Skip to main content

What is Big Data ?

What is Big Data ?
It is now time to answer an important question – What is Big Data?

Big data, as defined by Wikipedia, is this:
“Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharingstorage,transfervisualizationquerying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set.”
In simple terms, Big Data is data that has the 3 characteristics that we mentioned in the last section –
• It is big – typically in terabytes or even petabytes
• It is varied – it could be a traditional database, it could be video data, log data, text data or even voice data
• It keeps increasing as new data keeps flowing in
This kind of data is becoming common place in many fields including Science, public administration and business.
The ability to harness such data for better decision making is therefore in great demand in today’s world.

Where is Big Data Used ?
Big Data is most prevalent in consumer-centric industries that typically generate large volumes of data. Examples of
such industries are –
• Consumer products such as Proctor & Gamble
• Credit card and Insurance such as Capital One and Progressive Insurance
• E-commerce companies such as Amazon, Netflix and Flipkart
• Travel and leisure such as United Airlines and Caesars Casino
• Public utilities such as electricity companies
Big Data is also becoming increasingly important in industries such as –
• Telecom
• Media and Entertainment
• Education
• And healthcare
Within each of these industries, Big Data can be applied to various functions such as –
• Marketing – for example social media analysis to understand customer pulse
• Supply chain – for example better inventory management through GPS data analysis
• Finance – for example for fraud control
• Manufacturing – for example, to link manufacturing operations with the supply chain for better optimization
In this section, you have seen industries and functions where Big Data is making a significant impact.
Now let us get an overview of some of the technologies that are driving the Big Data revolution.

Comments

Popular posts from this blog

Hadoop As Big Data

Introduction: In this blog, I will discuss Big Data, its characteristics, different sources of Big Dataand some key components of Hadoop Framework. In the two part blog series, I will cover the basics of Hadoop Ecosystem. Let us start with Big Data and its importance in Hadoop Framework. Ethics, privacy, security measures are very important and need to be taken care while dealing with the challenges of Big Data. Big Data: When the Data itself becomes the part of the problem. Data is crucial for all organizations. It has to be stored for future use. We can refer the term Big Data as the data, which is beyond the storage capacity and the processing power of an organization. What are the sources of this huge data? There are different sources of data such as the social networks, CCTV cameras, sensors, online shopping portals, hospitality data, GPS, automobile industry etc., that generate data massively. Big Data can be characterized as: The Volume of the Data Velocit...

Machine Learning & Deep Learning

Machine Learning & Deep Learning Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two concepts you’ve likely heard of before:  machine learning  and  deep learning . These terms are often thrown around in ways that can make them seem like interchangeable buzzwords, hence why it’s important to understand the differences. And those differences should be known! Examples of machine learning and deep learning are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook knows whose face is in a photo. And it’s how a customer service representative will know if you’ll be satisfied with their support before you even take a customer satisfaction (CSAT) survey. So what are these concepts that dominate the conversations about artificial intelligence and how exactly are they different? What is machine learning? Here’s a basic definition of machine learn...

Automatic Builds With GCP Cloud Build

Automatic Builds With GCP Cloud Build If you are looking for an easy way to automatically build your application in the cloud, then maybe Google Cloud Platform (GCP) Cloud Build is for you. In this post, we will build a Spring Boot Maven project with Cloud Build, create a Docker image for it, and push it to GCP Container Registry. 1. Introduction Cloud Build is the build server tooling of GCP, something similar as Jenkins. But, Cloud Build is available out-of-the-box in your GCP account and that is a major advantage. The only thing you will need is a build configuration file in your git repository containing the build steps. Each build step is running in its own Docker container. Several cloud builders which can be used as a build step are generally available. You can read more about Cloud Build on the  overview  and  concepts  website of GCP. There are three categories of build steps: Official  cloud builders provided by GCP; Community  cloud ...