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What are the steps or processes to test big data applications? We answer all this and more in our big data testing tutorial below. This will be difficult to handle with traditional databases. Data is created at high speed and has to be handled and processed quickly. Data comes in all formats. Storing data without analyzing it to gain meaningful insights from the data would be a waste of resources. Before we look at testing of big data it would be useful to understand how it is being used in the real world.
Amazon, Flipkart and other e-commerce sites have millions of visitors each day with hundreds of thousands of products. Amazon uses big data to store information regarding products, customer and purchases. Apart from this data is also gathered around the product searches, views, products being added to cart, cart abandonment, products that are bought together etc. All of this data is stored and processed in order to suggest products that the customer is most likely to buy. All of this data has to be processed very quickly which is not feasible with traditional databases. Social media sites generate huge amounts of data in terms of pictures, videos, likes, posts, comments etc.
Not only is data stored in big data platforms, they are also processed and analyzed to offer recommendations on content that you might like. Not only is data stored in big data platforms, they are also processed and analyzed to offer recommendations of things you may be interested in. For example, if you search for a washing machine on Amazon and go to Facebook, Facebook will show you ads for the same. This is a big data use case because, there millions of websites that advertise on Facebook and there are billions of users. Storing and processing this information to display the right advertisement to the right users cannot be accomplished by traditional databases in the same amount of time.