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  Photo by Gül Işık Handling large binary data in Azure Synapse When dealing with large binary data types like geography or image data in Azure Synapse, you may encounter challenges due to limitations in supported data types and column sizes. Let's take the example of a City table with a Location column holding geography data, which needs to be converted to a varbinary type during loading since Azure Synapse doesn't natively support geography types. Example: 1. Convert to varbinary: During loading, convert the geography data to varbinary. 2. Data Chunking: Since PolyBase supports varbinary up to 8000 bytes, data may get truncated. To overcome this, split the data into manageable chunks. 3. Temporary Staging: Create a temporary staging table for the Location column. 4. Chunk Processing: Split the location data into 8000-byte chunks for each city, resulting in 1 to N rows for each city. 5. Reassembly: Reassemble the chunks using T-SQL PIVOT operator to convert rows into colum...

  pexel Let first discuss what is incremental loading into the data warehouse by ETL from different data sources including databases. Incremental Loading into Data Warehouses: Incremental loading is crucial for efficiently updating data warehouses without reprocessing all data. It involves adding only new or modified data since the last update. Key aspects include: 1. Efficiency: Incremental loading reduces processing time and resource usage by only handling changes. 2. Change Detection: Techniques like timestamp comparison or change data capture (CDC) identify modified data. 3. Data Consistency: Ensure consistency by maintaining referential integrity during incremental updates. 4. Performance: Proper indexing, partitioning, and parallel processing enhance performance during incremental loads. 5. Logging and Auditing: Logging changes ensures traceability and facilitates error recovery in incremental loading processes. Incremental Loading Explained In contrast to a full load,...