both supervised and unsupervised learning. Some of the algorithms dealing with classification are Naive Bayes Classifier (NBC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), etc.


These tools are helpful to cluster a set of data into groups based on its similarities. The clustering process is unsupervised and focuses more on discovering underlying patterns in the dataset, which helps us extract results from it. There are different types of clustering techniques like K-Means, spectral clustering, etc.


Regression deals with finding relationships between variables using algorithms. After establishing these relationships, you can fit them into regression models to help forecast future values or make predictions. Linear regression is an example of a simple form of regression, whereas multiple regression is one with many independent variables. Some popular algorithms for regression are Ordinary Least Squares (OLS), Ridge Regression, etc.

At an essential level, regression analysis includes controlling some autonomous variable (for example ambient sound) to perceive what it means for a reliant variable (for example time spent available). It depicts how the worth of a reliant variable changes when the autonomous variable is differed. It works best with ceaseless quantitative information like weight, speed or age.

Recommender system

Recommenders systems provide a list of recommendations for users at their demand. It is used in various domains and provides results like products, movies, songs, etc. The collaborative filtering approach is the most commonly used technique with recommender systems. Other techniques involve content-based filtering and social-based approaches. Some examples of tools include Amazon’s product suggestions, movie suggestion engines like Inpixio, etc.


This is a way to present insights from data interactively and intuitively through different graphs and charts that help the users understand it easily without any technicality involved. There are many tools available for this process, such as Chartio and RShiny.

Visual analytics

Visual Analytics is a process to provide results in the form of visual representations, which helps users understand the results easily. There are many tools available for this process, including the formentioned Tableau, etc. These methods are more focused on delivering interactive insights for non-expert audiences.

Stream Processing

This process deals with data streams that one can use in a real-time environment. Stream processing tools work with large volumes of data and include online analytical processing (OLAP) techniques like MapReduce, CEP, etc. Some popular stream processing platforms include Apache Storm, Apache Samza.

Big Data Ecosystems

Nowadays, these ecosystems also play an important role in big data applications. Ecosystems include platforms for analytics, visualization and BI tools, etc. Here we consider multiple together for building solutions rather than considering them in isolation.

As we can see from the above list of tools and techniques, there are many to choose from when it comes to building big data solutions. We have some popular tools like R and Python, which are widely used. Still, there is a massive demand for new emerging technologies such as deep learning and machine learning algorithms having higher accuracy.

Wrapping things up

With all the tools and techniques available to marketers, you need to understand that big data is not a magic bullet. It takes work on your part to create goals, identify what metrics will be most useful in measuring those goals, determine which of these tools and techniques are best suited for achieving those goals with an analysis plan tailored specifically for your business objectives.

The key takeaway here is that don’t think of big data as something you plug into Excel or Google Analytics and start crunching numbers; instead, consider how specific analytics types can help make more informed marketing decisions.

About the Author

Maria Jones

Maria Jones is a Business Analyst. She shared her tips with friends. She is passionate about new technology.

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