How to use atypical data (texts, video, images...)?
The data lake is an ideal central repository for all your polymorphic data due to its ability to store and facilitate the exploitation of heterogeneous and unstructured data.
When building a data warehouse, dealing with complex and polymorphic data can become a challenge, especially the ingestion of non-relational data into machine learning projects. This is when a data lake can be used to maximize the amount and variety of data available in your data warehouse. Data lakes differ from data warehouses in that the data is stored without the need to transform it. Additionally, data lakes are able to easily collect and store data of all types (also known as polymorphic data), whether unstructured or semi-structured. Data lakes are also better suited for your data science and augmented analytics projects, such as text mining (used in sentiment analysis, user review analysis, document summarization, information) or image recognition. Data lakes and data warehouses are not mutually exclusive. They are mostly inclusive and complementary. They allow you to design complex datasets and derive unique insights from them.
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