Azure Data Factory (ADF) and Azure Synapse pipelines are fairly simple yet powerful tools for data movement, especially when paired with Microsoft Azure Cosmos DB as a data store. As part of preparing for your DP-420 exam, it’s critical to understand how to use these tools to design and implement native applications effectively.
Azure Data Factory
Azure Data Factory is a cloud-based data integration service. It allows you to create data-driven workflows for orchestrating and automating data movement and transformation.
Here’s an example of how to move data using Azure Data Factory:
- Create a pipeline in Azure Data Factory.
- Add a Copy activity to the pipeline.
- In the activity, set your source to the data you want to move and the destination to Azure Cosmos DB.
- Run the pipeline.
Moving Data with Azure Data Factory
In Azure Data Factory, moving data to Cosmos DB can be done using the ‘Copy Activity.’ Here’s a sample JSON code for a Copy Activity moving data from Blob Storage to Cosmos DB:
{
"name": "CopyFromBlobToCosmos",
"properties": {
"activities": [
{
"name": "CopyData",
"type": "Copy",
"inputs": [
{
"referenceName": "
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "
"type": "DatasetReference"
}
],
"typeProperties": {
"source": {
"type": "BlobSource"
},
"sink": {
"type": "DocumentDbCollectionSink",
"writeBehavior": "upsert"
}
}
}
]
}
}
This JSON script creates a copy activity named ‘CopyData’ that takes data from the referenced Blob source and moves (‘upserts’) it to the referenced Cosmos DB sink.
Azure Synapse Pipelines
Azure Synapse is an analytics service that brings together data warehousing and Big Data analytics. It features pipelines for data integration, similar to ADF, but more tightly integrated with data warehousing and analysis services.
Here is how you would move data using Synapse pipelines:
- Set up a data flow in Synapse Studio.
- Add a source for your data and set it to where your current dataset resides.
- Add a sink, setting it to Azure Cosmos DB.
- Configure any necessary transformations.
- Run the data flow.
Moving Data with Azure Synapse Pipelines
Running a data flow in Azure Synapse to move data from the Azure Data Lake Store to Cosmos DB can be configured in much the same way:
- In the ‘Transform data’ tab of Synapse Studio, select ‘+ New’ to create a new data flow.
- Add a source, configure it to your Azure Data Lake.
- Add a sink, configure it to your Cosmos DB instance.
- Add any necessary transformations, then publish and run the data flow.
One key difference between Azure Data Factory and Synapse pipelines is that Synapse is integrated with Azure Data Lake, Azure Databricks, and Power BI, making it more suitable for advanced analytics workloads and bringing your big data analytics and data warehousing capabilities together in one platform.
Below is a comparative analysis of using Azure Data Factory and Azure Synapse pipelines:
| Features | Azure Data Factory | Azure Synapse |
| Data Integration | Highly effective | Effective |
| Serverless | Available | Partial Availability |
| Integration with Other Azure Services | Limited | Broad |
| Suitable for Big Data and Analytics | Partially Suitable | Highly Suitable |
| Integrated with Power BI | No | Yes |
Both Azure Data Factory and Azure Synapse pipelines provide efficient ways to move data in and out of Cosmos DB. However, the choice between the two largely depends on your specific use case and requirements. Understanding these differences is important for your DP-420 exam preparation and in designing and implementing native applications using Microsoft Azure Cosmos DB.
Practice Test
True or False: Azure Data Factory is used to create and schedule data-driven workflows, but it can’t move data from one place to another.
- True
- False
Answer: False
Explanation: Azure Data Factory does not only create and schedule data-driven workflows, but it can also be used to move and transform data from one place to another.
Which of the following are true about the Azure Data Factory and Azure Synapse Pipelines?
- A) They both allow data transformation
- B) They can only move data within Azure
- C) They both support data movement and transformation tasks
- D) They require manual coding for each pipeline
Answer: A, C
Explanation: Both Azure Data Factory and Azure Synapse Pipelines support data movement and transformation tasks. They also allow data transformation but they don’t require manual coding for each pipeline and can move data outside of Azure as well.
True or False: Azure Data Factory supports moving data to and from Azure Cosmos DB.
- True
- False
Answer: True
Explanation: Azure Data Factory does support moving data to and from Azure Cosmos DB. It helps in migration and analytic solutions.
Which Microsoft service would you use to automate and orchestrate data movement and data transformation?
- A) Azure Synapse Pipelines
- B) Azure Machine Learning
- C) Azure IoT Hub
- D) Azure Data Lake
Answer: A) Azure Synapse Pipelines
Explanation: Azure Synapse Pipelines helps in automating and orchestrating data movement and data transformation.
True or False: Azure Synapse Pipelines is only compatible with Azure data services.
- True
- False
Answer: False
Explanation: Azure Synapse Pipelines is not only compatible with Azure data services but also supports a broad range of other systems and databases including both cloud and on-premises.
What is the main purpose of Azure Data Factory?
- A) Data movement
- B) Data transformation
- C) Both Data movement and transformation
- D) Data analysis
Answer: C) Both Data movement and transformation.
Explanation: Azure Data Factory can be used for both, data movement (ETL) and data transformation processes.
True or False: Azure Synapse Analytics can be integrated with Azure Data Factory to create and run data-driven workflows.
- True
- False
Answer: True
Explanation: Azure Synapse Analytics integrates with Azure Data Factory to provide the capabilities of creating, scheduling and management of data driven workflows.
Azure Synapse Pipelines provides ________________.
- A) Data Movement
- B) Data transformation
- C) A and B
- D) Data analysis
Answer: C) A and B
Explanation: Azure Synapse Pipelines provides both data movement and data transformation capabilities.
True or False: Azure Data Factory enables to move data into Azure Cosmos DB, but not out of it.
- True
- False
Answer: False
Explanation: Azure Data Factory can move data both to and from Azure Cosmos DB.
Which Azure feature allows for migration of data into Azure Cosmos DB?
- A) Azure SQL Database
- B) Azure Table Storage
- C) Azure Data Factory
- D) Azure Batch
Answer: C) Azure Data Factory
Explanation: Azure Data Factory supports a variety of migration scenarios and allows data to be moved into Azure Cosmos DB.
Interview Questions
What is Azure Data Factory?
Azure Data Factory is a cloud-based data integration service that provides the ability to orchestrate and automate data movement and data transformation.
What is Azure Synapse Pipeline?
Azure Synapse Pipeline is a service for creating, scheduling, and managing data-driven workflows and orchestrating the movement and transformation of data at scale.
How can Azure Data Factory be used in conjunction with Azure Synapse?
Azure Data Factory can be used to construct ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes to integrate data from various sources. These data sources can then be loaded into Azure Synapse for further processing and analytics.
What is the maximum volume of data that can be moved using Azure Data Factory?
Azure Data Factory does not have an explicit limit on the volume of data it can move, it allows transferring massive amounts of data in and out of various data stores very quickly and efficiently.
Can Azure Data Factory copy data to Azure Cosmos DB?
Yes, Azure Data Factory supports Azure Cosmos DB as both a source and a sink (destination).
What type of tasks can be performed by Azure Synapse Pipelines?
Azure Synapse Pipelines can perform data ingestion, data preparation, data management, and data serving tasks. It also provides complex ETL capabilities, which includes running U-SQL scripts on Azure Data Lake.
How do you ensure data security during the movement using Azure Data Factory?
Azure Data Factory ensures data security by encrypting data at rest and in transit. For encryption of data at rest, it uses service-managed keys or customer-managed keys in Azure Key Vault. For encryption of data in transit, it uses SSL/TLS.
What is the purpose of pipelines in Azure Synapse?
Pipelines in Azure Synapse are used to create, schedule, and manage data-driven workflows that ingest, prepare, transform, and analyze data.
Is it possible to copy a hierarchical data structure from Azure Blob Storage to Azure Cosmos DB using Azure Data Factory?
Yes, Azure Data Factory can copy hierarchical data (like JSON) from Azure Blob Storage to Azure Cosmos DB.
Can Azure Synapse Pipelines operate on streaming data?
Currently, Azure Synapse Pipelines primarily operate on batch data. For processing streaming data, Azure Stream Analytics or Azure Databricks are better suited.
Can Azure Data Factory be used for real-time data processing?
Azure Data Factory is primarily a batch data processing service. It doesn’t support real-time data processing natively. Real-time processing can be handled using other Azure services like Azure Stream Analytics or Azure Functions.
How does Azure Data Factory support fault tolerance?
Azure Data Factory has built-in support for retrying transient failures and tolerating intermittent failures. It also provides monitoring and management capabilities to track, diagnose, and troubleshoot issues that occur during data movement and transformation.
Can you move on-premises SQL server data to Azure Cosmos DB using Azure Data Factory?
Yes, Azure Data Factory supports a wide array of on-premises and cloud-based data sources, including SQL server. Data can be extracted from an on-premises SQL Server and moved to Azure Cosmos DB.
How can Azure Synapse Pipelines aid in improving data quality?
Azure Synapse Pipelines can help improve data quality by providing functionalities to clean, validate, and transform data as it moves from source to destination.
What type of transformations can be performed in Azure Data Factory?
Azure Data Factory supports a wide range of transformations including data masking, column mapping, filter, join, pivot, unpivot, and deriving new columns from existing ones.
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