big-data-analytics

Big Data Analytics

What Is Big Data Analytics?

Big Data Analytics is a complete process of examining large sets of data through varied tools and processes in order to discover unknown patterns, hidden correlations, meaningful trends, and other insights for making data-driven decisions in the pursuit of better results.

Today, Big Data is one of the most important conversations among business leaders and industry skippers. We are today living in a digitally-driven world, because of which every enterprise is following Big Data so as to derive important experiences out of the gigantic measure of raw data. Along these lines, in this blog entry, we will learn what Big Data Analytics is, the reason it is so important, and what its various features and preferences are.

Big Data Types

BigDataTypes-1

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 can be measured in terms of terabytes and that’s just the beginning. Now and again, Big Data can cross over petabytes. The structured data incorporates all the data that can be stored in a tabular segment. The unstructured data is the one that can’t be stored in a spreadsheet; and semi-structured data is something that doesn’t conform with the model of the structured data. You can in any case search semi-structured data simply like structured data, however it doesn’t offer the straightforwardness with which you can do it on the structured data.

The structured data can be stored in a tabular section. Relational databases are instances of structured data. It is anything but difficult to understand the relational databases. A large portion of the modern computers are ready to understand structured data.

Unstructured data, then again, is the one which can’t be fit into tabular databases. Instances of unstructured data incorporate sound, video, and other sorts of data which comprise such a big piece of the Big Data today.

The semi-structured data incorporates both structured and unstructured data. This kind of data sets incorporate a proper structure, yet it probably won’t be conceivable to sort or process that data because of certain constraints. This sort of data incorporates the XML data, JSON records, and others.

Comparing Big Data Analytics with Data Science

CriteriaBig Data AnalyticsData Science
Type of Data ProcessedStructuredAll types
Types of ToolsStatistics and data modelingHadoop, coding, and Machine Learning
Domain ExpanseRelatively smallerHuge
New IdeasNot neededNeeded

Processing Big Data

So as to process Big Data, you have to have cloud and physical machines also. Today, because of the headways in the innovation, we may incorporate Cloud Computing and Artificial Intelligence within the ambit of Big Data processing. Because of every one of these headways, manual sources of info can be reduced and mechanization can dominate.

Data Analytics refers to the arrangement of quantitative and qualitative approaches to derive significant experiences from data. It includes numerous processes that incorporate extracting data, categorizing it so as to investigate various patterns, relations, and associations, and gathering other such important bits of knowledge from it.

Today, pretty much every organization has morphed itself into a data-driven organization, and this implies they are sending a data-driven approach so as to gather more data that is related to the customers, markets, and business processes. This data is then categorized, stored, and examined to bode well out of it and derive important experiences from it.

Understanding Big Data Analytics

With Big Data Analytics, you can answer another range of demonstrative questions about your business needs. It provides more data and refined analytics to deliver significant results to your business groups. You may start with a general inquiry, one your traditional descriptive analytics has revealed.

Further, Big Data Analytics lets you explore deeper indicative questions—some of which you probably won’t have even idea of soliciting—to reveal another degree of understanding and distinguish steps that must be taken to improve business performance. Numerous definitions on the subject of Big Data center around a base up see, utilizing the three Vs of data—volume, variety, and velocity.

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

The term ‘Big Data Analytics’ strength look basic, however there are large number of processes which are comprised in Big Data Analytics. We can consider Big Data as one which has immense volume, velocity, and variety. Big Data Analytics tools can understand the gigantic volumes of data and convert it into significant business experiences.

Despite the fact that the term ‘Big Data Analytics’ strength appear to be basic, it is definitely not basic. Data Analytics is most intricate when it is sent for Big Data applications. The three most important attributes of Big Data incorporate volume, velocity, and variety.

The requirement for Big Data Analytics originates from the way that we are generating data at extremely high speeds and every organization needs to comprehend this data. As per confirmed sources, constantly 2020, we will generate a staggering 1.7 MB of data every second, contributed by every person on earth.

This discloses to us the importance of Big Data Analytics for understanding all the enormous volumes of data. Big Data Analytics encourages us organize, transform, and model the data dependent on the requirements of an organization and recognize patterns and draw ends from it.

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.

How Does Big Data Analytics Help Derive Business Insights?

There are various tools in Big Data Analytics that can be effectively sent so as to parse data and derive important bits of knowledge out of it. The computational and data-taking care of difficulties that are looked at scale imply that the tools should be explicitly ready to work with such sorts of data.

The appearance of Big Data changed analytics forever, on account of the inability of the traditional data taking care of tools like relational database management frameworks to work with Big Data in its varied forms. Additionally, data warehouses couldn’t deal with data of extremely big size.

The era of Big Data drastically changed the requirements for extracting significance from business data. In the world of relational databases, administrators effortlessly generated reports on data substance for business use, however these provided little or no broad business intelligence. For that, they utilized data warehouses, yet data warehouses generally can’t deal with the size of Big Data, cost-viably.

While data warehouses are certainly a relevant form of Data Analytics, the term ‘Data Analytics’ is gradually acquiring a particular subtext related to the test of dissecting data of monstrous volume, variety, and velocity. Check this informative blog that discussions about how Big Data Analytics is driving the best Formula 1 groups ahead.

Databases for Big Data Analytics

Non-relational Databases

Non-relational databases are used for working with unstructured data. Here, the data cannot be stored in the regular tabular column. JSON files and XML are some of the most important unstructured data types. With JSON, you can write tasks in the application layer and this allows enhanced cross-platform functionalities.

In-memory Databases

When it comes to Big Data processing engines like Hadoop, the speed at which the processing happens is extremely low, thanks to the constant read and write access that is 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 think of Hadoop as a hybrid processing engine that can work for both data storage and processing systems. The storage arm of Hadoop is the Hadoop Distributed File System, and the processing arm of Hadoop is MapReduce. Due to the need for hybrid processing engines in today’s digitally disruptive world, Hadoop is finding increased acceptance. Apache Hadoop is a hybrid data storage and processing tool that can be harnessed even by small organizations since it is part of the open-source platform.

Importance of Data Mining

Data mining can be utilized for reducing costs and increasing revenues. Data mining is one of the major strides in the Data Analytics process. It is the progression wherein you perform the Extract, Transform, and Load for getting the right data into data warehouses. It additionally assumes the undertaking of storing and overseeing data situated in multidimensional databases. Within data mining, we have some recent marvels that depend on relevant examining of big data sets to discover the relationship between separate data items. The goal is to utilize a solitary data set for different purposes by different users. At long last, data mining is additionally allocated with the errand of presenting the data which has been investigated in a straightforward yet compelling manner.

Top Tools Used in Big Data Analytics

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.

Major Sectors Using Big Data Analytics

Retail

The retail industry is effectively conveying Big Data Analytics. They are applying the procedures of Data Analytics to understand what the consumers are purchasing and offering products and services that are tailor-made for these customers. Today, it is tied in with having an omni-channel experience. Customers may connect with a brand on one channel, at that point at last get it through another channel, then adhering to more intermediary procedures. Retailer should monitor these customer journeys, and they should send their marketing and advertising efforts dependent on that so as to improve the odds of deals and lower costs.

Innovation

Innovation organizations, offering products and services, are likewise intensely conveying Big Data Analytics. They are discovering more how the customers interact with their websites or applications and gather key information. In light of this, they are ready to advance their business, customer service, improve customer fulfillment, and then some. This likewise encourages them dispatch new products and services since today we are living in an information serious economy, and the enterprises in the innovation sector are reaping the benefits of Big Data Analytics.

Healthcare

Healthcare is another industry that can benefit a ton from Big Data Analytics tools, methods, and processes. Healthcare personnel can analyze the strength of their patients through various tests, run it through their computers, search for indications of oddities and diseases, and then some. Big Data Analytics additionally improves understanding care and increase the effectiveness of the treatment and drug processes. A few infections can be analyzed before its beginning with the goal that the measures can be taken in a preventive manner rather than a remedial manner.

Manufacturing

Manufacturing is an industrial sector that is associated with creating physical merchandise. The existence pattern of a manufacturing process can vary from product to product. The manufacturing frameworks are included within the industry arrangement and across the manufacturing floor. There are a great deal of advancements that are included like Internet of Things, Robotics, and others, however the foundation of each of these is firmly founded on Big Data Analytics. Utilizing Big Data Analytics, manufacturers can improve the yield, reduce an opportunity to market, upgrade the quality, enhance the gracefully chain and coordinations process, and construct prototypes before the dispatch of products in order to understand all the suggestions. Throughout every one of these means, Big Data Analytics helps the manufacturers.

Energy

A large portion of the oil and gas organizations which go under the energy sector are big users of Big Data Analytics. With regards to discovering oil and resources, a great deal of Big Data Analytics is conveyed. Likewise, the market is very unstable for the petroleum products. In this way, there is tremendous measures of Big Data Analytics that goes into discovering what the price of a barrel of oil will be, what the yield ought to be, and if an oil well will be profitable or not. Big Data Analytics is additionally conveyed in discovering the hardware failures, send predictive support, and ideally utilize the resources so as to reduce the capital expenditure.

 

Related Posts