From a very generic perspective it does not matter how small or big the company is, it depends on the amount of data the type of data and how are they using the data. As per the recent trend, the need to handle with large amount of data is increased. As per the business requirements we are not able to achieve 100% accuracy when we are dealing with normal data conversions. As Hadoop is introduced in the recent present and since it can deal with huge data, accuracy of the data is achieved 100%.
Now everyone moving towards Analytics and nobody wants to lose the data this results to huge data, so how do we manage these data and how do we process this much data in a short time? To do this you write a program which may run hours or days together, to improve the performance to process the data quickly just think how you write code. You write code that runs parallelly still not better performance, now you write code and run in different machines again you have sync'd all these machines to make sure all the data are read and processed properly. Finally, to do all these it takes lot of development time. Solution for all the above is Hadoop which runs parallelly and distributed systems. Hadoop gives you the high-level framework where you can develop the application quickly with very good performance. So, the companies don't want to waste time in developing our own code they need very fast productivity this reason makes Companies to hire Hadoop people. Data is gathered from your activities on social media and by providing your information to different apps and websites. Data is also being gathered from your activities other than on 'your' mobile or desktop; like when you visit to a hospital, shopping mall, retail store, petrol pump, banks and even restaurants and movie theatres. CCTV cameras and different sensors also generate data.
We can better understand with the help of example in the connected cars data collected from the sensors in the car and inferences can be drawn regarding consumer behaviour. For example, to know if there is a link between the music people listen to and drive through restaurants they frequent. There is also a good potential for autonomous driving to keep our roads safer. For this to become a reality, they require Big Data. These vehicles are embedded with sensors measuring everything from position, speed, direction to breaking of traffic signals, pedestrian proximity and hazards. Using this data, the vehicle can make decisions and carry out appropriate responses devoid human error. These kinds of connections can help safety decision making, product designing, advertising resource allocations and budgets, and thus the information gathered from different sources is invaluable commercially.
Big data is huge in number, less structured and heterogenous. There are many challenges with big data like:
1. Recognition of data
2. Innovative ways to execute the problems associated with big data.
3. Effective and efficient ways to contextualize the data so that it is relevant and to specific individuals or groups.
4. Its very tough to analyse and visualize the results of big data.
5. Storage streaming and processing of big data to extract insights from it.
But technologies have come up with solutions to these problems. They are the technologies like Hadoop and Spark Hadoop. Hadoop is an open source, scalable and fault tolerant framework from Apache Software foundation and is coded in Java. Open source which means that it is available for free to everyone and its source can also be changed as per the requirements.
Hadoop processes Big data on a cluster of commodity hardware. If a certain function fails or doesn't fulfil your need, you can change it accordingly. That the reason the big data and Hadoop are in demand.