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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.
Here are the key steps to design and implement incremental loads using Azure Data Factory:
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.
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.
a. Azure Synapse Analytics
b. Azure Data Factory
c. Azure Databricks
d. All of the above
Answer: d. All of the above
a. Loading the entire dataset from the source system to the destination every time
b. Loading only the updated or new records from the source system to the destination
c. Loading the entire dataset and applying transformations on the destination
d. Loading only the schema definitions from the source system to the destination
Answer: b. Loading only the updated or new records from the source system to the destination
a. Pipelines
b. Data flows
c. Factories
d. Triggers
Answer: b. Data flows
a. Copy activity
b. Execute pipeline activity
c. Lookup activity
d. If condition activity
Answer: a. Copy activity
Answer: False
a. PolyBase
b. Data Lake Storage Gen2
c. Data Flow transformations
d. Copy activity
Answer: a. PolyBase
a. Incremental table
b. Staging table
c. Fact table
d. Change tracking table
Answer: d. Change tracking table
Answer: False
a. Merge operations
b. Parquet file format
c. Data skipping
d. Streaming capabilities
Answer: a. Merge operations
a. read()
b. load()
c. modified()
d. delta()
Answer: c. modified()
1 Reply to “Design and implement incremental loads”
Suggested corrections
– Which Azure Data Factory component can be used to perform incremental loads? Answer – Pipelines
– In Azure Data Factory, you can use change tracking to identify the updated or new records for incremental loads. – True