The Evolution of Data Lakes

insidebigdata | June 18, 2018

The Evolution of Data Lakes
The Internet of Things (IoT) and Machine Learning are key aspects of Industry 4.0. Both these technologies will result in the unprecedented collection and analysis of data to drive new insights and benefits for manufacturers. Interestingly, there is nothing particularly new about wanting to use manufacturing data to drive improvements. What is new is the transition away from the large data preparation effort that was often a large portion of Data Warehouse and even Big Data efforts. Data from disparate systems often went through multiple levels of aggregation and indexing in order to prepare it for answering traditional questions. That type of aggregation is no longer necessary. Manufacturers should be planning for all enterprise data sets to be part of the greater data lake. A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data structure and requirements are not defined until the data is needed (unlike a traditional database). The transition to a data lake emphasizes flexible access to analysis tools and makes the process less centered on data preparation. By definition, the data lake will be made up of a variety of data sources and the accessibility requirements and effort will only be defined at the time of the query. Information may be housed in traditional ERP data that can be a core part of the Industry 4.0 effort. For example, flexible manufacturing assets can be used to produce many different SKUs.

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