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Top Big Data Technologies for 2022 You Should Watch out

Top Big Data Technologies for 2022 You Should Watch out

We live in a digital era where companies produce and deal with vast amounts of data daily. Big data is defined simply as a sizable collection of structured and unstructured data that has the potential to grow exponentially with increased digitization. Due to the volume and complexity of data, traditional data processing software cannot handle or extract useful information, which is why many businesses are turning to big data technologies today. 

With the aid of big data technologies, businesses can easily store, process, and analyze vast amounts of data to uncover useful information.

In 2022, there will be a variety of trustworthy big data technologies to choose from, but the real question we should be asking is which of these technologies is more likely to succeed after 2022. 

Keep reading this article to discover the Top 10 Big Data Technologies to look out for in 2022. 

What is Big Data Technology?

The term "big data" refers to the large volume, velocity, and variety of information assets that demand cost-effective and creative methods to process data for better insights and decision-making instead of conventional methods for data processing. Thus, seeing the advantage, companies globally are embracing big data technologies to gain more insights and make more profitable decisions.  

The term "big data technologies" refers to a class of software tools primarily created to process, analyze, and extract data from large complex datasets that more conventional data processing techniques cannot handle. 

Top 10 Trending Big Data Technologies to look out for

  1. Apache Hadoop

The Apache Software Foundation created Apache Hadoop, an open-source, Java-based framework for handling and storing large amounts of data. Apache Hadoop uses the MapReduce programming model to process a lot of data and provide a platform for distributed storage. The best feature of the Hadoop framework is its ability to handle hardware failures automatically. The five components of the Hadoop framework are Hadoop Distributed File System (HDFS), Hadoop YARN (Yet Another Resource Negotiator), Hadoop MapReduce, Hadoop Common, and Hadoop Ozone.

A few key features of Apache Hadoop are

  • Hadoop Distributed File System (HDFS) is a distributed file system that enables fast data transfer between nodes.
  • Hadoop is a fault-tolerant, inexpensive, and extremely flexible framework capable of storing and processing data in any format.  
  1. MongoDB

MongoDB is a document-oriented, open source, cross-platform database that handles large amounts of data while offering high availability, performance, and scalability. Because it doesn't store or retrieve data in the form of tables, MongoDB is regarded as a NoSQL database. The widespread use of MongoDB results from its document-oriented NoSQL features, its ability to perform Mao reduction calculations, and distributed key-value store. 

The DB- Engines named MongoDB the "Database Management Management System of the Year." 

  1. Artificial Intelligence 

Artificial intelligence is encouraging a change in the IT landscape and across all industries, along with augmented technologies like machine learning (ML) and deep learning. 

The applications range from sophisticated robotic surgeries and self-driving cars to precise weather forecasts and voice-based assistants. Business analytics is powered by AI and ML, changing the world and enabling organizations to innovate at a higher level.  

  1. R Programming 

R is free software that uses Eclipse-based environments to help with statistical computing, visualization, and communication. R is a programming language and thus offers an array of coding and pacing tools. 

Statisticians and data miners primarily use R for data analytics because it supports high-quality plotting, graphing, and reporting. R programming is also integrated with Hadoop and other database management systems and popular languages like C, C++, Python, and Java.  

  1. Data Lakes 

Because of the enormous amount of data generated by cloud computing from every organization, more sophisticated storage techniques have been developed. One of the most popular Big Data technologies, data lakes are repositories that let users store any kind and amount of data.  

The benefits of this cloud-based Big Data technology are that it includes scalability and versatility of data formats, which translates into reduced data management costs. Data lakes' ability to support on-site processing is another selling point.  

  1. Blockchain 

The main technology underlying cryptocurrencies like bitcoin is called a blockchain. It captures structured data in a way that, once written, can never be changed or deleted, creating a highly secure ecosystem ideal for the banking, finance, security, and insurance (BFSI) industries.

As the presumed savior of legacy banking and IT infrastructures, blockchain continues to be the hottest Big Data technology this year. Blockchain technology can reduce storage costs for legal-based transactional data when applied to information technology. 

By decentralizing the necessary technology, blockchain can increase the security and ease of information exchange while lowering the associated infrastructure costs, thereby eliminating the accessibility of analytics tools.  

  1. Kubernetes

Kubernetes is one of the cloud Big Data technologies developed by Google. It is primarily used for container management and vendor-neutral clustering. KUbernetes, in its early days, was not capable of managing Big data workloads, but today new advancements allow this tool to support large-scale infrastructures.

Closing Thoughts

Overall, the future of Big Data looks promising. The era of Big Data Technologies has given rise to various innovations that are likely to gain popularity as the big organization's demands increase. These innovations will catalyze business development. To make the most of Big Data technologies available on the market, you should identify the type of problems your organization is facing. This article will assist readers in efficiently navigating the best Big Data technologies.