IoT solutions are generally data-driven and have considerable high volume. Imagine each car having a device and each one is sending messages every five seconds. If there are a million cars sending messages, it will be equal to 288 million messages per day and 8 billion messages per month. Together this data has lots of hidden information and insights; however, making sense of this kind of data is quite difficult by mere viewing. The data captured and stored can be consumed for solving business problems. Depending on the nature of the problem, not all data captured is of importance. There could be a subset of data that should be used for solving a problem. The data captured and stored could not be consistent as well. To ensure that the data is consistent, not biased or skewed, appropriate transformation should be executed on it for making it readily consumable. This process is typically known as transformation. Here, data is filtered, sorted, removed, enriched, and transformed to a structure; further downstream components and applications can consume readily.