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.
As its full name suggests (Structured Query Language), SQL is responsible for querying and modifying information stored in a specific database management system.
In AI and machine learning projects, instead of processing raw data directly, it is necessary to make it more meaningful and processable. An important concept that comes into play at this point is Embedding.
This process, known as database shrinking, is a form of compression. It is intended to reduce the overall space without interfering with the data.
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