Processing Huge Data sets using Big Data

Processing Huge Data sets using Big Data

More and more companies, such as banks or retail, government agencies, or public institutions, benefit from analyzing large data sets. A systematic approach to Big Data enables companies to use the power of data effectively. In this article, we briefly explained Big Data processing and listed some tools for handling large amounts of data.

Big Data

Every day, each of us discloses our data unconsciously. For example, when visiting a website, paying with a card, or buying in an online or in-store shop. All this information forms a set called big data.

The term Big Data refers to the collection of structured, semi-structured, and unstructured data. This data can then be processed and used in ML, predictive analytics, and other advanced data analytics applications. As a result, companies gain new insights based on which they can make better decisions.

What is big data processing?

A big data processing strategy is a collection of techniques enabling access to enormous amounts of information.


The first stage of big data is ETL processing. It is a process of:

  • Extracting: This is the process of getting data from the source system to the staging area. You can collect data from various sources like web pages, enterprise applications, or marketing tools.
  • Transformation: As the data is raw, it is necessary to convert it to the required formats for later storage. After transformation, data is understandable to the user, and business operations become more efficient.
  • Loading: This is the process of loading the desired output to the target data warehouse. You can load data either in real-time or in scheduled batches.

Visualization is the next stage in the big data processing. It is necessary for effective data analysis and decision-making based on it. It allows companies to learn and understand patterns and relationships. In addition, it helps organizations spot emerging trends.

Machine learning is a very important stage of Big Data processing. All because learning algorithms allow you to analyze large amounts of data faster. We can distinguish the following types of ML: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Big data technologies


Hadoop is an open-source software framework for storing and processing data in a distributed computing environment with commodity hardware and a simple program execution model. It provides, among others, mass storage of any type of data and tremendous computing power. Hadoop can be used to store and analyze data present on various machines with high capacity, speed, and low cost.


Apache Cassandra is an example of a NoSQL database that uses a distributed and fault-tolerant architecture. This open-source system provides an effective organization of large data across multiple servers. In addition, the system will continue to function even if one of the servers goes down.


Apache Spark is an open-source, general-purpose cluster computing platform equipped with:

  • In-memory functions for processing Big Data sets,
  • APIs for the following programming languages: Scala, Python, Java, and R.

The multi-stage processing used in the Spark system is based on in-memory technology. This allows you to perform most of the calculations directly in the operating memory. In addition, the API is simple to use and supports large datasets for quick analytical queries.


It is the fastest and most powerful data visualization tool that is used in the Business Intelligence domain. Visualizations in Tableau are created in the form of worksheets and dashboards. It gives rich possibilities for data analysis, visualization, and presentation, instantly integrating with any data source, thus offering better use of data.


Big Data processing is a popular technology used by many industries. It allows, above all, to forecast trends, monitor processes, or make better decisions. Moreover, Big Data tools play a significant role in processing huge amounts of data. They help organizations process huge amounts of information quickly and efficiently.

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About author

Brandi is a news publisher at FCT. But she doesn't just write news stories. She also makes free custom templates for websites and blogs, designs graphics, and helps her fellow website designers with CSS coder issues.