Azure Databricks Training | Data Engineering Training Hyderabad

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File Incremental Loads in ADF - Databricks & Powerbi
In Azure Data Factory (ADF), performing incremental loads is a common requirement when dealing with large datasets to minimize the amount of data transferred and improve overall performance. Incremental loads involve loading only the new or changed data since the last successful load. - Azure Databricks Training
Here are the general steps to implement file incremental loads in Azure Data Factory:
1. Identify the Incremental Key: Determine a column or set of columns in your data that can be used as a unique identifier to identify new or changed records. This is often referred to as the incremental key.
2. Maintain a Last Extracted Value: Store the last successfully extracted value for the incremental key. This can be stored in a database table, Azure Storage, or any other suitable location. A common practice is to use a watermark column to track the last extraction timestamp. - Data Engineering Training Hyderabad
3. Source Data Query: In your source dataset definition in ADF, modify the query to filter data based on the incremental key and ...
... the last extracted value.
For example, if you're using a SQL database, the query might look like:
```sql
SELECT *
FROM YourTable
WHERE IncrementalKey > @LastExtractedValue
```
4. Use Parameters: Define parameters in your ADF pipeline to hold values like the last extracted value. You can pass these parameters to your data flow or source query.
5. Data Flow or Copy Activity: Use a data flow or copy activity to move the filtered data from the source to the destination. Ensure that the destination data store supports efficient loading for incremental data.
6. Update Last Extracted Value: After a successful data transfer, update the last extracted value in your storage (e.g., a control table or Azure Storage).
7. Logging and Monitoring: Implement logging and monitoring within your pipeline to track the progress of incremental loads and identify any issues that may arise. - Azure Data Engineering Training in Ameerpet
Here's a simple example using a parameterized query in a source dataset:
```json
{
"name": "YourSourceDataset",
"properties": {
"type": "AzureSqlTable",
"linkedServiceName": {
"referenceName": "YourAzureSqlLinkedService",
"type": "LinkedServiceReference"
},
"typeProperties": {
"tableName": "YourTable",
"sqlReaderQuery": {
"value": "SELECT * FROM YourTable WHERE IncrementalKey > @LastExtractedValue",
"type": "Expression"
}
}
}
}
```
Remember that the specific implementation may vary based on your source and destination data stores. Always refer to the official Azure Data Factory documentation. - Microsoft Azure Online Data Engineering Training

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