Hello friends, If you are talking about data, database , data-warehouse or big data nothing will complete without schema. Schema plays an important role in Data Platform. Today I am exploring about Schema on write and schema on read in respect of datawarehouse and data lake. Let see differences.
Schema on write
Schema on write
- Structured Data,
- RDBMS,
- OLAP / Data-warehouse.
- Heavy ETL (extract-transform-load) role in data movement.
- Change in data-model is costly.
- work well in range of Data Mart.
- User have set of questions.
- Business Analysis.
- Collect Data - Apply Schema - Write Data - Analyze.
Schema on read
- Structure & Un-structured Data.
- RDBMS, NoSQ & Hadoop.
- BigData / Data Lake.
- ELT (extract-load-transform) & Low cost extraction.
- Schema is just a structured file can be switched dynamically.
- Ideal for large volume of data.
- User is exploring data without pre-defined query.
- Data science & Research.
- Collect Data - Write Data - Apply Schema- Analyze.
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