The following are the top 10 Big Data technologies

Writer : Angle Marque EG

One of the most important terms in the field of data management is "Data Management." Modern Big Data technologies practices are being investigated for their strength and consistency in raising business to the next level through the use of new strategies and methods.

When it comes to modern technology, big data is at the forefront of the best advancements.

We'll cover everything from the definition of big data and the various types of big data technologies to the most cutting-edge big data innovations that have the potential to completely alter the technological landscape in the near future.

What are the big data technologies?

"Big data" is a term used to describe an ever-expanding collection of data that is massive in size and grows exponentially over time. It simply identifies the enormous amount of data that is difficult to store, investigate, and transform with conventional management tools.

Large-scale data processing and storage systems have been dubbed "Big Data Technologies" because they encompass a wide range of software tools and techniques needed to investigate and transform vast amounts of information.

Machine Learning, Deep Learning, AI, and the Internet of Things (IoT) are widely associated with rage in technology in the large-scale perceptions.

It is possible to categorize Big Data Technologies into two main groups.

1. Operational Big Data Technologies:

Big data technologies are used to analyze data from a variety of sources, such as daily online transactions, social media, or any other type of data generated by a single company. Analytical Big Data Technologies use it as raw data.

Executive information in a multinational corporation is one example of how operational big data technologies are being used. Other examples include online trading and purchasing from Amazon, Flipkart, Walmart and other online retailers and online ticketing for movies, flights, and trains, among others.

2. Analytical Big Data Technologies:

In contrast to operational Big Data, this refers to a more advanced adaptation of Big Data Technologies. This section is responsible for conducting in-depth analyses of large amounts of data that are critical to business decisions. Stock marketing, weather forecasting, time series analysis, and medical records are just a few of the topics that fall under this category.

2020's Most Popular Big Data Technologies.

Now, let's talk about the most cutting-edge technologies that have recently had an impact on the market and the IT industry (in no particular order);

1. Artificial Intelligence

Artificial intelligence (AI) encompasses a wide range of computer science topics concerned with the development of intelligent machines capable of performing a wide range of tasks that typically require human intelligence. (You can learn more about how AI mimics human thought processes here.)

Because it is an interdisciplinary field of study, artificial intelligence (AI) is progressing quickly across a wide range of fields, from voice assistants like Siri to self-driving cars.

When it comes to artificial intelligence, one of the best features is its ability to reason and make decisions that increase the likelihood of achieving specific goals. An increasing number of industries are reaping the benefits of artificial intelligence. AI can be used in a variety of ways, including drug treatment, healing patients, and surgery in the operating room.

2. NoSQL Database

To design modern applications, NoSQL incorporates a wide range of separate database technologies. Data can be stored and retrieved using this non-SQL or non-relational database. Web applications and big data analytics make use of this technology.

In addition to storing unstructured data, it provides faster performance and greater flexibility when dealing with a wide range of data types at a large scale. Redis, Cassandra and MongoDB are just a few of the databases mentioned.

Design integrity, horizontal scaling to a variety of devices, and opportunity control are all covered. To speed up computations in NoSQL, it employs data structures that are distinct from those used by relational databases. For example, companies such as Facebook, Google, and Twitter store terabytes of user data every single day.

3. R Programming

As a programming language and open-source undertaking, R is both popular and widely used today. It's a free program widely used for statistical computing, visualization, and communication between unified development environments like Eclipse and Visual Studio.

An expert claims that it has become the most widely spoken language in the world. As a result of its widespread use by statisticians and data miners, it is frequently employed in the design of statistical software and in data analytics.

4. Data Lakes

Unstructured as well as structured data can be stored in one central location, which is known as a "Data Lake."

It is possible to store data in its raw form, without transforming it into structured data, for the sake of better business outcomes, rather than transforming it and performing a variety of data analytics, such as dashboard and data visualization, big data transformation, real-time analytics, and machine learning. (Refer to the Blog: 5 Common Types of Data Visualization in Business Analytics for more information on the various types of data visualization).

Machine learning across log files, data from social media and clickstreams, and even IoT devices that are frozen in data lakes can be used to conduct new types of analytics. organizations that use data lakes.

Customers are brought and engaged, productivity is maintained, devices are actively maintained, and informed decisions are taken to help businesses grow faster.

5. Predictive Analytics

Analyzing historical data to make predictions about the future is one of the main goals of this subset of big data analytics. It uses machine learning, data mining, statistical modeling, and some mathematical models to predict future events.

Predictive analytics is a branch of science that generates future inferences with a high degree of accuracy. Predictive analytics tools and models can be used by any company to identify trends and behaviors that could occur at a specific time. Predictive modeling in machine learning is described in detail on this blog.

As an illustration, consider examining the connections between various trending parameters. These types of models are made to evaluate the promise or risk that a particular set of possibilities delivers.

6. Apache Spark

Apache Spark is the fastest and most common generator of big data transformation because of its built-in features for streaming, SQL, machine learning, and graph processing. Among the many big data programming languages supported by this platform are Python, R, Scala, and Java, to name a few.

Previously, we talked about the Apache architecture in this blog.

Due to the primary goal of data processing speed, the Hadoop was introduced as a result of spark. It reduces the amount of time it takes for a program to run after an interrogation. Spark is primarily used for storage and processing in Hadoop. As compared to MapReduce, it is a hundred times faster.

7. Prescriptive Analytics

In order to help companies achieve their desired outcomes, Prescriptive Analytics provides them with advice. For example, if a product's borderline is expected to decrease, then prescriptive analytics can help a company investigate various factors in response to market changes and predict the best outcomes.

In the context of both descriptive and predictive analytics, it emphasizes valuable insights over data monitoring and provides the best solution for customer satisfaction, business profits, as well as overall operation efficiency.

8. In-memory Database

The in-memory database management system (IMDBMS) manages and stores the in-memory database (IMDB) in the computer's main memory (RAM). Prior to the advent of cloud computing, conventional databases were kept on disk drives.

Conventional disk-based databases, on the other hand, are designed with the block-adapting machines in mind.

As an alternative, when one part of the database refers to another part, it feels the need to read a different number of blocks from the disk. Using an in-memory database, this is not a problem, as direct indicators of interlinked database connections are used to monitor the databases.

In-memory databases are designed to reduce the amount of time it takes to retrieve data by omitting the need to access disks. There is, however, a high risk of losing data due to the fact that all data is stored and controlled in the main memory.

9. Blockchain

A unique feature of the Blockchain database technology that carries Bitcoin digital currency is that once a transaction is written, it cannot be deleted or altered later on.

In the banking, finance, insurance, healthcare, and retail industries, it's a great option for a variety of big data applications because of its high level of security.

Although blockchain technology is still in the early stages of development, merchants from various organizations including AWS, IBM and Microsoft as well as startups have tried numerous experiments to introduce the possible solutions to building blockchain technology. Is There a Model for Blockchain and Artificial Intelligence? (See blog)

10. Hadoop Ecosystem

In the Hadoop ecosystem, there is a platform that helps with big data issues. For example, it can be used to store, analyze, and maintain data as well as to ingest, store, and store it again.

HDFS, YARN, MapReduce, and Common are just a few of the components that make up the Hadoop ecosystem as a whole.

An array of commercial and open-source initiatives make up the Hadoop ecosystem, as does Apache Open Source. Spark, Hive, Pig, Sqoop, and Oozie are some of the most well-known open source examples.


In response to the increasing demand for IT services, a slew of new technologies and services are being introduced into the big data ecosystem all the time. These innovations ensure a smooth workflow under the watchful eye of a skilled leader.

I hope that this blog has given you a basic understanding of how revolutionary big data technologies are changing the traditional model of data analysis. We also learned how Big Data is preparing to soar to new heights by breaking the deck with cutting-edge tools and technologies.

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