The flexibility of Data Lakes enables organizations to keep a much larger volume and variety of potentially useful data than they could in traditional relational stores.
Devoid of pre-defined schemas, a Data Lake can store:
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semi-structured data containing tags and hierarchies, such as CSV files, log files, XML, and JSON, retail point-of-sales (POS), and Internet of Things (IoT)
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unstructured data, lacking pre-defined data models and form of organization, such as emails, documents, PDFs, and binary data such as images, audio, and video clips
This unmatched flexibility of Data Lakes to store raw data in any form for reuse at any time, provides enterprise organizations the ability to reuse and repurpose their raw data for their various business cases and data analysis needs today, tomorrow, and in the future.