We all use Smartphones, right? But have you ever given a thought that how much data it generates in the form of text, phone calls, emails, photos, videos, searches, music and etc. Approximately 40 exabytes of data gets generated by a single smartphone user. Just multiply 40 exabytes by 5 million smartphone users and pheww we cannot even imagine or process the data in mind, which is just from Smartphone users. Other things Apart!
It’s quite a lot of data for traditional computing systems to handle and to put it in words, this massive data is what we term as Big Data.
Let’s peep into some interesting massive data generated per minute on the internet:
Snapchat: 2.1 Million snaps are shared
Google: 3.8 Million Queries
Facebook: 1.1 Million people log on to Facebook
Youtube: 4.5 million Videos
Emails: 188 Million
To put it into simple language, it is one of the ability to make massive amounts of data and derive a story from it. Now, this is a very interesting thing since years before all of this data was mostly in a blather way. But now we can analyze this data, derive predictions and detect patterns. But the entrance of distributed computing and parallel processing changed all of that!
What Big Data is used for?
Big Data analytics help businesses become more effective, which helps in reducing costs and increase profits. Here are the uses of Big Data:
- Fraud detection: This can be useful to predict and prevent cybercrimes. It also helps to handle issues related to missed transactions, and failures in net banking.
- Precision medicine: Hospitals can improve patient care by leaps and bounds. 24*7 care of patients can become a reality, and less staff-intensive. The need for direct supervision can be reduced drastically.
- Location tracking: Gathering real-time data about goods and consignments, data about weather and traffic conditions can prove helpful for logistics companies. This can help in reducing risks, improve speed and reliability in delivery.
- Entertainment & advertising: Television giants will be able to study user behavior like never before, thus providing content as per your liking. This is going to bump up the user experience.
The Three V’s of Big Data
Big data comes with three main V’s which are three defining properties of Big Data –
There are many factors which are contributing to boost the data volume. Taking example of social media, the volume here can be referred to as the data generated through websites, portals, and applications. For B2C companies Volume circumscribes the data which is out there and can be assessed for relevance.
Volume to brief is all about:
• The amount of data which is generated
• The Offline and Online Transactions
• It is saved in Tables, Records, and Files
With reference to velocity, we can refer to the speed by which this data is been generated. Keeping the example of social media, every second, every minute there is a nuclear data explosion. And here comes into picture the Big Data where it helps the company to hold on to the explosion and further process it rapidly to avoid creating the bottlenecks.
Velocity to summarize is about:
• The spend of generating the Data
• Online and Offline Data
• Is generated in Real-Time
• In streams, bits, and batches
The importance of variety in Big Data is that it refers to all unstructured (Audio recording, ECG Reading, emails, voice mails, handwritten text and etc.) and structured (texts, tweets, videos, and pictures) data. Variety in simple words is about classifying the incoming data into different categories.
• Machine-generated Readings
• Online Videos and Pictures
• Structured and unstructured data
Which big data technology is in demand?
As per the recent Trend, Hadoop Technology is highly demanded for Big Data.
It makes the best Technology and is important because –
The ability to store and process huge data quickly: With data variability and Volume which are increasing constantly especially from Social Media that’s the key consideration.
High Computing power: The computing model distributed by Hadoop processes the big data fast. The more you make use of computing nodes, the more processing power you get.
Flexibility: We need not follow the traditional ways here of pre-processing data before storing it. You can store as much data and can decide how to use it later.
Fault Tolerance: Data and application processing are protected from hardware failure. If in any case nodes goes down, the jobs are automatically redistributed to other nodes to make sure the computing does not fail and also multiple copies are stored automatically.
The Scalability: By simply adding on the nodes one can simply grow the system more to handle more data.
Low Cost: Open Source Framework is free and to store large quantity data it uses commodity hardware.
How Will Big Data And Bioinformatics Will Change Biology:
Image analysis is a big area where machine learning and big data can help. Repurposing machine-learning algorithms, the way they used to do computer vision for self-driving cars or the way Facebook uses to recognize and locate people in pictures can help in better image analysis. Big data can be used to analyze imaging data from microscopes. Access to these tools can help biologists in a great way everywhere in the world. For example, a pathologist can use these tools and techniques to look at various cancer cells and learn how these cells interact with immune cells and cancer cells.
Bioinformatics and big data are also being used to look at very high-dimensional data sets, including genomes and single-cell sequencing data. It has proved to be very helpful as it can reveal the interconnections of the networks of genes, such as which genes are dominating the expression of other genes in a gene network. This big data and bioinformatics can find the “master genes” that dominate the processes like brain development or that control the development of T cells in the immune system.