10 Top Big Data Technologies You Must Know 2023

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Top Big Data Technologies – ” Data Management “, an important term that can stem data intrusion and process it into intelligent interference. New strategies and methods are explored to make contemporary Big Data practices that provide the power and consistency to take businesses to the next level.

The best evolution in the digital age embraces big data technology to account for more spark in conventional technology.

In this blog, we are going to learn a plausible scenario from what is big data technologies and types of big data technologies to top innovations in big data technologies that are ready to transform the technological field.

Big Data Technologies
Big Data Technologies

What are The Big Data Technologies?

Big data is a specific indication that is used to describe the vast assemblage of data that is huge in size and exponentially increasing with time. It simply specifies the massive amount of data that is hard to stock, investigate, and transform with conventional tools of management.

According to Gartner, the definition of Big Data –  “Big data is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

Actually, Big Data Technologies is the utilized software that incorporates data mining, data storage, data sharing, and data visualization, the comprehensive term embraces data, data framework including tools and techniques used to investigate and transform data.

In the large perceptions of rage in technology, it is widely associated with other technologies like Machine Learning, Deep Learning, Artificial Intelligence, and IoT that are augmented on the large scales.

Have a look at the video below for a more clear understanding of big data (introduction)

Read Also: 7 Best Big Data Analytics Trends To Know In 2023

Big Data Technologies can be split into two categories

Big Data Technologies
Big Data Technologies

1. Operational Big Data Technologies:

It indicates the generated amount of data on a daily basis such as online transactions, social media, or any sort of data from a specific firm used for the analysis through big data technologies based software. It acts as raw data to feed the Analytical Big Data Technologies.

Few cases that outline the Operational Big Data Technologies include executives’ particulars in an MNC, online trading and purchasing from Amazon, Flipkart, Walmart, etc, online ticket booking for movies, flight, railways and many more.

2. Analytical Big Data Technologies:

It refers to advance adaptation of Big Data Technologies, a bit complicated in comparison to Operational Big Data. The real investigation of massive data that is crucial for business decisions comes under this part. Some examples covered in this domain are stock marketing, weather forecasting, time series analysis, and medical-health records.

TOP Big Data Technologies Trending in 2023

Now, we shall discuss the leading-edge technologies (in no particular order) that influence the market and IT industries in recent time;

1. Artificial Intelligence

A broad bandwidth of computer science that deals in designing smart machines capable of accomplishing various tasks that typically demand human intelligence is known as Artificial Intelligence.

From SIRI to self-driving car, AI is developing very swiftly, on being an interdisciplinary branch of science, it takes many approaches like augmented machine learning and deep learning into account to make a remarkable shift in almost every tech industry.

The excellent aspect of AI is the strength to intellectualize and make decisions that can provide a plausible likelihood in achieving a definite goal. AI is evolving consistently to make benefits in various industries. For example, AI can be used for drug treatment, healing patients, and conducting surgery in OT.

2. NoSQL Database

NoSQL incorporates a broad range of separate database technologies that are developing to design modern applications. It depicts a non SQL  or nonrelational database that delivers a method for accumulation and retrieval of data. They are deployed in real-time web applications and big data analytics.

It stores unstructured data and delivers faster performance, and proffers flexibility while dealing with varieties of datatypes at a huge scale. Examples included MongoDB, Redis, and Cassandra.

It covers the integrity of design, easier horizontal scaling to an array of devices and ease control over opportunities. It uses data structures that are different from those accounted by default in relational databases, it makes computations quicker in NoSQL. For example, companies like Facebook, Google and Twitter store terabytes of user data every single day.

Read Also: Top 6 Big Data Platform Tools Free 2023 (Update)

3. R Programming

R is the programming language and an open-source project. It is a free software highly used for statistical computing, visualization, unified developing environments like Eclipse and Visual Studio assistance communication.

Expert says it has graced the most prominent language across the world. Along with it, being used by data miners and statisticians, it is widely implemented for designing statistical software and mainly in data analytics.

4. Data Lakes

Data Lakes refers to a consolidated repository to stockpile all formats of data in terms of structured and unstructured data at any scale.

In the process of data accumulation, data can be saved as it is, without transforming it into structured data and executing numerous kinds of data analytics from dashboard and data visualization to big data transformation, real-time analytics, and machine learning for better business interferences.

Organizations that use data lakes will be able to defeat their peers, new types of analytics can be conducted such as machine learning across new sources of log files, data from social media and click-streams and even IoT devices freeze in data lakes.

It helps organizations to know and respond to better opportunities for faster business growth by bringing and engaging customers, sustaining productivity, maintaining devices actively, and taking acquainted decisions.

5. Predictive Analytics

A subpart of big data analytics, it endeavors to predict future behavior via prior data. It works using machine learning technologies, data mining and statistical modeling and some mathematical models to forecast future events.

The science of predictive analytics generates upcoming inferences with a compelling degree of precision. With the tools and models of predictive analytics, any firm deploys prior and latest data to drag out trends and behaviors that could occur at a particular time. You should check the description of predictive modeling in machine learning in this blog.

For example, to explore the relationships among various trending parameters. Such models are designed to assess the pledge or risk delivered by a specific set of possibilities.

6. Apache Spark

With in-built features for streaming, SQL, machine learning and graph processing support, Apache Spark earns the cite as the speedest and common generator for big data transformation. It supports major languages of big data comprising Python, R, Scala, and Java.

The Hadoop was introduced due to spark, concerning the main objective with data processing is speed. It lessens the waiting time between interrogating and program execution timing. The spark is used within Hadoop mainly for storage and processing. It is a hundred times faster than MapReduce.

7. Prescriptive Analytics

Prescriptive Analytics gives guidance to companies about what they could do when to achieve aspired outcomes. For example, it can give notice to a company that the borderline of a product is expecting to decrease, then prescriptive analytics can assist in investigating various factors in response to market changes and predict the most favorable outcomes.

Where it relates both descriptive and predictive analytics but focuses on valuable insights over data monitoring and give the best solution for customer satisfaction, business profits, and operational efficiency.

8. In-memory Database

The in-memory database (IMDB) is stored in the main memory of the computer (RAM) and controlled by the in-memory database management system. In prior, conventional databases are stored on disk drives.

If you consider, conventional disk-based databases are configured with the attention of the block-adapt machines at which data is written and read.Instead, When one part of the database refers to another part, it feels the necessity of different blocks to be read on the disk. This is a non-issue with an in-memory database where interlinked connections of the databases are monitored using direct indicators.

In-memory databases are built in order to achieve minimum time by omitting the requirements to access disks. But, as all data is collected and controlled in the main memory completely, there are high chances of losing the data upon a process or server failure.

9. Blockchain

Blockchain is the assigned database technology that carries Bitcoin digital currency with a unique feature of secured data, once it gets written it never be deleted or changed later on the fact.

It is a highly secure ecosystem and an amazing choice for various applications of big data in industries of banking, finance, insurance, healthcare, retailing, etc.

Blockchain technology is still in the process of development, however, many merchants of various organizations like AWS, IBM, Microsoft including startups have tried multiple experiments to introduce the possible solutions in building blockchain technology.

10. Hadoop Ecosystem

The Hadoop ecosystem consists of platforms that help solve challenges around big data. It incorporates various components and services which vary i.e. ingest, store, analyze and maintain in it.

The majority service that is prevalent in the Hadoop ecosystem is to complement its various components which include HDFS, YARN, MapReduce, and Common.

The Hadoop ecosystem consists of the Apache Open Source project and various other commercial tools and solutions. Some well-known open source examples include Spark, Hive, Pig, Sqoop and Oozie.


Big data ecosystems are constantly emerging and new Big Data Technologies are emerging at a rapid pace, many of which are evolving more closely to the demands of the IT industry. This technology ensures harmonious work with good supervision and safety.

I hope this blog has given you a general introduction to how big data technology is revolutionizing traditional data analysis models. We also understand the breakdown of the tools and deck technologies that Big Data uses to spread its wings to reach the highest heights.