Along with digital transformations and adopting more Internet of Things components into our manufacturing system, comes a ton of data that needs to be stored, processed, and served to different locations or utilized in other systems. A modern factory setup can generate millions if not billions of data. The conventional methods of storage in traditional databases, processing data and serving it right before you need it, does not always work with this amount of data. So, how can a modern factory effectively and efficiently store, crunch, and deliver all that data so delays are minimal? Enter Data Lakes.
What Are Data Lakes?
Data Lakes are made up of centralized systems that stockpile, refine, and secure larger amounts of both structured and unstructured data. A Data Lake receives data in its original format. That means that the data contained there can be accessed and refined through multiple separate processes without damaging that original data. That processed data can then be stored back into the Data Lake to use later in whatever format it’s in. Data Lakes don’t care about the data that is fed into them, which is perfect when that data is coming from so many different sources. In addition, Data Lakes can have different user permissions set to access only certain types of data, so you can secure the data you need to.
How Do Data Lakes Benefit Industry 4.0 Enabled Manufacturers?
How are Data Lakes beneficial to manufacturers who are running or looking to run Industry 4.0 setups? For one, they are flexible. They can store any type of data, so getting new systems setup, and implemented is that much easier. Second, they can help with predictive maintenance. Predictive maintenance requires coordinating people, processes, data, and technology expertise. Being able to store very large amounts of data, and have it easily accessible will go a long way to predicting maintenance needs. Finally, data lakes can help in determining the viability of an upgrade or update to the system. All that centralized data means that you don’t have to extract it from various systems when it’s time to analyze it. In the case of integrating a new system, you have accessible data you can easily compare to data from the new setup.
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