Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory

Azure Synapse Pipelines and Azure Data Factory are robust data integration services provided by Microsoft Azure. These services are essential for data engineers preparing for the Data Engineering on Microsoft Azure exam, as they enable efficient ingestion and transformation of data. In this article, we will dive into the features and capabilities of both Azure Synapse Pipelines and Azure Data Factory.

Azure Synapse Pipelines

Azure Synapse Pipelines is a cloud-based data integration service designed to create, schedule, and orchestrate data-driven workflows. Its serverless architecture makes it a scalable and cost-effective solution for ingesting and transforming data. Pipelines can be authored using a visual interface or code.

To create a pipeline in Azure Synapse Pipelines, you have two options: utilizing the Azure Synapse Studio web interface or writing code in JSON-based format known as a pipeline manifest. Let’s take a look at a basic pipeline manifest example:

{
"name": "MyPipeline",
"properties": {
"activities": [
{
"name": "CopyActivity",
"type": "Copy",
"linkedServiceName": {
"referenceName": "MyLinkedService",
"type": "LinkedServiceReference"
},
"inputs": [
{
"referenceName": "MyInputDataset",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "MyOutputDataset",
"type": "DatasetReference"
}
]
}
],
"annotations": []
}
}

In the code snippet provided, we define a pipeline named “MyPipeline” with a single activity called “CopyActivity”. This activity copies data from an input dataset to an output dataset. Linked services and datasets define the sources and destinations for the pipeline.

Azure Data Factory

Azure Data Factory is a cloud-based Extract, Transform, Load (ETL) service that enables the creation, scheduling, and management of data integration workflows. It allows ingestion of data from various sources, transformation, and loading into the desired destination. Data Factory supports both code-free and code-first authoring experiences.

Similar to Azure Synapse Pipelines, you can author Data Factory pipelines using the web interface or by writing JSON-based code. Let’s explore an example of a Data Factory pipeline that copies data from an Azure Blob Storage to an Azure SQL Database:

{
"name": "MyDataFactoryPipeline",
"properties": {
"activities": [
{
"name": "CopyDataActivity",
"type": "Copy",
"inputs": [
{
"name": "MyBlobDataset"
}
],
"outputs": [
{
"name": "MySqlDataset"
}
],
"typeProperties": {
"source": {
"type": "BlobSource"
},
"sink": {
"type": "SqlSink"
}
}
}
]
}
}

In this example, we define a pipeline named “MyDataFactoryPipeline” with a single activity named “CopyDataActivity”. The activity utilizes a Blob dataset as the source and a SQL dataset as the sink. The “typeProperties” section specifies the specific source and sink types.

Conclusion

Azure Synapse Pipelines and Azure Data Factory are indispensable tools for efficiently ingesting and transforming data on Microsoft Azure. In this article, we explored the fundamentals of creating pipelines using both services. While the provided examples are simple, remember that Azure Synapse Pipelines and Azure Data Factory offer a wide range of capabilities to handle complex data integration scenarios. By leveraging these services, data engineers can effectively manage their data workflows and confidently tackle the Data Engineering on Microsoft Azure exam.