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Big data technologies are changing the world: here’s how

Big data” is indeed massive in size. The term big is quite literal. And the amount of data we are discussing is impossible to handle and process by traditional means. Big data is a collection of all kinds of data. The huge volume and variety make the same demand for high-end processing and storage capabilities. Thanks to the recent developments in computer sciences and allied disciplines, humanity in 2022 wields the necessary power for storing and processing big data. And the power is granted by constantly evolving big data technologies. This article will discuss the same in detail and ensure that the reader is adequately enlightened.

Types of big data technologies

Apart from the storage, processing, and visualization capabilities, big data technology that is in use can be divided into two major groups. 

Operational technologies

Operational big data tech is concerned with real-time operations using big data. Operational technologies are interactive and are widely used by the masses.

  • Ticket bookings require a lot of data. This seemingly easy process involves extensive analysis of available seats and tickets. And assigning temporal tags with the tickets.
  • Online shopping in our times has evolved greatly. We not only can shop from home, but we can also shop better and in an optimized manner, thanks to big data-powered suggestions and engagement bots.
  • Social media like Facebook, WhatsApp, and Twitter provide a few essential data-driven services and can share anonymous data for research and development purposes. 
  • In big companies with a huge number of divisions and employees, managing office work involves utilizing big data. In this case, the same is an employee and divisional details.

Analytical technology

Analytical technology is concerned with the analysis of big data and not with the utilization of the same for day-to-day activities. Analytical big data technologies are used in the case of decision-making for commercial entities. And analyzing operational big data. A few examples are,

  • Stock market-related operations involve huge amounts of data and analytical big data technology.
  • Marketing and product development divisions are known for analytical big data tech.
  • Weather forecast and disaster management involve the analysis of huge amounts of data.
  • In healthcare, training diagnostic tools and personalized therapy developments involve analytical and visualization technologies.

Technological challenges in big data utilization

Big data utilization involves a few crucial steps that involve the collection, formatting, filtering, analysis, and visualization of analysis. Due to the gargantuan volume of big data, these steps involve possessing a lot of adeptness, processing, and storage capabilities.

Data accumulation and storage

Humans during their day-to-day activities generate a huge amount of data in 2022. And the same is ethically obtainable from a plethora of sources. Both for free and paid mediums. This data is unstructured and requires fast and efficient cloud computation for adept handling. To store this data an organization can, hire dedicated data storage services or maintain its warehouse for the same. Data efficient data collection and storage dedicated platforms are in heavy use. Hadoop, MongoDB, and Rainstor are the platforms for collection and storage. And for data mining, platforms like RapidMiner and Presto are popular among data scientists.

Data filtration and formatting

As we discussed earlier, big data is impossible to analyze by humans alone. Therefore, automated analytical tools are being used for making sense of the same. And these tools cannot perform at their optimal prowess without the right format of data sets. Therefore, the mined and stored data then structured and rendered into a format that is recognizable by the automated analysis tools.

Data analysis

Data analysis is a process that involves the right approach and the right point of view. The same data set can be used for many analytical purposes. After the same is decided, analytics tools are deployed to automatically analyze huge amounts of data.


Communication of analysis reports involves experience more than anything else. An experienced analyst is expected to communicate the analysis reports in lucid and easy language. So that involved parties can understand the same regardless of their standing with the discipline and backgrounds. Thus, visualization tools like Tableau and Plotly are among the most utilized big data technologies in the case of visualization.

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