What is big data technology & sample Top 10 Technologies

Writer : Mr. George Miguel

One of the most important terms in the field of data management is "Data Management." New approaches and methods are being investigated to create a modern Big Data practice that provides the strength and consistency needed to raise business to a higher level.

Big data technologies are the best evolution in the digital era because they allow conventional technologies to have more oomph.

There are a number of big data technologies that will be discussed in this blog, ranging from definitions and classifications to cutting-edge advancements that could have a profound impact on today's world of technology.


Where can I find out about big data technology?

The term "big data" refers to a collection of data that is both enormous in scope and growing at an exponential rate. There are simply too many data points to store, investigate, and transform with current management methods.

It is true that Big Data Technologies are the tools and techniques used to investigate and transform massive amounts of digital data. This includes data mining, storage, sharing, and visualization.

Radical technologies like Machine Learning, Deep Learning and Artificial Intelligence (AI/IoT) are widely associated with large scale rage in technology.

You can learn more about big data by watching this video (introduction)


Using big data, it is possible to categorize technologies.


1. It's all about operational Big Data:

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.

An MNC's executives' personal information, online trading and purchasing from Amazon and Flipkart, as well as online ticket bookings for movies, planes, and trains, are examples of operational big data technologies.


2. Big Data technologies for analysis:

Compared to Operational Big Data, this refers to the more advanced adaptation of Big Data technologies. Data analysis that is critical to making business decisions is included in this section. Time series analysis and medical-health records are among the examples covered in this field.


2020's Big Data Technologies of the Year.

It is time for us to take a closer look at some of the most cutting-edge technologies that have recently had an impact on business and technology.


1. Artificial Intelligence

As a broad field of computer science, Artificial Intelligence covers the development of intelligent machines that can perform a wide range of tasks that typically require human intelligence. (You can learn more about how AI mimics the human brain here)

Artificial Intelligence (AI) is advancing rapidly, taking into account a wide range of approaches, such as augmented machine learning and deep learning, to make a significant shift in almost every technology industry.

AI's greatest strength is its ability to think critically and make decisions that have a reasonable chance of achieving a desired outcome. Various industries are seeing the benefits of AI's constant improvement. To give just a few examples, AI can be used in OT to treat patients, heal them, and perform surgery.


2. NoSQL Database

To design modern applications, NoSQL incorporates a wide range of separate database technologies. An illustration depicts a non-SQL or non-relational database that provides a means of storing and retrieving data. Web-based real-time applications and big data analytics make use of these technologies.

To understand the real-time-big data analytics: (Must read to understand the Internet of Things(IoT )'s)

It stores unstructured data and delivers faster performance, and it offers flexibility while dealing with a wide range of datatypes at a large scale. MongoDB, Redis, and Cassandra are just a few examples.

As well as improving horizontal scaling to a wide range of devices, it also gives users more power and control over potential opportunities. It speeds up NoSQL computations by using data structures that aren't included by default in relational databases. Terabytes of user data are stored every day by companies such as Facebook, Google, and Twitter.


3. R Programming

R is a programming language and an open-source project that is freely available. Statistical computing, visualization, and unified development environments like Eclipse and Visual Studio assistance communication use this free software.

He claims it has become the most widely spoken language on this planet, according to an expert. Additionally, it is frequently employed in the design of statistical software and in the analysis of large amounts of data, particularly in data mining.




4. Data Lakes

The term "Data Lakes" refers to a centralized repository for storing all types of data, both structured and unstructured, at any size.

You don't necessarily need to transform your data into structured information and perform numerous types of analytics on it, such as dashboards and visualizations or real-time analytics or machine learning for better business interferences during the data accumulation process. (See blog: 5 Common Types of Business Analytics Data Visualization)

There are many advantages to using data lakes in an organization, such as being able to conduct machine learning across log files from social media and click-streams, as well as IoT devices that are frozen in 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

It is a subset of big data analytics that aims to predict future behavior based on previous data. Foreseeing future events is made possible through the use of machine learning, data mining, statistical modeling, and other mathematical models.

Predictive analytics is a science that generates accurate inferences for the future. Using the tools and models of predictive analytics, any company can use data from both the past and the present to identify possible future trends and behaviors. To learn more about predictive modeling in machine learning, you should read this blog post.

Such as to investigate the correlations between trending parameters. Models like this one are used to evaluate the promise and risk associated with a specific set of possibilities.



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. Python, R, Scala, and Java are all supported by this tool.

It was already covered in a previous post about the architecture of the Apache web server.

Spark was the driving force behind the introduction of Hadoop, as the primary goal of data processing is speed. It reduces the amount of time it takes for the program to run after the user has been questioned. Spark is primarily used in Hadoop to store and process data. Over one-hundred-fold improvement over MapReduce.



7. Prescriptive Analytics

In order to help companies achieve their desired outcomes, Prescriptive Analytics provides them with advice. So, if the company receives notice that the product's edge is expected to shrink, prescriptive analytics can help investigate various factors in response to market changes and predict which outcomes will be most favorable.

However, rather than focusing on data collection and collection only, this approach focuses on gaining insights that can be used to improve customer satisfaction and business profitability while also enhancing operational efficiency.


8. In-memory Database

It is the in-memory database management system (IMDBMS) that manages and stores the in-memory database (IMDB). Disk drives used to be the primary storage medium for conventional databases.

The block-adapt machines at which data is written and read influence the design of conventional disk databases.

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

Since disk access is no longer necessary, in-memory databases are designed to reduce processing time to a minimum. Due to this, a process or server failure could result in complete loss of data, as all information is stored and controlled entirely in the main memory.


9. Blockchain

Because of its unique feature of permanently preserving data once it has been written, the Blockchain is the designated database technology for the Bitcoin digital currency.

One of the best places to use big data in industries such as banking and financial services, health care, and retail is in this highly secure ecosystem.

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 multiple experiments to introduce the possible solutions in building blockchain technology. (Refer to the blog post: Do Blockchain and Artificial Intelligence Incorporate an Ideal Model? ).



10. Hadoop Ecosystem

The Hadoop ecosystem consists of a platform that aids in the resolution of big data challenges. Ingesting, storing, analyzing, and maintaining data are just some of the functions it provides.

In the Hadoop ecosystem, most of the services exist to support the various components such as HDFS (YARN), MapReduce (Common), or HDFS (HDFS).

A wide range of commercial and open source solutions and tools are included in the Hadoop ecosystem. Spark, Hive, Pig, Sqoop, and Oozie are just a few of the well-known open source examples.


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