Design and implement incremental loads

Incremental loading is an essential concept in data engineering, especially when dealing with large volumes of data. It allows you to update your data systems efficiently by only processing and loading the changes since the last load, rather than reprocessing the entire dataset. In this article, we will explore how to design and implement incremental loads in Microsoft Azure.

Azure provides various services and tools that can be used to implement incremental loads, such as Azure Data Factory (ADF), Azure Databricks, and Azure SQL Data Warehouse. We will focus on using Azure Data Factory for this article.

Key Steps

Here are the key steps to design and implement incremental loads using Azure Data Factory:

1. Identify the Source and Target Datastores: The first step is to identify the source and target datastores. The source datastore contains the data that needs to be loaded incrementally, while the target datastore is where the incremental changes will be loaded. The source can be any supported data sources such as Azure Blob Storage, Azure SQL Database, or Azure Data Lake Storage.
2. Enable Change Tracking: Change tracking is a feature that enables capturing the changes that occur in a source data store since the last load. It provides a way to identify the newly added, modified, or deleted records in the source. To enable change tracking, refer to the documentation specific to your data source, such as Azure SQL Database Change Tracking or Azure Blob Storage Change Feed.
3. Create a Pipeline in Azure Data Factory: Create a pipeline in Azure Data Factory that orchestrates the incremental load process. The pipeline should consist of the following components:
• Source Dataset: Define the dataset representing the source data store. Specify the connection details and the query to fetch the changed data using change tracking.
• Lookup Activity: Use a lookup activity to retrieve the last processed timestamp from the target datastore. This timestamp will be used as a parameter in the source dataset query to fetch only the changes that occurred after this timestamp.
• Copy Activity: The copy activity is used to copy the changed data from the source datastore to the target datastore. Configure the source and destination datasets, and ensure you enable the “Enable Staging” option. This enables Data Factory to handle incremental loads efficiently by using staging tables.
• Stored Procedure Activity (Optional): If you need to perform some transformations on the data before loading it into the target datastore, you can use a stored procedure activity. This activity can call a stored procedure in the target database, which can perform any necessary transformations or business logic.
4. Configure Incremental Updates: To ensure the incremental load process runs regularly, you can use triggers in Azure Data Factory. Triggers help automate the execution of pipelines based on a specified schedule or event. Configure a trigger to execute the pipeline at the desired frequency, such as daily or hourly, depending on your requirements.

By following these steps, you can design and implement an efficient incremental load process in Azure Data Factory. Remember to test the pipeline thoroughly and monitor its performance to ensure data integrity and reliability.

Example Pipeline JSON Structure

Here’s an example of a pipeline JSON structure for an incremental load:

{
"name": "IncrementalLoadPipeline",
"properties": {
"activities": [
{
"name": "FetchChanges",
"type": "Copy",
"inputs": [
{
"name": "SourceDataset"
}
],
"outputs": [
{
"name": "DestinationDataset"
}
],
"typeProperties": {
"source": {
"type": "Query",
"query": "SELECT * FROM SourceTable WHERE ModifiedDate > @{pipeline().parameters.lastProcessedTimestamp}"
},
"enableStaging": true
}
}
],
"parameters": {
"lastProcessedTimestamp": {
"type": "String"
}
},
"variables": {},
"triggers": {
"name": "DailyTrigger",
"type": "ScheduleTrigger",
"typeProperties": {
"recurrence": {
"frequency": "Day",
"interval": 1
},
"startTime": "2022-01-01T00:00:00Z"
}
}
}
}

In conclusion, implementing incremental loads in Azure Data Engineering using Azure Data Factory is a powerful technique to efficiently update your data systems. By following the steps mentioned above and leveraging Azure’s services, you can design a robust and scalable solution for handling incremental data updates in your organization.