Raw Data Storage vs Processed Data Storage
Developers should use Raw Data Storage when building systems that require historical data integrity, such as analytics platforms, machine learning pipelines, or compliance-driven applications meets developers should learn about processed data storage when building data-intensive applications, analytics platforms, or etl (extract, transform, load) pipelines, as it ensures data is stored in a usable state for downstream tasks. Here's our take.
Raw Data Storage
Developers should use Raw Data Storage when building systems that require historical data integrity, such as analytics platforms, machine learning pipelines, or compliance-driven applications
Raw Data Storage
Nice PickDevelopers should use Raw Data Storage when building systems that require historical data integrity, such as analytics platforms, machine learning pipelines, or compliance-driven applications
Pros
- +It enables reprocessing of data with new algorithms or schemas without loss of information, making it ideal for scenarios where data usage patterns are unpredictable or evolving
- +Related to: data-lakes, data-warehousing
Cons
- -Specific tradeoffs depend on your use case
Processed Data Storage
Developers should learn about Processed Data Storage when building data-intensive applications, analytics platforms, or ETL (Extract, Transform, Load) pipelines, as it ensures data is stored in a usable state for downstream tasks
Pros
- +It is crucial in scenarios like real-time dashboards, where pre-aggregated data speeds up queries, or in machine learning workflows, where cleaned datasets are needed for model training
- +Related to: data-warehousing, etl-pipelines
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Raw Data Storage if: You want it enables reprocessing of data with new algorithms or schemas without loss of information, making it ideal for scenarios where data usage patterns are unpredictable or evolving and can live with specific tradeoffs depend on your use case.
Use Processed Data Storage if: You prioritize it is crucial in scenarios like real-time dashboards, where pre-aggregated data speeds up queries, or in machine learning workflows, where cleaned datasets are needed for model training over what Raw Data Storage offers.
Developers should use Raw Data Storage when building systems that require historical data integrity, such as analytics platforms, machine learning pipelines, or compliance-driven applications
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