What is Big Data – Importance and Use Cases
This tutorial will answers questions like what is Big data, why to learn big data, why no one can escape from it. We will also discuss why industries are investing heavily in this technology, why professionals are paid huge in big data, why the industry is shifting from legacy system to big data, why it is the biggest paradigm shift IT industry has ever seen, why, why and why???
What is Big Data – Use Cases and Need
To get an answer to Why You should learn Big Data? Let’s start with what industry leaders say about Big Data:
Industries today are searching new and better ways to maintain their position and be prepared for the future. According to experts, Big Data analytics provides leaders a path to capture insights and ideas to stay ahead in the tough competition.
If these professionals can make a switch to Big Data, so can you:
What is Big Data Analytics?
So, What is Big data? Different publishers have given their own definition for Big data to explain this buzzword.
In other words, big data gets generated in multi terabyte quantities. It changes fast and comes in varieties of forms that are difficult to manage and process using RDBMS or other traditional technologies. Big Data solutions provide the tools, methodologies, and technologies that are used to capture, store, search & analyze the data in seconds to find relationships and insights for innovation and competitive gain that were previously unavailable.
80% of the data getting generated today is unstructured and cannot be handled by our traditional technologies. Earlier, an amount of data generated was not that high. We kept archiving the data as there was just need of historical analysis of data. But today data generation is in petabytes that it is not possible to archive the data again and again and retrieve it again when needed as data scientists need to play with data now and then for predictive analysis unlike historical as used to be done with traditional.
It is saying that- “An image is a worth of thousand words“. Hence we have also provided a video tutorial for more understand what is Big data and its need.
After learning what is analytics. Let us now discuss various use cases of Big data. Below are some of the Big data use cases from different domains:
There are lots of technologies to solve the problem of Big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, etc. Let’s take an overview of these technologies in one by one-
Big data is creating a Big impact on industries today. Therefore the world’s 50% of the data has already been moved to Hadoop. It is predicted that by 2017, more than 75% of the world’s data will be moved to Hadoop and this technology will be the most demanding in the market as it is now.
Further enhancement of this technology has led to an evolution of Apache Spark– lightning fast and general purpose computation engine for large-scale processing. It can process the data up to 100 times faster than MapReduce.
iii. Apache Kafka
Apache Kafka is another addition to this Big data Ecosystem which is a high throughput distributed messaging system frequently used with Hadoop.
IT organizations have started considering Big data initiative for managing their data in a better manner, visualizing this data, gaining insights of this data as and when required and finding new business opportunities to accelerate their business growth. Every CIO wants to transform his company, enhance their business models and identify potential revenue sources whether he being from the telecom domain, banking domain, retail or healthcare domain etc. Such business transformation requires the right tools and hiring the right people to ensure right insights extract at right time from the available data.
Hence, Big Data is a big deal and a new competitive advantage to give a boost to your career and land your dream job in the industry!!!
Hope this blog helped you to understand what is big data and the need to learn its technologies. If you have any other questions so please let us know by leaving a comment in a section given below.