“Data is a precious thing, and will last longer than the systems themselves.” – Tim Berners-Lee, Inventor of the World Wide Web (WWW)
If you want the storing, processing, analyzing, and visualizing your Big Data, then you should explore the latest Big Data Technologies in 2021.
To understand Big Data Technologies, one should first understand the concept of “Data Management”. This particular term is an all-important term that can give rise to and advance the data and processes into smart interferences.
One can say that Big Data Technologies is the key to the withdrawal of perception, ideas, intuition, and then generating value from your data.
As the world is changing with the modern inclusion of technology in every industry or organization, the consideration of extracting business value from basic information is the way for success measurement now. To make this happen, massive business enterprises have to deal with Big Data.
The term “Big Data” can be described as a massive pool of information that has the requirement of huge and expensive storage space, wants management, and needs analysis via the conventional systems. This is the point where the problem arises. As these old data management systems lack the effectiveness and limberness for dealing with Big Data (in an unstructured form), the ability of these management systems reaches its limit, thus creating a need for Big Data Technologies to handle this extremely large data volume.
You might have heard about Apache Hadoop, Apache Spark, NoSQL, Kafka, Apache Flame, etc. These technologies are now trending among all of the bid data handlers in the world. What might be the reason for this outbreak of Big Data Technologies? The reason is, these technologies are set in such a way that they continuously develop, grow, minimize the cost, and keep data in a simple form. Earlier, a few years ago, this could not have happened, and the solution to handle Big Data was done by hiring data management employees.
- The Flow of Big Data
- What is Big Data Technologies?
- Types of Big Data Technologies
- Advantages of Big Data Technologies
- Top Big Data Technologies in 2021
The Flow of Big Data
Where the flow of Big Data would go in the coming years?
Various data scientists have predicted that with the rising trend of Big Data, the following changes would be seen in the coming years:
- The data volume would grow, and it will drift to the cloud.
- The job in highest demands will be of Data Scientists and Chief Data Officers (CDOs).
- People will lose their privacy and would have to pay huge sums of money for the maintenance of their privacy.
- Technologies, especially machines, will continue their self-learning ML (machines learning) via AI (artificial intelligence).
- Fast Data will be in huge demand as per its processing speed in real-time.
Analysis shows that billions of people login into the internet daily. The demand for the internet has reached the point of being the bread and butter for almost everyone. Emails, tweets, social media posts, blogs, videos, images, etc are uploaded on an everyday basis and have their numbers in billions.
Data scientists say that around 2.7 quintillion bytes of data are uploaded and generated every day. This Big Data is in the form of unorganized information and a gold mine for the big companies at the same time. All of this created the demand for Big Data Technologies and tools to analyze this data so these businesses can make profitable decisions.
What is Big Data Technologies?
“The era of Data Technology is here and it will surpass the Information Technology era. The DT era is about transparency, sharing of information, and enabling others. Ali Baba is excited about the possibilities of the DT era and how it can bring value to society.” – Jack Ma, Executive Chairman, Ali Baba
The Big Data Technologies as mentioned earlier, are those kinds/types of software that are designed for the sole purpose of analyzing, extracting, and lastly processing information from the disorganized Big Data which the present/conventional/orthodox technologies cannot handle.
Overgrown businesses (Billion dollar enterprises) utilize Big Data Technologies to go through this enormous pool of Big Data to determine the demand and supply economic factor. The use of Big Data Technologies assists them in making those decisions that reduce the risk of losses.
Further, Big Data Technologies can integrate and embrace data, do data mining, data visualization, data sharing, data storage. They operate with tools and techniques for the investigation and transformation of data.
Types of Big Data Technologies
These are some major types of Big Data Technologies:
- Operational Big Data Technologies
- Analytical Big Data Technologies
- Data Mining Technologies
- Data Visualization Technologies
1. Operational Big Data Technologies
This type of Big Data Technologies covers the Big Data that is produced daily via google searches, social media posts, emails, or any type of data that comes under the category of Big Data. Afterward, this operational data is fed to the analytical Big Data Technologies for further analysis.
For instance, if you buy something on Amazon, that transaction is a part of operational Big Data Technology. Now just consider, how many online transactions are made every day not just on Amazon but worldwide. All of those transactions are a part of operational Big Data Technologies.
2. Analytical Big Data Technologies
Moving forward, the operational data technology feeds the data to the Analytical Big Data Technologies where things get a little more intricate. The Analytical Big Data Technologies take that massive data and narrow it down to the polished form of Big Data that can be later employed to make profitable business decisions.
In particular, we can say that this Analytical Big Data Technology is available in the medical health sector where the data records are kept.
3. Data Mining Technologies
Fundamentally, we can say that data mining is all about diving deep down into the sea of scattered Big Data and then extract the relevant information/data required by us.
Various data mining tools are available nowadays that can mine both the organized and scattered data that is uploaded/stored on multiple sources. These sources can range from database management systems (DBMS) to application programming interfaces (APIs).
One example of data mining technologies in KNIME. It is used in pharmaceutical research mostly. Furthermore, it can perform financial data analysis, customer data analysis, and business intelligence.
4. Data Visualization Technologies
Another popular type of Big Data Technologies is data visualization technologies. What it can do is that it can present the Big Data analysis in the form of charts, graphs, images, or anything that can be visually understood by people who are not data scientists.
Take Infogram for instance, it is a featured drag and drops visualization tool that gives liberty to users without a graphic design background. It can generate marketing reports, social media posts, maps, dashboards, infographics, and much more.
Advantages of Big Data Technologies
Several advantages of Big Data Technologies are as follow:
- Escalates business value.
- Improvement in Machines Learning (ML) and Artificial Intelligence (AI).
- Big Data Technologies cut down expenses, problems, and complications.
- Helps in cloud computing in addition to being very flexible.
- Access to the limitless volume of Big Data and the potential of analyzing that Big Data for business operations e.g. launch of a new product or a service.
- Allows the small businesses to compete with big businesses.
Top Big Data Technologies in 2021
Let us see a few top brands in Big Data Technologies in 2021.
- NoSQL Database
- R Programming
- Predictive Analytics
- Apache Spark
- Apache Hadoop
1. NoSQL Database
Various present-day applications are constructed and developed on NoSQL. It has a wide variety of distinct database technologies which are molded in such a manner that they can design and develop modern applications.
NoSQL database process works on real-time web application and analytics of Big Data. It shows a non-SQL or non-relational database that offers a procedure for retrieval of data and accumulation.
The one thing that makes it stand out from the other Big Data Technologies is its storage capacity of all the unorganized data while delivering a fast execution at the same time. It offers workability whenever it deals with several variations of datatypes at an enormous scale.
Coming to its design, it is very user-friendly with a horizontal scale interface where the user has ease of control over opportunities. In relational databases, data is accounted for by default data structures, however, NoSQL uses those data structures that are somewhat different from the default relational databases. This approach allows the computation to be at a faster pace than other Big Data Technologies.
2. R Programming
One of the free software out there that is a part of Big Data Technologies is R Programming. Basically, “R” is a programming language and is an open-source project. It is primarily used for data visualization, statistical computing, and unified developing environment like Eclipse and Visual Studio in assistance communication.
If you are in data analytics, you would be familiar with it. It is a very famous Big Data Technology especially used by data miners and statistical purposes.
3. Predictive Analytics
As per the demands of big businesses of predicting the future behavior of the market and analyzing the supply and demand economical factor, predictive analytics is a major player to fulfill this kind of need.
It moves to predict the future behavior of the market through the analysis of Big Data. It is considered to be a subpart of Big Data analytics, although many consider it to be a separate entity.
The way it works is by using the MLT (machine learning technologies), statistical modeling, data mining, and various mathematical models to predict the future move in the market.
Predictive Analytics is programmed with the ability to generate deductions very precisely. For instance, it can generate a business’s trend-based upon products via the Big Data in its raw form. It will show you the set of possibilities and probable outcomes of several techniques that you can apply as your next move.
4. Apache Spark
It is one of the most famous Big Data Technologies out there. The vital reason for its fame is its speed and the generation of Big Data transformation at a very common level.
Closely looking at its built-in feature gives us an insight into the matter of its popular usage. It has SQL, ML (machine learning), graph processing support, and streaming. Besides, it can support some major languages associated with Big Data like R, Java, Python, and Scala.
The integration of Apache Spark and Apache Hadoop, reduced the processing period by a ballpark. From cross-examination till program execution time, you don’t have to wait that much longer. Apache Hadoop was developed due to Apache Spark. The Apache Spark is used within Apache Hadoop for the reason of processing and storage purposes.
Apache Spark when compared to MapReduce, it is a hundred times faster.
Considering one of the most secure ecosystems out there is Blockchain. With the revolution brought by Big Data Technologies, Blockchain offers a wide range of applications for industries like banking, finance, healthcare, retailing, insurance, and many more.
Blockchain nowadays is majorly allocated with Bitcoin (digital currency) and it carries the database of bitcoin. Whenever data is stored in Blockchain, it gets secured in such a way that it can neither be deleted nor it can be varied.
Blockchain has global prospects although it is not yet completely developed. A wide range of innovative businesses like Microsoft, IBM, and others are experimenting with it to elaborate the usage of Blockchain in all the colors of life.
6. Apache Hadoop
Apache software foundation developed Apache Hadoop as open-source software for the reason of storing and processing Big Data. In some experts’ opinions, it is the top variant in Big Data Technologies.
Apache Hadoop is cheap, tolerant, and has such a huge framework that it can process data of every size easily and works with all kinds/types of formats.
The language it was writing is JAVA. Experts have commented that the Apache Hadoop HDFS is the most trustworthy storage on a global scale.
Exploring the features of Apache Hadoop will inform you that its framework is so user-friendly that it can easily work even in the most unfavorable conditions, for-instance machine crashes. It stores the data across commodity hardware, thus cutting all kinds of costs.
Multi-billion dollar companies like LinkedIn, IBM, MapR, Microsoft, Intel, and Facebook are all using Apache Hadoop and storing data in their data centres.
Following in the footsteps of other Big Data Technologies tools and software, MongoDB is also an open-source data analytics tool.
With its high-reliability features and the cost-cutting part, MongoDB has reached for the stars in its popularity.
The language query of MongoDB reinforces geo-based search, text search, graph search, and aggregation. AdHoc queries, indexing, replication, sharding, etc can be supported by MongoDB.
It can easily handle the management portion of all kinds of data i.e. stable, un-stable, organized, unorganized, or semi-structured data.
MongoDB administers Java, NET applications, and MEAN software stack.
Google, eBay, Facebook, etc all are using MongoDB.
We all have read, heard talks, and saw the name “Tableau” here and there whenever someone talks about Big Data Technologies.
The hidden reason behind its fame is that the experts say it is the perfect tool for the transformation of Big Data in raw format into a very simple/basic understandable and comprehensive way. You don’t need to have any technical skills or software coding knowledge to read Tableau report analysis.
Tableau has made the decision-making process considerably easy for the users of small businesses and even large giant companies, as the data processed by Tableau has visual insights which clear the entire scenario and give us a very digestible report.
Now coming to its features, it has a very simple layout with a drag and drops feature to make bar charts, histograms, treemaps, bullet charts, Gantt charts, pie charts, and various other variations.
The data sources it has to offer are also very huge and they can range from text files, CSV, Excel, spreadsheets, both relational and non-relational databases, on-cloud data, Big Data.
Furthermore, it can work with various Big Data tools by integrating and synchronizing with them.
Extraction of insights and having the ability to create value from data is nothing smaller than a miracle, and this miracle is covered by Big Data Technologies. They supply and furnish access to limitless volumes of information and analyze it in the simplest formats for making business decisions. Data mining, data sharing, data storage, and data visualization are all covered by Big Data Technologies.
Big Data Technologies is the future and we must all start looking into it from today as who knows, what will come next.