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Data Engineering
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Why Azure Databricks needs to store data in temp storage in Azure before writing to the synapse.

Ajay-Pandey
Esteemed Contributor III

I was following the tutorial about data transformation with azure databricks, and it says before loading data into azure synapse analytics, the data transformed by azure databricks would be saved on temp storage in azure blob storage first before loading into azure synapse analytics. Why the need to save it to a temp storage before loading into azure synapse analytics?

Ajay Kumar Pandey
2 REPLIES 2

Anonymous
Not applicable

@Ajay Pandey​ 

Saving the transformed data to temporary storage in Azure Blob Storage before loading into Azure Synapse Analytics provides a number of benefits to ensure that the data is accurate, optimized, and performs well in the target environment.

  1. Data Validation: Saving the transformed data to temporary storage provides an opportunity to validate the data before loading it into Azure Synapse Analytics. This helps to ensure that the data is in the correct format, has the correct schema, and is free of any errors that could cause problems during the load process.
  2. Data Optimization: Saving the transformed data to temporary storage can allow for additional data processing steps to be performed before loading the data into Azure Synapse Analytics. For example, you might want to perform additional transformations or apply data compression techniques to reduce the amount of storage space required for the data.
  3. Performance Optimization: Loading data from temporary storage can be faster than loading data directly from Azure Databricks. This is because temporary storage can be optimized for data loading, whereas Azure Databricks is optimized for data processing. Additionally, temporary storage can be located closer to Azure Synapse Analytics, which can reduce network latency and improve performance.
  4. Data Versioning: Saving the transformed data to temporary storage provides a record of the data at a specific point in time. This can be useful for tracking changes to the data over time and for ensuring that the correct version of the data is loaded into Azure Synapse Analytics.

Ajay-Pandey
Esteemed Contributor III

Thanks for reply.

Ajay Kumar Pandey

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