What Is Big Data Analytics.webp

What Is Big Data Analytics?

Today, Big Data is one of the most important discussions among business leaders and industry captains. We are today living during a digitally-driven world, thanks to which each enterprise goes after Big Data so as to derive valuable insights out of the large amount of raw data. So, during this blog post, we'll learn what Big Data Analytics is, why it's so important, and what its various features and advantages are.

Big Data Types

Big Data is primarily measured by the volume of the data. But along with that, Big Data also includes data that is coming in fast and at huge varieties. Primarily, there are three types of Big Data, namely:

  • Structured Data

  • Unstructured Data

  • Semi-structured Data

Big Data are often measured in terms of terabytes and more. Sometimes, Big Data can cross over petabytes. The structured data includes all the info which will be stored during a tabular column. The unstructured data is the one that cannot be stored in a spreadsheet; and semi-structured data is something that does not conform to the model of the structured data. You can still search semi-structured data just like structured data, but it does not offer the ease with which you can do it on the structured data.

The structured data can be stored in a tabular column. Relational databases are examples of structured data. It is easy to form sense of the relational databases. Most of the modern computers are able to make sense of structured data. Unstructured data, on the other hand, is the one which cannot be fit into tabular databases. Examples of unstructured data include audio, video, and other kinds of data which comprise such an enormous chunk of the large Data today.

The semi-structured data includes both structured and unstructured data. This type of data sets include a proper structure, but still it might not be possible to sort or process that data due to some constraints. This type of data includes the XML data, JSON files, and others. Check out this insightful video on Big Data Analytics for beginners: Comparing Big Data Analytics with Data Science






Processing Big Data

In order to process Big Data, you would like to possess cloud and physical machines also. Today, thanks to the advancements within the technology, we'd include Cloud Computing and AI within the ambit of massive processing. Thanks to of these advancements, manual inputs are often reduced and automation can take over.

Data Analytics refers to the set of quantitative and qualitative approaches to derive valuable insights from data. It involves many processes that include extracting data, categorizing it so as to research various patterns, relations, and connections, and gathering other such valuable insights from it.

Today, almost every organization has morphed itself into a data-driven organization, and this suggests that they're deploying a data-driven approach so as to gather more data that's associated with the purchasers, markets, and business processes. This data is then categorized, stored, and analyzed to form sense out of it and derive valuable insights from it.

Understanding Big Data Analytics

With Big Data Analytics, you'll answer a replacement range of diagnostic questions on your business needs. It provides more data and complicated analytics to deliver actionable results to your business teams. You may start with a general question, one your traditional descriptive analytics has revealed.

Further, Big Data Analytics allows you to explore deeper diagnostic questions—some of which you would possibly not have even thought of asking—to reveal a replacement level of insight and identify steps that have to be taken to improve business performance. Many definitions on the subject of massive Data specialise in a bottom-up view, using the three Vs of data—volume, variety, and velocity.


Check this R tutorial that helps learn Big Data Analytics with R!

The term ‘Big Data Analytics’ might look simple, but there are sizable amount of processes which are comprised in Big Data Analytics. We can consider Big Data together which has huge volume, velocity, and variety. Big Data Analytics tools can add up of the large volumes of knowledge and convert it into valuable business insights.

Though the term ‘Big Data Analytics’ might sound simple, it's anything but simple. Data Analytics is most complex when it is deployed for Big Data applications. The three most vital attributes of massive Data include volume, velocity, and variety. The need for giant Data Analytics comes from the very fact that we are generating data at extremely high speeds and each organization must add up of this data. As per confirmed sources, by the year 2020, we will be generating a staggering 1.7 MB of data every second, contributed by every individual on earth.

All this tells us the importance of Big Data Analytics for making sense of all the huge volumes of data. Big Data Analytics helps us organize, transform, and model the info supported the wants of a corporation and identify patterns and draw conclusions from it.

Watch this insightful video to seek out out what an enormous Data Analyst does in real life: The larger the dimensions of the info the larger the matter. So, Big Data may be defined as the data where the size of it itself poses the problem and it needs newer ways of handling the same. The analysis of knowledge that's at high volume, velocity, and variety means the normal methods of working with the info wouldn't apply here.

Types of Big Data Analytics

Prescriptive Analytics: This is the type of analytics talks about an analysis, which is based on the rules and recommendations, to prescribe a certain analytical path for the organization. At the next level, prescriptive analytics will automate decisions and actions—how can I make it happen? Building upon the previous analytics, neural networks and heuristics are applied to the data to recommend the best possible actions that derive desired outcomes.

Predictive Analytics: This type of analytics ensures that the path is predicted for the future course of action. Answering the how and why questions will reveal specific patterns to detect when outcomes are about to occur. Predictive analytics builds upon the diagnostic analytics to look for these patterns and see what is going to happen. Machine Learning is also applied to continuously learn as new patterns emerge.

Descriptive Analytics: In this type of analytics, we work based on the incoming data. For the mining of this data, we deploy analytics and come up with a description based on the data. Many organizations have spent years generating descriptive analytics—answering the ‘what happened’ questions. This information is valuable, but only provides a high-level, rearview mirror view of the business performance. In Diagnostic Analytics, most organizations start to apply Big Data Analytics to answer diagnostic questions—how and why something happened. Some might also call these behavioral analytics.

Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard. Diagnostic Analytics with Big Data helps in two ways: (a) the additional data brought by the digital age eliminates analytic blind spots, and (b) the how and why questions deliver insights that pinpoint the actions need to be taken.

Regardless of the type of Big Data Analytics you want to deploy, algorithms play a key role. Read this insightful blog to find out more.

How Does Big Data Analytics Help Derive Business Insights?

There are various tools in Big Data Analytics which will be successfully deployed so as to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools got to be specifically ready to work with such sorts of data.

The advent of massive Data changed analytics forever, because of the lack of the normal data handling tools like electronic database management systems to figure with Big Data in its varied forms. Also, data warehouses couldn't handle data of extremely big size.

The era of massive Data drastically changed the wants for extracting meaning from business data. within the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the size of massive Data, cost-effectively.

While data warehouses are certainly a relevant sort of Data Analytics, the term ‘Data Analytics’ is slowly acquiring a selected subtext associated with the challenge of analyzing data of massive volume, variety, and velocity. Check this informative blog that talks about how Big Data Analytics is driving the simplest Formula 1 teams ahead.

Databases for Big Data Analytics

Non-relational Databases

Non-relational databases are used for working with unstructured data. Here, the info can't be stored within the regular tabular column. JSON files and XML are a number of the foremost important unstructured data types. With JSON, you'll write tasks within the application layer and this enables enhanced cross-platform functionalities.

In-memory Databases

When it involves big processing engines like Hadoop, the speed at which the processing happens is extremely low, because of the constant read and write access that's needed with respect to disk storage. But with the high-speed in-memory processing, you can do read and write at a much higher pace. This is where the in-memory processing engines like Apache Spark come into the picture.

Hadoop Hybrid: Data Storage and Processing

You can consider Hadoop as a hybrid processing engine which will work for both data storage and processing systems. The storage arm of Hadoop is that the Hadoop Distributed filing system, and therefore the processing arm of Hadoop is MapReduce. Due to the necessity for hybrid processing engines in today’s digitally disruptive world, Hadoop is finding increased acceptance. Apache Hadoop may be a hybrid data storage and processing tool which will be harnessed even by small organizations since it's a part of the open-source platform.

Importance of Data Mining

Data mining are often used for reducing costs and increasing revenues. Data processing is one among the elemental steps within the Data Analytics process. It’s the step wherein you perform the Extract, Transform, and cargo for getting the proper data into data warehouses. It also takes on the task of storing and managing data based in multidimensional databases. Within data processing, we've some recent phenomena that are supported contextual analyzing of massive data sets to get the connection between separate data items. The target is to use one data set for various purposes by different users. Finally, data processing is additionally assigned with the task of presenting the info which has been analyzed during a simple yet effective way.

Top Tools Used in Big Data Analytics

data science
top tools used in big data analytics.web

In this section, we will be familiarizing you with various aspects of the Big Data Analytics domain. Here, we include a list of analytical courses that you can take up:

Apache Spark: Spark is a framework for real-time Data Analytics which is part of the Hadoop ecosystem.

Python: This is one of the most versatile programming languages that is rapidly being deployed for various applications including Machine Learning.

SAS: SAS is an advanced analytical tool that is being used for working with huge volumes of data and deriving valuable insights from it.

Hadoop: It is the most popular Big Data framework that is being deployed by some of the widest range of organizations from around the world for making sense of big data.

SQL: This is the structured query language that is used for working with relational database management systems.

Tableau: This is the most popular Business Intelligence tool that is deployed for the purpose of data visualization and business analytics.

Splunk: Splunk is the tool of choice for parsing the machine-generated data and deriving valuable business insights out of it.

R Programming: R is the Number 1 programming language that is being used by Data Scientists for the purpose of statistical computing and graphical applications alike.

Watch this insightful video to learn more about the job role of a Data Analyst:


Major Sectors Using Big Data Analytics:

top industry deploying big data analytic


The retail industry is actively deploying Big Data Analytics. they're applying the techniques of knowledge Analytics to know what the consumers are buying and offering products and services that are tailor-made for these customers. Today, it's all about having an omni-channel experience. Customers might make contact with a brand on one channel, then finally pip out through another channel, meanwhile browsing more intermediary channels. Retailer will need to keep track of those customer journeys, and that they must deploy their marketing and advertising campaigns supported that so as to enhance the probabilities of sales and lower costs.


Technology companies, offering products and services, also are heavily deploying Big Data Analytics. They are checking out more how the purchasers interact with their websites or apps and gather key information. Based on this, they are able to optimize their sales, customer service, improve customer satisfaction, and more. This also helps them launch new products and services since today we live during a knowledge-intensive economy, and therefore the enterprises within the technology sector are reaping the advantages of Big Data Analytics.


Healthcare is another industry which will benefit tons from Big Data Analytics tools, techniques, and processes. Healthcare personnel can diagnose the health of their patients through various tests, run it through their computers, and search for telltale signs of anomalies and maladies, and more. Big Data Analytics also helps improve patient care and increase the efficiency of the treatment and drugs processes. Some diseases are often diagnosed before its onset in order that the measures are often taken during a preventive manner instead of a remedial manner.


Manufacturing is an industrial sector that's involved developing physical goods. The life cycle of a producing process can vary from product to product. The manufacturing systems are involved within the industry setup and across the manufacturing floor. There are tons of technologies that are involved like Internet of Things, Robotics, et al., but the backbone of every of those is firmly supported Big Data Analytics. Using Big Data Analytics, manufacturers can improve the yield, reduce the time to plug, enhance the standard, optimize the availability chain and logistics process, and build prototypes before the launch of products so on understand all the implications. Throughout of these steps, Big Data Analytics helps the manufacturers.


Most of the oil and gas companies which come under the energy sector are big users of massive Data Analytics. When it involves discovering oil and resources, tons of massive Data Analytics is deployed. Also, the market is extremely volatile for the fossil fuels. So, there's tremendous amounts of massive Data Analytics that goes into checking out what the worth of a barrel of oil are going to be , what the output should be, and if an oiler are going to be profitable or not. Big Data Analytics is additionally deployed find out the equipment failures, deploy predictive maintenance, and optimally use the resources so as to scale back the cost.


Data Analytics is one among the foremost vital aspects that's driving a number of the most important and best companies forward today. Enterprises which may convert data into information and knowledge into insights are those which can own the longer term during a hyper-competitive world. For instance, Uber disrupted the taxi hailing business, and Airbnb disrupted the hospitality business. Both these organizations are thriving on the sheer power of their deep data analytical mindset. So, the way forward for any company worth its salt is to possess a transparent data-driven approach and harness the facility of massive Data using transformational data analytical techniques.

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