Latest Trends in Big Data Technologies

Big data refers to the collection of a large volume of data, both structured and unstructured, that submerges business daily. What the organization does with this data is more important as this data can be used for analysis which can be used for deriving insights that can help the company make better decisions and strategic moves.

Big data is quantified by the three Vs. Below are the following Vs.:

• Volume: The amount of data is what matters. A lot of high volumes and low-density data will be processed with big data.
• Velocity: The rate of the data receipt and the action taken upon it is known as Velocity. It’s a fast rate. The higher velocity data streams directly into the memory.
• Variety: Variety is the type of data available. The traditional types were structured data, but with the rise of big data, the new type is unstructured data. The unstructured and semi-structured data types need additional processing to derive meaning and support metadata.

The type of big data analyses:

• Operational big data technologies:

Operational Big Data technologies demonstrate the volume of information created each day like online exchanges, web-based media, or any data structure a specific organization utilized for analysis by the product dependent on huge information advances. It goes about as raw information to supply big data technology.

• Analytical big data technologies:

Analytical Big Data technologies concern the progressive change of big data technologies, which is convoluted than Operational Big Data. This classification incorporates accurate analysis of big data, which is crucial for business choices. Some examples are stock marketing, weather forecasting, time series, and medical record analysis.

Top 6 Big Data Technologies:

1. Hadoop Ecosystem:
   A simple programming model developed the Hadoop framework to store and process data in a distributed data processing environment. The data present on high-speed, low-expense machines can be stored and analysed. Enterprises have adopted Hadoop as Big Data Technologies for their data warehouse needs in the past year, and this trend seems to grow in the upcoming years as well.
2. Artificial intelligence:
   Artificial Intelligence is a computer technology that deals with the advancement of intelligent machines efficient to perform different tasks normally requiring human intelligence. AI has been progressing from apple’s Siri to self-driving cars. Machine learning (ML) and Deep Learning (DL) are the approaches that help in making a significant shift in tech industries. AI revolutionizes the existing Big Data Technologies.
3. NoSQL database:
   NoSQL includes a wide variety of different technologies in the database that are developed to design modern applications. A non-SQL or non-relational database provides a method for data acquisition and recovery. It keeps storage of unstructured data and helps in faster performing with flexibility while addressing various data types.
4. R Programming:
   R is an open-source big data technology that is more of a programming language used for development. Its free software is broadly used for statistical computing, visualizing, unified development environments. It has been the world’s leading Language, according to the experts. Data miners and statisticians also widely use this system to develop statistical software and mainly data analysis.
5. Data Lakes
   Data lakes mean a secure collection for storage of all data formats at all levels in terms of structural and unstructured data. Businesses using Data lakes stay ahead in the game from their competitors and carry out new analytics such as Machine Learning through new log file sources, data from social media, and click streaming. This Big Data technology helps enterprises respond better to business growth opportunities by understanding and engaging clients, sustaining productivity, active device maintenance, and familiar decision making to better business growth opportunities.
6. Blockchain:
   Blockchain is the Big Data technology that conveys an exceptional data-safe component in digital Bitcoin currency, so it isn’t erased or adjusted after the pack is written. To give some examples, it is a highly secured environment and an exceptional choice for various big data applications in different businesses like banking, finance, insurance, medical, and retail.

Future of big data technologies:

• Cloud solutions will power big data technologies:
  Data generation is on its rise, with IoT in the front seat. Applications with IoT require a perfect scalable solution for managing a massive volume of data. The advantages of Hadoop on the cloud are being realized by many organizations and technologies pertaining to big data technologies.
• Traditional database world will revolutionize:
  With the increase in unstructured data, companies have started realizing the potential of insights from this data. And to manage such data, the NoSQL database has been the best option until now and continues to be. The most popular NoSQL databases will continue to implement more vendors.
• Hadoop will rock:
  Hadoop will come up with features that will make it more enterprise-ready in terms of technical development. Once its security projects like Sentry, Rhino, etc. gain stability, its implementation will expand across many more sectors, and companies can use solutions without many securities concerns
• Realtime solutions will expand:
  All the companies by now have the data and know-how to store and process the big data. The real difference will be in how fast they can deliver the analytical solutions for better business decisions. The focus in 2021 is Speed. The processing abilities of big data technologies will undoubtedly increase.
• Self-service big data applications will continue to evolve:
  Bog data technologies that simplify data cleaning, data preparation, and data exploration tasks are expected to increase.

In conclusion, according to the leading Big Data Analytics Services providers; Big Data is a very crucial part of businesses today, and Big Data technologies help to navigate through the data and help derive conclusions and results from it. There is a rise in the new launch of such technologies and updates in the current.