Meta Data is data that describes other data in a structured, consistent form, so that large amounts of data can be collected, stored, and analyzed over time.
Metadata is used to store big data in data warehouses for easy retrieval and management. A data warehouse uses structured data in data sources that are standardized, cleaned, and consistent. Metadata provides uniformity in the collection and storage of this data, so business owners and data analysts can easily access and derive insights from the data.
Effective management of meta data is a necessary part of robust and flexible big data “ecosystems,” that is, it helps companies manage their data assets efficiently and makes that data available to data scientists and other analysts.
This process, known as database shrinking, is a form of compression. It is intended to reduce the overall space without interfering with the data.
Data visualization makes it possible to quickly obtain information by visualizing data regardless of format, type or source. It also allows the presentation of information that is not visible at first glance about what the data means in a clear and clear way.
Pattern recognition occurs when an algorithm places iterations or regularities within large datasets or into different datasets.
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