Introduction To Big Data
Big Data, as the name suggests, is something related to data, where big implies large or huge. To put simply, Big Data refers to large amounts of data (in terms of volume) that cannot be digested (processed) with traditional data processing applications in an effective way. As the data gets bigger, it also becomes more complex, and it requires more advanced and robust mathematical and statistical techniques to get what we want from data.
Here, let us try to understand the Introduction To Big Data with an example, Rewind back to 1940s, no computers, no cell phones, no internet, no digital life, so no data, right? Well, there was data, but it was not digital. There was no internet banking that time but there were banks, and banks had customers, and customer made transactions that were recorded, not digitally but on papers, accounting and finance and were all done on pen and papers.
Fast forward to 1990s, technology kicks in, computers and cell phones came into the market, income statements and balance sheets that were done on papers and stored in registers which had data of roughly 500 customers were now being done on excel and saved in drives that can store more than thousands of customers data. Here in the introduction to big data, we are going to learn that as data increased exponentially, organizations equipped themselves with more firepower to handle data more effectively. Now, on one single day, 2.5 quintillion bytes (2,500,000 Terabytes) of data is generated. That’s huge, right? With advancing technology, in the near future, almost every item in our surrounding will generate some data. We already have smart shoes, smart lights, smart pillows and other gadgets available that generate data on a daily basis. Therefore, Introduction to Big Data is one of the vital technologies that will play a major role in shaping the future world.
Main Components Of Big data
As we discussed above in the introduction to big data that what is big data, Now we are going ahead with the main components of big data.
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Machine Learning
It is the science of making computers learn stuff by themselves. In machine learning, a computer is expected to use algorithms and statistical models to perform specific tasks without any explicit instructions. Machine learning applications provide results based on past experience. For example, these days there are some mobile applications that will give you a summary of your finances, bills, will remind you on your bill payments, and also may give you suggestions to go for some saving plans. These functions are done by reading your emails and text messages.
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Natural Language Processing (NLP)
It is the ability of a computer to understand human language as spoken. The most obvious examples that people can relate to these days is google home and Amazon Alexa. Both use NLP and other technologies to give us a virtual assistant experience. NLP is all around us without us even realizing it. When writing a mail, while making any mistakes, it automatically corrects itself and these days it gives auto-suggests for completing the mails and automatically intimidates us when we try to send an email without the attachment that we referenced in the text of the email, this is part of Natural Language Processing Applications which are running at the backend.
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Business Intelligence
Business Intelligence (BI) is a method or process that is technology driven to gain insights by analyzing data and presenting it in a way that the end users (usually high-level executives) like managers and corporate leaders can gain some actionable insights from it and make informed business decisions on it.
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Cloud Computing
If we go by the name, it should be computing done on clouds, well, it is true, just here we are not talking about real clouds, cloud here is a reference for the Internet. So we can define cloud computing as the delivery of computing services—servers, storage, databases, networking, software, analytics, intelligence and more—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Characteristics Of Big Data
In this topic of Introduction To Big Data, we also show you the characteristics of Big Data.
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Volume:
In order to determine value out of data, the size needs to be considered, which plays a crucial part. Also, in order to identify if a particular type of data falls under the introduction to Big Data category or not, depends on volume.
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Variety:
Variety means different types of data according to their nature (structured and unstructured). Earlier, the only sources of data considered by most of the applications were in form of rows and columns which usually came in spreadsheets and databases. But nowadays, data comes in every form we can imagine like emails, photos, videos, audio, and many more.
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Velocity:
Velocity as the name suggests the speed of generation of data. From a source, how rapidly data can be generated and how fast it can be processed, determines the potential of the data.
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Variability:
Data can be variable, means it can be inconsistent, not in the flow, that interferes or becomes a blockage in handling and managing data in an effective way.
Applications Of Big Data
Big Data analytics are being used in the following ways
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Health Care:
We have these days’ wearable devices and sensors that provide real-time updates to the health statement of a Patient.
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Education:
A student’s progress can be tracked and improved by proper analysis through big data analytics.
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Weather:
Weather sensors and satellites, which have been deployed around the globe collect data huge amounts and use that data to monitor the weather and environmental conditions and also predict or forecast the weather conditions for the upcoming few days.
Advantages and Disadvantages Of Big Data
As we have studied the introduction to big data now we are going to understand the Advantages and Disadvantages of Big Data are as follows :
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Advantages |
Disadvantages |
| Better decision-making | Data quality: the quality of data needs to be good and arranged to proceed with big data analytics. |
| Increased productivity | Hardware needs: Storage space that needs to be there for housing the data, networking bandwidth to transfer it to and from analytics systems, are all expensive to purchase and maintain the Big Data environment. |
| Reduce costs | Cybersecurity risks: Storing sensitive and large amounts of data, can make companies a more attractive target for cyberattackers, which can use the data for ransom or other wrongful purposes. |
| Improved customer service | Hiccups in integrating with legacy systems: Many old enterprises that have been in business from a long time have stored data in different applications and systems throughout in different architecture and environments. This creates problems in integrating outdated data sources and moving data, which further adds to the time and expense of working with big data. |
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