With the advancement, affordability, and availability of massive hardware and systems like GPUs and TPUs, it becomes a critical necessity to squeeze every bit of these hardware capabilities to make the data science experiment iterations faster and more productive. Having an efficient, scalable as well as generic pipeline can handle a lot of hurdles to quickly create a neat prototype depending upon the type of data that we want to consume. If the problem is especially related to a deep learning context, then our data pipeline becomes one of the key elements of our entire training as well as the inference pipeline. While working with any data science problem, a data scientist has to deal with data in small, big, or huge quantities.
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And some types of data require specialized software or hardware to process properly.
Some data is easy to collect and analyze others are more difficult to get your hands on. There are different types of data out there-and not all of them are created equal. Here are some common uses for a data pipeline:ġ) Translating raw data into something that can be understood by humans and machines.Ģ) Creating reports based on the raw data.ģ) Performing calculations on the raw data so that you can see what impact those calculations will have on other parts of your business. A data pipeline is a series of processing steps that enable data flow from the source to the destination. It has three components- A source, processing steps, and a destination. What is a data pipeline? Why do we need it?Ī data pipeline is a process of taking your raw data and transforming it into the final product: a set of reports that let you analyze your data in real-time.
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