Big Data

What is Big Data Technology?

Big Data technology is software introduced to extract, process, and analyze information from large and complex data set. Traditional data processing applications can’t deal with massive data set; that’s why these technologies are designed.

Big data technologies do a great job predicting future needs, reducing the risk factor, and making effective decisions. Let’s explore types of data technologies.

Types of Big Data Technologies:

They are classified into two types:

  1. Operational Big Data Technologies
  2. Analytical Big Data Technologies

Operational Big Data

This data normally generated from daily operations such as social media, online transactions, particular organizations, etc. It’s generally raw form data that is used further for Analytical Big Data Tech.

Here are some examples of Operation big data technologies:

  • Online shopping from Walmart, Amazon, Snap deal, Flipkart, and many others
  • Social media platforms like WhatsApp, Facebook, Twitter, and many other applications
  • Booking an online ticket such as Flight tickets, bus or rail tickets, movie tickets, etc.
  • Details of any employee belong to a multinational organization.

Analytical Big Data

The advanced version of Big Data is known as Analytical Big Data. It is complex as compared to the operation data. Different companies take operational data and analyze it to make some crucial business decisions and evaluate the company’s overall performance.

Examples of analytical data technologies are as follow:

  • Stock market
  • Weather forecast information
  • To carry space mission where every piece of information is important
  • Medical fields where the health status of the patient can be monitored

Trending Big Data Technologies

Now it’s time to figure out some leading technologies that affect the IT companies and market.

1.NoSQL Database

In order to design modern applications, NoSQL is offering a broad range of database technologies. They can be used for developing big data analysis and real-time web applications. NoSQL stores unstructured data that provides flexibility and high performance even we are using different data types at a large scale. They have collections and documents instead of tables, columns, rows, and relations. Examples of these databases are Redis, MongoDB, and Cassandra.

2.Artificial Intelligence

 AI deals with making smart machines based on human intelligence that can perform various tasks. As the word indicates, we develop intelligent machines by making computer algorithms based on human intelligence.

From self-driving car to automated robots, AI is developing very fast. We use deep learning and machine learning concepts to make it an automated product. AI is evolving continuously to provide benefits in different industries. For example, we can use AI for conducting surgery in Operation Theater, healing patients, drug treatment.

3.R Programming

R is free software and also a programming language. It is an open-source project used for data visualization, statistical computing, and a unified development environment.

R is a well-known programming language. Experts recommend it for statistical data analysis, data mining, and designing statistical application software.

4.Predictive Analysis

It is a subpart of big data analysis that uses existing data and tries to predict future behavior. It uses statistical modeling, data mining, mathematical models, and machine learning technologies to forecast future events.

Predictive analysis keeps you one step ahead of your competitors. Using predictive analysis tools, any industry can use existing data to estimate behaviors and trends of a particular time. You can also get feedback about your service from customers, suppliers, and stakeholders to reduce risk.

5.Data Lakes

In order to store both structured and unstructured data at any scale to a stable repository, data lakes are used. While collecting the data, it can be stored as it is without transforming into structured data. After that, different data analytics and data visualization techniques are executed for better business interventions.

It helps organizations get better opportunities, make better decisions, engage more customers, get more business growth, and sustain productivity. 

6.Blockchain

Blockchain is a database that provides features of secured data that means data won’t be changed or deleted once it gets written. Blockchain technologies are used to manage Bitcoin digital currency.

Blockchain technology is under development process. However, many large organizations like IBM, Microsoft, and Amazon Web Services have tried to build blockchain technology.

7.Apache Spark

Apache spark is the fastest tool used for big data transformation. It has many built-in features such as machine learning, SQL, graph processing support, and streaming.

It supports many well-known languages such as Python, Java, R, and Scala. The processing speed of spark is a hundred times faster than MapReduce.

Benefits of Big Data Processing

Businesses can analyze the data and make future strategies accordingly. Big data technologies took the place of traditional feedback systems. These systems help to identify any future risks and improve customer services. Businesses can track and monitor their performances and can drive innovation.

Conclusion

We hope this blog provides detailed information about big data technologies. With the development of new technologies, the ecosystem of big data is also emerging. We also explained trending data technologies that help businesses to track and improve their performances.

See Also
What is Database Management System?
What is a Virtual Server?