Azure Data Factory is a cloud-based integration service that enables the creation of data-driven workflows for moving and transforming data at scale. Built for complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects, Azure Data Factory can ingest, prepare, transform, and analyze data irrespective of where it is located.
The following are the steps to create and manage a data pipeline in Azure Data Factory:
- Create a data factory: In the Azure portal, you create an instance of the Data Factory with a unique name.
- Create a pipeline: In Data Factory UI, you create a new pipeline and add activities to it.
- Publish the pipeline: The pipeline can be published by clicking on the “publish all” button. This action moves the pipeline from the draft mode to the live mode.
- Trigger a pipeline run: The published pipeline can be triggered manually or on a schedule.
- Monitor the pipeline: Built-in monitoring tools in ADF provide a way to check the status and performance of the pipeline.
az datafactory factory create --name "myDataFactory" --resource-group "myResourceGroup" --location "East US"
Overview of Azure Synapse Pipelines
Azure Synapse Pipelines is an advanced analytics service intended for big data and data warehousing workloads. It includes features for data exploration, preparation, data warehousing, big data, and AI tasks.
Azure Synapse Pipelines also utilizes data pipeline concepts to facilitate the movement and transformation of data. Each pipeline can have one or multiple activities, with each one representing a data movement or a transformation.
Creating and managing a data pipeline in Azure Synapse Pipelines involve the following steps:
- Create a Synapse workspace: A Synapse workspace is a collaborative space for data engineers to create and manage pipelines, SQL scripts, notebooks, and data flows.
- Create a pipeline: In Synapse Studio, you create a new pipeline and add activities to it. This can involve data movement activities like copy data or data transformation activities like data flow.
- Publish the pipeline: The pipeline can be published using the “publish all” button. This action pulls the pipeline out of draft mode and puts it into live mode.
- Trigger a pipeline run: Pipelines in Azure Synapse can be triggered manually, on a schedule, or in response to an event.
- Monitor the pipeline: Azure Synapse includes built-in monitoring tools to help you check the status and performance of your pipelines.
Comparison between Azure Data Factory and Azure Synapse Pipelines
While both Azure Data Factory and Azure Synapse Pipelines facilitate managing data pipelines, they have differences. Listed below are some key differences:
| Feature | Azure Data Factory | Azure Synapse Pipelines |
|---|---|---|
| Integration with Data Lake | Data Lake Store is the main integration point | Directly integrated with Azure Synapse Analytics |
| Supported data sources | Extensive range of sources | More focused on data warehousing sources |
| Data transformation | Both ETL and ELT processes | ELT process primarily |
| Pricing | Based on the data movement activities and pipeline runs | Based on data warehousing units (DWU) + data storage |
| Use cases | Ideal for hybrid ETL, ELT, and data integration across different data stores | Best for big data analytics and data warehousing workloads |
In conclusion, understanding how to manage data pipelines in Azure Data Factory and Azure Synapse Pipelines is a crucial part of preparation for the DP-203 exam, and gaining an understanding of their differences and use cases can contribute to a successful exam outcome.
Practice Test
True or False: The Copy Data Tool is used in Azure Data Factory (ADF) to build data pipelines.
- True
- False
Answer: True
Explanation: The Copy Data Tool is a wizard in Azure Data Factory that walks the users through creating a pipeline for moving data.
In Azure Data Factory, what is used to organize and control data flow from source systems to destinations?
- A) Data Flows
- B) Pipelines
- C) Triggers
- D) Data Sets
Answer: B) Pipelines
Explanation: In Azure Data Factory, pipelines are used to organize and control data flow from source systems to destinations.
In Azure Synapse Pipelines, which activity is used to copy data between two data stores?
- A) Data Flow Activity
- B) Lookup Activity
- C) Copy Activity
- D) Web Activity
Answer: C) Copy Activity
Explanation: The Copy Activity in Azure Synapse Pipelines is used for the movement of data between two data stores.
True or False: Azure Data Factory does not support scheduling the execution of pipelines based on a time or event data.
- True
- False
Answer: False
Explanation: Azure Data Factory supports time-based and event-driven scheduling of pipeline execution through triggers.
Azure Data Factory can integrate with which of the following services for data processing?
- A) Azure Databricks
- B) Azure Machine Learning
- C) HDInsight
- D) All of the above
Answer: D) All of the above
Explanation: Azure Data Factory can integrate with Azure Databricks, Azure Machine Learning, and HDInsight for data processing.
True or False: You can manage Azure Synapse Pipelines using either the Azure portal, PowerShell, Rest APIs, or .NET libraries.
- True
- False
Answer: True
Explanation: Azure Synapse Pipelines can be managed using a variety of interfaces including the Azure portal, PowerShell, Rest APIs, and .NET libraries.
In Azure Data Factory, What is the purpose of a Control flow?
- A) Perform a data transformation
- B) Set up dependencies among activities
- C) Manage data storage
- D) None of the above
Answer: B) Set up dependencies among activities
Explanation: Control flow in Azure Data Factory is used to set dependencies among multiple activities including the order of operations and decision-making processes.
Microsoft’s on-premises data gateway is used for which of the following?
- A) Securely copying on-premises data
- B) Accessing data in Azure
- C) Connecting to non-Azure services
- D) All of the above
Answer: D) All of the above
Explanation: Microsoft’s on-premises data gateway allows secure data transfer from on-premises databases, as well as access to data in Azure and connections to non-Azure services.
True or False: Each pipeline in Azure Data Factory must have at least one activity.
- True
- False
Answer: True
Explanation: Every pipeline in Azure Data Factory should include at least one activity. An activity is a processing step in a pipeline.
The pipeline in Azure Production Environment can contain _____ activities.
- A) Only 1
- B) Up to 10
- C) Up to 40
- D) Any number
Answer: D) Any number
Explanation: Azure Production Environment does not impose any hard limit on the number of activities a pipeline can contain. There is only a soft limit based on performance considerations.
Interview Questions
What is Azure Data Factory?
Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation.
What is Azure Synapse Pipelines?
Azure Synapse Pipelines is a cloud-based data integration service that creates data-driven workflows for orchestrating and automating data movement and data transformation. It’s a part of Azure Synapse Analytics.
What is the purpose of data pipelines in Azure Data Factory or Azure Synapse Pipelines?
The purpose is to efficiently manage and orchestrate the transformation of large volumes of data from different sources and perform various actions like copy, transform, and load to various destinations.
How does Azure Data Factory manage data pipelines?
Azure Data Factory manages data pipelines through activities, which are the steps in a pipeline. Each activity takes inputs and produces outputs. The service links all the activities in a pipeline together in a logical sequence.
How can you monitor your data pipelines in Azure Data Factory?
You can monitor data pipelines in Azure Data Factory through the Azure portal, Azure Monitor, Azure Log Analytics, or programmatically using the API or PowerShell.
What types of data storage can be used with Azure Data Factory?
Azure Data Factory can work with different types of data storage, such as Azure Blob Storage, Azure Data Lake Storage, Azure Cosmos DB, Azure SQL Database, and more.
Can Azure Synapse Pipelines process real-time data?
No, Azure Synapse Pipelines is designed for batch data processing and not optimized for real-time data processing.
How can security be ensured in Azure Synapse Pipelines?
Security in Azure Synapse Pipelines can be ensured through Azure Active Directory for identity management, SSL/TLS for secure data transport, firewall rules, virtual network service endpoints, authentication, and more.
What is the role of triggers in Azure Data Factory?
Triggers in Azure Data Factory are used to run pipelines on a wall-clock schedule, on a data event, or as a reaction to another pipeline’s success or failure.
What is the difference between a pipeline and a data flow in Azure Data Factory?
A pipeline is a logical grouping of activities that together perform a task, while a data flow is a graphical representation of the transformations on the data within a pipeline.
What is the maximum number of activities a single pipeline in Azure Data Factory can have?
A single pipeline in Azure Data Factory can have up to 40 activities.
Is it possible to call one pipeline from another pipeline in Azure Data Factory?
Yes, it is possible using the ‘Execute Pipeline’ activity in Azure Data Factory.
Is it possible to perform a complex transformation of data within Azure Data Factory?
Yes, Azure Data Factory supports various transformation activities such as Data Flow, Databricks, HDInsight Hive, HDInsight Pig, HDInsight MapReduce, and Stored Procedure.
Can we use non-Azure data sources and sinks in Azure Data Factory pipelines?
Yes, aside from Azure-based data stores, Azure Data Factory also supports a wide range of on-premises and cloud-based data stores including, but not limited to, SQL Server, Oracle, MySQL, Amazon S3, etc.
What types of computing environments are supported by Azure Synapse Pipelines for data transformation?
Azure Synapse Pipelines supports data transformations using various compute services such as Azure Synapse Analytics (SQL serverless or dedicated), Azure Databricks, Azure HDInsight, and Azure Machine Learning.
extutorials.com