The bubble around Big Data has certainly started to burst, and the coming year awaits reasonable developments in the applications of the Big Data world. Well, most of us are now more than familiar with terms like Hadoop, Spark, NO-SQL, Hive, Cloud, etc. We know there are at least 20 NO-SQL databases and a number of other Big Data technologies emerging every month. But which of these Big Data technologies see prospects going forward? Which tools for Big Data are going to fetch you big benefits?
In this article, we’ll dive into the world of Big Data and explore popular Big Data technologies of 2022.
Let’s start with understanding the basics.
Big data is a specific indicator for the vast assembly of data, increasing enormously in size and exponentially with time. Big Data Technologies can be defined as software tools for analyzing, processing, and extracting data from an extremely complex and large data set with which traditional management tools can never deal.
Big Data Technologies are broadly classified into two categories.
Operational Big Data Technologies
Operational Big Data Technologies indicates the volume of data generated every day, such as online transactions, social media or any information from a particular company used for analysis by software based on big data technology. It acts as raw data to supply big data analysis technology. A few Operational Big Data Technologies cases include information on MNC management, Amazon, Flipkart, Walmart, online ticketing for movies, flights, railways, and more.
Analytical Big Data Technologies
Analytical Big Data Technologies concerns the advanced adjustment of Big Data Technologies, which is rather complicated than Operational Big Data. This category includes the real analysis of Big Data, which is essential to business decisions. Some examples in this area include stock marketing, weather forecasting, time series, and medical records analysis.
Let’s take a look at the top 5 Big Data technologies being used in IT Industries.
Hadoop Framework was developed to store and process data with a simple programming model in a distributed data processing environment. The data present on different high-speed and low-expense machines can be stored and analyzed. Enterprises have widely adopted Hadoop as Big Data Technologies for their data warehouse needs in the past year. The trend seems to continue and grow in the coming year as well. Companies that have not explored Hadoop so far will most likely see its advantages and applications.
Artificial Intelligence is a broad bandwidth of computer technology that deals with the development of intelligent machines capable of carrying out different tasks typically requiring human intelligence. AI is developing fast, from Apple’s Siri to self-driving cars. As an interdisciplinary branch of science, it takes into account a number of approaches, such as increased Machine Learning and Deep Learning to make a remarkable shift in most tech industries. AI is revolutionizing the existing Big Data Technologies.
NoSQL includes various Big Data Technologies in the database, which are developed to design modern applications. It shows a non-SQL or non-relational database providing a method for data acquisition and recovery. They are used in Web and Big Data Analytics in real time. It stores unstructured data and offers faster performance and flexibility while addressing various data types—for example, MongoDB, Redis, and Cassandra. It provides design integrity, easier horizontal scaling, and control over opportunities in a range of devices. It uses data structures that are different from those concerning databases by default, which speeds up NoSQL calculations. Facebook, Google, Twitter, and similar companies store user data on terabytes daily.
R is one of the open-source Big Data Technologies and programming languages. The free software is widely used for statistical computing, visualization, and unified development environments such as Eclipse and Visual Studio assistance communication. According to experts, it has been the world’s leading language. The system is also widely used by data miners and statisticians to develop statistical software and mainly data analysis.
Data Lakes means a consolidated repository for storage of all data formats at all levels in terms of structural and unstructured data. Data can be saved during Data accumulation as is without being transformed into structured data. It enables performing numerous types of Data analysis from dashboards and Data visualization to Big Data transformation in real-time for better business interference.
Businesses that use Data Lakes stay ahead of their competitors’ game 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 to better business growth opportunities by understanding and engaging clients, sustaining productivity, active device maintenance, and familiar decision-making to better business growth opportunities.
This is one of the most widely used and popular big data technologies. Being a sub-part of Big Data analytics, Predictive Analytics uses previous data to predict future events and behavior. It uses the following technologies:
Blockchain is the primary technology behind cryptocurrencies. Its uniqueness lies in its ability to capture structured data in a way that can never be altered or deleted. Because of this feature creates an extremely secure and reliable ecosystem for the Banking, Finance, Securities, and Insurance (BFSI) sector. Along with BFSI blockchains, they also find applications in social welfare sectors like education and healthcare.
TensorFlow has a robust, scalable ecosystem of resources, tools, and libraries for researchers, allowing them to quickly create and deploy powerful Machine Learning applications.
Apache Beam offers a compact API layout to create sophisticated Parallel Data Processing pipelines through various Execution Engines or Runners. Apache Software Foundation developed these tools for Big Data in the year 2016.
Docker is one of the tools for Big Data that makes the development, deployment and running of container applications simpler. Containers help developers stack an application with all the necessary components, such as libraries and other dependencies.
Apache Airflow is a Process Management and Scheduling System for the management of data pipelines. Airflow utilizes job workflows made up of DAGs (Directed Acyclic Graphs) tasks. The code description of workflows makes it easy to manage, validate and version a large amount of Data.
Kubernetes is one of the open-source tools for Big Data developed by Google for vendor-agnostic cluster and container management. It offers a platform for container systems’ automation, deployment, escalation, and execution through host clusters.
Blockchain is the Big Data technology that carries a unique data safe feature in the digital Bitcoin currency so that it is not deleted or modified after the fact is written. It’s a highly secured environment and an outstanding option for numerous Big Data applications in various industries like baking, finance, insurance, medical, and retail, to name a few.
The Big Data environment is continually evolving. Very easily, the latest innovations in Big Data Technologies are being launched, many of which will increase based on the demand in the IT industry. These innovations will ensure that there is harmonious functioning for the development of businesses.
Let’s take a look:
To summarize, Big Data is still very much rising with more adoptions and more applications of the existing Big Data technologies and the launch of newer solutions related to Big Data security, Cloud integrations, data mining, etc.
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