Introduction to Big data analytics
What is Big Data?
Big Data is nothing but a large volume of data. Data could be of any kind i.e. structured data like numbers, dates, group of words etc., semi-structured json, XML etc., or unstructured data like text, images, videos etc. It is so difficult to process this data using a traditional database. The data can be collected from various sources like social media, emails, banking transactions, online shopping, mobile devices, and many other sources. This data when gathered, manipulated, stored and analyzed, can help organizations to gain useful insights to increase their revenue, gain new and retain old customers and improve operations.
We can define big data as three Vs:
Volume: The amount of data that is being generated every second. Every day organizations like social media, e-commerce business, airlines collect a huge amount of data.
Velocity: The rate at which the data is generated. Social Media is being used by everybody and there will be lots of data generated every second because people do a lot of things over social media they post the comments, like the photos, share the videos etc.
Variety: Data could be of various forms structured data like numeric data, unstructured data like text, images, videos, financial transactions etc. or semi-structured data like json or XML.
What are we doing with this Big Data?
We can use this big data to process and draw some meaningful insights out of it. There are various frameworks available to process the big data. Below list provides the popular framework that is widely being used by big data developers and analysts.
Apache Hadoop: we can write map-reduce the program to process the data.
Spark: we can write spark program to process the data, using spark we can process live stream of data as well.
Apache Flink: this framework is also used to process a stream of data.
And many more like Storm, Samza.
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Big Data Analytics:
Big Data analytics is the process of collecting, organizing and analyzing a large amount of data to uncover hidden pattern, correlation and other meaningful insights. It helps an organization to understand the information contained in their data and use it to provide new opportunities to improve their business which in turn leads to more efficient operations, higher profits and happier customers.
To analyze such a large volume of data, Big Data analytics applications enables big data analyst, data scientists, predictive modelers, statisticians, and other analytical performers to analyze the growing volume of structured and unstructured data. It is performed using specialized software tools and applications. Using these tools various data operations can be performed like data mining, text mining, predictive analysis, forecasting etc., all these processes are performed separately and are a part of high-performance analytics. Using Big Data analytic tools and software enables an organization to process a large amount of data and provide meaningful insights that provide better business decisions in the future.
The key Technologies behind Big Data Analytics:
Analytics comprises various technologies that help you get most valued information from the data.
Hadoop: The open source framework that is widely used to store a large amount of data and run various applications on a cluster of commodity hardware. It has become a key technology to be used in big data because of the constant increase in the variety and volume of data and its distributed computing model provides faster access to data.
Data Mining: Once the data is stored in the data management system. You can use data mining techniques to discover the patterns which are used for further analysis and answer complex business questions. With data mining, all the repetitive and noisy data can be removed and point out only the relevant information that is used to accelerate the pace of making informed decisions.
Text Mining: With text mining, we can analyze the text data from the web like the comments, likes from social media and other text-based sources like email we can identify if the mail is spam. Text Mining uses technologies like machine learning or natural language processing to analyze a large amount of data and discover the various patterns.
Predictive Analytics: Predictive analytics uses data, statistical algorithms and machine learning techniques to identify future outcomes based on historical data. It’s all about providing the best future outcomes so that organizations can feel confident in their current business decisions.
Benefits of Big Data Analytics:
Big Data Analytics has been popular among various organizations. The organizations like e-commerce industry, social media, healthcare, Banking, Entertainment industries etc., are widely using analytics to understand various patterns, collecting and utilizing the customer insights, fraud detection, monitor financial market activities etc.
Let’s take an example of e-commerce industry:
e-commerce industry like Amazon, Flipkart, Myntra and many other online shopping sites make use of big data.
They collect customer data in several ways like
- Collect information about the items searched by the customer
- Information regarding their preferences.
- Information about the popularity of the products and many other data
Using these kinds of data, organizations derive some patterns and provide the best customer service like
- displaying the popular products that are being sold.
- show the products that are related to the products that a customer bought.
- Provide secure money transitions and identify if there are any fraudulent transactions being made.
- Forecast the demand for the products and many more.
Big Data is a game changer. Many organizations are using more analytics to drive strategic actions and offer better customer experience. A slight change in the efficiency or smallest savings can lead to a huge profit, which is why most organizations are moving towards big data.
This has been a guide to Big data Analytics. Here we have discussed basic concepts like what is Big data Analytics, it’s benefits, key technology behind Big data Analytics, etc. You may also look at the following article to learn more –