What is Big Data Technology? Why is it important?

In simple terms, Bigdata is a phrase that refers to a large collection of data that continues to increase rapidly over time. Examples of Big Data analytics include financial exchanges, social media platforms, and jet engines, among others.

“Big Data Technology” is a software tool that analyzes, processes, and interprets vast amounts of structured and unstructured data that cannot be processed manually or traditionally. This aids in the formation of future conclusions and projections, allowing numerous hazards to be avoided.

Operational and analytical big data technologies are two types of big data technologies.

Daily activities such as online transactions, social media interactions, and so on are dealt with by operational technology, whereas analytical technology deals with the stock market, weather forecasts, scientific computations, and so on.

Big data technology is used for data storage and mining, visualization, and analytics.

It can be classified into 3 types:

  1. Structured Data
  2. Unstructured Data
  3. Semi-structured Data


Does that seem to be too much to grasp? Continue reading in order to understand more.

The Top 5 Big Data Technologies

Hadoop Ecosystem

In a distributed data processing environment, the Hadoop Framework was created to store and process data using a simple programming style. It is possible to store and analyze data from a variety of high-speed and low-cost equipment. In the last year, Hadoop as a Big Data Technology has been widely adopted by businesses for their data warehousing needs. The trend appears to continue and even accelerate the following year. Companies that have not yet evaluated Hadoop’s benefits and uses will most certainly do so.

Artificial Intelligence

Artificial intelligence (AI) is a vast field of computer science concerned with the creation of intelligent computers capable of carrying out activities that would normally require human intelligence. From Siri and Google Assistant to self-driving vehicles, AI is rapidly evolving. As a multidisciplinary discipline of research, it considers a variety of methodologies, such as expanded Machine Learning and Deep Learning, to create a significant change in the majority of tech businesses. Existing Big Data Technologies are being revolutionized by AI.

NoSQL Database

In the database, NoSQL encompasses a number of different big data technologies that were created to construct current applications. It depicts a non-SQL or non-relational database with a data gathering and recovery strategy. They are utilized in real-time Web and Big Data Analytics. It saves unstructured data and provides quicker performance and flexibility for a variety of data formats, including MongoDB, Redis, and Cassandra. In a variety of devices, it gives design integrity, better horizontal scaling, and control over possibilities. By default, it employs data structures that aren’t related to databases, which speeds up NoSQL computations. Every day, Facebook, Google, Twitter, and other comparable corporations keep gigabytes of consumer data.

R Programming

R is an open-source Big Data programming language and technology. The free program is frequently used for statistical computing, visualization, and support communication in unified development environments like Eclipse and Visual Studio. According to experts, it is the most widely spoken language on the planet. Data miners and statisticians utilize the system to build statistical software and, in particular, perform data analysis.

Data Lakes

In terms of structural and unstructured data, “Data Lakes” refer to a centralized repository for storing all data forms at all levels.

Without being turned into structured data, the data can be kept as is during data accumulation. It allows for real-time data analysis ranging from dashboards and data visualization to Big Data transformation for enhanced business insight.

Businesses that utilize Data Lakes keep ahead of the competition by using new log file sources, social media data, and click-streaming to do new analytics, such as Machine Learning.

Understanding and engaging customers, maintaining efficiency, active device management, and intuitive decision-making are some of the ways that Big Data technology helps businesses respond to greater company growth prospects.

 The Big Data landscape is always changing. The latest breakthroughs in Big Data Technologies are easily launched, and many of them will grow in response to the IT industry’s needs. These advances will guarantee that firms are able to grow in a harmonious manner.

To recap, Big Data is constantly on the growth, with increasing adoptions and uses of current Big Data technologies, as well as the introduction of fresh solutions in areas such as Big Data security, Cloud integrations, and data mining.

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