A data analytics team is tasked with building a repository for thousands of high-resolution video recordings. The goal is to store these large files cost-effectively and prepare them for future machine learning analysis. Which storage approach is BEST for managing this large volume of video data while enabling flexible analysis?
Convert each video into a CSV file containing frame details in text columns
Implement a star schema that maps each recording to integer fields in dimension tables
Attach the recordings to records within a transactional database table as binary fields
Store the recordings in an unstructured data system capable of holding large binary objects
Storing video content in an unstructured data store that handles large binary objects is well-suited for later retrieval, indexing, and analysis. Reformatting video files into CSV rows or using a transactional system with limited binary support can cause performance or capacity problems. Using a star schema is generally intended for structured information and is not optimal for extensive multimedia storage.
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What is an unstructured data system, and why is it suited for large binary objects?
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Why is a star schema not ideal for storing video files?
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What are the limitations of storing video files in a transactional database table as binary fields?