Azure Data Factory is an excellent tool for designing and orchestrating your Extract, Transform, Load (ETL) processes. In this chapter, we introduce several fundamental data factory concepts and guide you through the creation and scheduling of increasingly complex data-driven workflows. All the work in this chapter is done using the Microsoft data factory online portal. You’ll learn how to create and configure linked services and datasets, take advantage of built-in expressions and functions, and, most importantly, learn how and when to use the most popular Data Factory activities.
This chapter covers the following topics:
NOTE
To make fully understanding the recipes easier, we make naming suggestions for the accounts, pipelines, and so on throughout the chapter. Many services, such as Azure Storage and SQL Server, require that the names you assign are unique. Follow your own preferred naming conventions, making appropriate substitutions as you follow the recipes. For the Azure resource naming rules, refer to the documentation at https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/resource-name-rules.
In addition to Azure Data Factory, we shall be using three other Azure services: Logic Apps, Blob Storage, and Azure SQL Database. You will need to have Azure Blob Storage and Azure SQL Database accounts set up to follow the recipes. The following steps describe the necessary preparation:
adforchestrationstorage
. When creating the storage account, select the same region (that is, East US) as you selected when you created the Data Factory instance. This will reduce our costs when moving data.data
within this storage account, and upload two CSV files to the folder: airlines.csv
and countries.csv
(the files can be found on GitHub: https://github.com/PacktPublishing/Azure-Data-Factory-Cookbook/tree/master/data).AzureSQLDatabase
. When you create the Azure SQL Database instance, you will have the option of creating a server on which the SQL database will be hosted. Create that server and take note of the credentials you entered. You will need these credentials later when you log in to your database.Choose the basic configuration for your SQL server to save on costs. Once your instance is up and running, configure the Networking settings for the SQL server as highlighted in Figure 2.1. Go to the Networking page under the Security menu, then under Firewall rules, create a rule to allow your IP to access the database. Under Exceptions, make sure that you check the Allow Azure services and resources to access this database option.
Figure 2.1: Firewall configuration
Download the following SQL scripts from GitHub at https://github.com/PacktPublishing/Azure-Data-Factory-Cookbook/tree/master/Chapter02/sql-scripts:
CreateAirlineTable.sql
and CreateCountryTable.sql
: These scripts will add two tables, Country
and Airline
, which are used in several recipes, including the first one.CreateMetadataTable.sql
: This will create the FileMetadata
table and a stored procedure to insert data into that table. This table is necessary for the Using Metadata and Stored Procedure activities and Filtering your data and looping through your files recipes.CreateActivityLogsTable.sql
: This will create the PipelineLog
table and a stored procedure to insert data into that table. This table is necessary for the Chaining and branching activities within your pipeline recipe.CreateEmailRecipients.sql
: This script will create the EmailRecipients
table and populate it with a record. This table is used in the Using the Lookup, Web, and Execute Pipeline activities recipe. You will need to edit it to enter email recipient information.To create tables from the downloaded files, open your Azure SQL Database instance, go to the Query editor page, then paste the SQL scripts from the downloaded files and run them one by one.
Now that we’re all set up, let’s move on to the first recipe.
In this recipe, we shall demonstrate the power and versatility of ADF by performing a common task: importing data from several files (blobs) from a storage container into tables in Azure SQL Database. We shall create a pipeline, define datasets, and use a Copy
activity to tie all the pieces together and transfer the data. We shall also see how easy it is to back up data with a quick modification to the pipeline.
In this recipe, we shall be using most of the services that were mentioned in the Technical requirements section of this chapter. Make sure that you have access to Azure SQL Database (with the AzureSQLDatabase
instance we created) and the Azure storage account with the necessary .csv
files already uploaded.
First, open your Azure Data Factory instance in the Azure portal and go to the Author and Monitor interface. Here, we shall define the datasets for input files and database tables, along with the linked services (for Azure Blob Storage and Azure SQL Database):
AzureSQLDatabase
.adforchestrationstorage
storage account:Figure 2.2: The New linked service blade
OrchestrationAzureBlobStorage1
).
Figure 2.3: Connection configurations for Azure Blob Storage
Select the appropriate subscription and enter the name of your storage account (where you store the .csv
files):
NOTE
In this recipe, we are using Account Key authentication to access our storage account, primarily for the sake of simplicity. However, in your work environment, it is recommended to authenticate using Managed Identity, taking advantage of the Azure Active Directory service. This is more secure and allows you to avoid using credentials in your code. You can review the references for more information about using Managed Identity with Azure Data Factory in the See also section of this recipe.
AzureSQLDatabase
:
Figure 2.4: Connection properties for Azure SQL Database
Azure SQL
into the search field to find it easily.AzureSQLDatabase
) from the dropdown in the Database Name section.Now, we shall create two datasets, one for each linked service.
Figure 2.5: Create a new dataset
CsvData
and select OrchestrationAzureBlobStorage in the Linked Service dropdown.Figure 2.6: Dataset properties
AzureSQLTables
.AzureSQLTables
dataset:tableName
:
Figure 2.7: Parameterizing the dataset
CsvData
dataset:filename
.filename
. This will generate the correct code to refer to the dataset’s filename
parameter in the dynamic content text box:
Figure 2.9: Dynamic content interface
Click on the Finish button to finalize your choice.
Verify that you can see both datasets on the Datasets tab:
Figure 2.10: Datasets resource in the Author tab of Data Factory
In the Author tab, create a new pipeline. Change its name to pl_orchestration_recipe_1
.
Figure 2.11: Pipeline canvas with a Copy activity
CsvData
dataset and specify countries.csv
in the filename
textbox.AzureSQLTables
dataset and specify Country
in the tableName text field.NOTE
You will learn more about using the debug capabilities of Azure Data Factory in Chapter 9, Managing Deployment Processes with Azure DevOps. In this recipe, we introduce you to the Output pane, which will help you understand the design and function of this pipeline.
Figure 2.12: Debug output
After your pipeline has run, you should see that the dbo.Country
table in your Azure SQL database has been populated with the countries data:
Figure 2.13: Contents of the Country table in Azure SQL Database
We have copied the contents of the Countries.csv
file into the database. In the next steps, we shall demonstrate how parameterizing the datasets gives us the flexibility to define which file we want to copy and which SQL table we want as the destination without redesigning the pipeline.
airlines.csv
for the filename in the Source tab and Airline
for the table name in the Sink tab. Run your pipeline again (in Debug mode), and you should see that the second table is populated with the data – using the same pipeline!.csv
files. We can easily enhance the existing pipeline to accomplish this.Backup Copy Activity
, and configure it in the following way:AzureSQLDatabase
for the linked service, and add Airline
in the text box for the table name.CsvData
as the linked service, and enter the following formula into the filename
textbox: @concat('Airlines-', utcnow(), '.backup' )
.Figure 2.14: Adding backup functionality to the pipeline
Let’s look at how this works!
In this recipe, we became familiar with all the major components of an Azure Data Factory pipeline: linked services, datasets, and activities:
Every pipeline that you design will have those components.
In step 1 and step 2, we created the linked services to connect to Azure Blob Storage and Azure SQL Database. Then, in step 3 and step 4, we created datasets that connected to those linked services and referred to specific files or tables. We created parameters that represented the data we referred to in step 5 and step 6, and this allowed us to change which files we wanted to load into tables without creating additional pipelines. In the remaining steps, we worked with instances of the Copy activity, specifying the inputs and outputs (sources and sinks) for the data.
We used a built-in function for generating UTC timestamps in step 12. Data Factory provides many convenient built-in functions and expressions, as well as system variables, for your use. To see them, click on Backup SQL Data activity in your pipeline and go to the Source tab below it. Put your cursor inside the tableName text field.
You will see an Add dynamic content link appear underneath. Click on it, and you will see the Add dynamic content blade:
Figure 2.15: Data Factory functions and system variables
This blade lists many useful functions and system variables to explore. We will use some of them in later recipes.
Microsoft keeps extensive documentation on Data Factory. For a more detailed explanation of the concepts used in this recipe, refer to the following pages:
In this recipe, we shall create a pipeline that fetches some metadata from an Azure storage container and stores it in an Azure SQL database table. You will work with two frequently used activities, the Metadata activity and the Stored Procedure activity.
AzureSqlDatabase
and OrchestrationAzureBlobStorage
linked services in this recipe as well, so if you did not create them before, please go through the necessary steps in the previous recipe.AzureSQLDatabase
. If you haven’t done so already, create the FileMetadata
table and the stored procedure to insert the data as described in the Technical requirements section of this chapter.pl_orchestration_recipe_2
.CsvDataFolder
, pointing to the Azure Storage container (adforchestrationstorage
) we specified in the Technical requirements section. Use the delimited text file format. This time, do not specify the filename; leave it pointing to the data container itself. Use the same linked service for Azure Blob Storage as we used in the previous recipe.CsvDataFolder Metadata
.CsvDataFolder
dataset. In the same tab, under Field list, use the New button to add two fields, and select Item Name and Last Modified as the values for those fields:
Figure 2.16: Get Metadata activity configuration
Insert Metadata
.AzureSqlDatabase
) and the name of the stored procedure: [dbo].[InsertFileMetadata]
.@activity('CsvDataFolder Metadata').output.itemName
@convertFromUtc(activity('CsvDataFolder Metadata').output.lastModified, 'Pacific Standard Time')
@convertFromUtc(utcnow(), 'Pacific Standard Time')
:
Figure 2.17: Stored Procedure activity configuration
.csv
files.In this simple recipe, we introduced two new activities. In step 2, we have used the Metadata activity, with the dataset representing a folder in our container. In this step, we were only interested in the item name and the last-modified date of the folder. In step 3, we added a Stored Procedure activity, which allows us to directly invoke a stored procedure in the remote database. In order to configure the Stored Procedure activity, we needed to obtain the parameters (itemName
, lastModified
, and UpdatedAt
). The formulas used in step 5 (such as @activity('CsvDataFolder Metadata').output.itemName
) define which activity the value is coming from (the CsvDataFolder
Metadata activity) and which parts of the output are required (output.itemName
). We have used the built-in convertFromUtc
conversion function in order to present the time in a specific time zone (Pacific Standard Time, in our case).
In this recipe, we only specified the itemName
and lastModified
fields as the metadata outputs. However, the Metadata activity supports many more options. Here is the list of currently supported options from the Data Factory documentation at https://learn.microsoft.com/en-us/azure/data-factory/control-flow-get-metadata-activity#capabilities:
Figure 2.18: Metadata activity options
The Metadata type options that are available to you will depend on the dataset: for example, the contentMD5
option is only available for files, while childItems
is only available for folders.
In this recipe, we introduce you to the Filter and ForEach activities. We shall enhance the pipeline from the previous recipe to not just examine the data in the Azure Storage container, but filter it based on the file type and then record the last-modified date for every .csv
file in the folder.
The preparation steps are the same as for the previous recipe. We shall be reusing the pipeline from the Using Metadata and Stored Procedure activities recipe, so if you did not go through the steps then, do so now.
pl_orchestration_recipe_3
.CsvDataFolder
is selected as the dataset.FilterOnCsv
.@activity('CsvDataFolder Metadata').output.childItems
@endswith(item().name, '.csv')
Figure 2.19: Pipeline status overview in Debug mode
After the pipeline is finished running, hover over the row representing the Get Metadata activity run in the Output pane and examine the activity’s output. You should see that the Get Metadata activity fetched the metadata for all the files in the folder, as follows:
Figure 2.20: Get Metadata activity output
Do the same for the FilterOnCSV activity and verify that the outputs were filtered to only the csv
files.
@activity('FilterOnCSV').output.Value
.ForEach Metadata
.CsvData
(the parameterized dataset we created in the Using parameters and built-in functions recipe) as the dataset for this activity. If you do not have this dataset, please refer to the Using parameters and built-in functions recipe to see how to create a parameterized dataset.filename
parameter, enter @item().name
.Figure 2.21: Adding arguments in the Field list section
[dbo][InsertFileMetadata]
as the stored procedure name. @{item().name}
@convertFromUtc(activity('ForEach Metadata').output.lastModified,'Pacific Standard Time')
@convertFromUtc(utcnow(), 'Pacific Standard Time')
(you can use your own time zone here, as well):Figure 2.22: Stored Procedure activity configuration
Run your whole pipeline in Debug mode. When it is finished, you should see two additional rows in your FileMetadata table (in Azure SQL Database) showing the last-modified date for airlines.csv
and countries.csv.
In this recipe, we used the Metadata activity again and took advantage of the childItems
option to retrieve information about the folder. After this, we filtered the output to restrict processing to CSV files only with the help of the Filter activity.
Next, we needed to select only the CSV files from the folder for further processing. For this, we added a Filter activity. Using @activity('Get Metadata').output.childItems
, we specified that the Filter activity’s input is the metadata of all the files inside the folder. We configured the Filter activity’s condition to only keep files whose name ends with csv
(the built-in endswith
function gave us a convenient way to do this).
Finally, in order to process each file separately, we used the ForEach activity, which we used in step 6. ForEach is what is called a compound activity, because it contains a group of activities that are performed on each of the items in a loop. We configured the Filter activity to take as input the filtered file list (the output of the Filter activity), and in steps 7 and 8, we designed the sequence of actions that we want to have performed on each of the files. We used a second instance of the Metadata activity for this sub-pipeline and configured it to retrieve information about a particular file. To accomplish this, we configured it with the parameterized CsvData
dataset and specified the filename. In order to refer to the file, we used the built-in formula @item
(which provides a reference to the current file in the ForEach
loop) and indicated that we need the name
property of that object.
The configuration of the Stored Procedure activity is similar to the previous step. In order to provide the filename for the Stored Procedure parameters, we again referred to the provided current object reference, @item
. We could also have used @activity('ForEach Metadata').output.itemName
, as we did in the previous recipe.
In this recipe, we shall build a pipeline that will extract the data from the CSV files in Azure Blob Storage, load this data into the Azure SQL table, and record a log message with the status of this job. The status message will depend on whether the extract and load succeeded or failed.
We shall be using all the Azure services that are mentioned in the Technical requirements section at the beginning of the chapter. We shall be using the PipelineLog
table and the InsertLogRecord
stored procedure. If you have not created the table and the stored procedure in your Azure SQL database yet, please do so now.
pl_orchestration_recipe_4
. If you did not, go through steps 1-10 of that recipe and create a parameterized pipeline.Figure 2.23: Possible activity outcomes
On Success
.AzureSQLTables
as the linked service and [dbo].[InsertPipelineLog]
as the Stored Procedure name. Click on Test Connection to verify that you can connect to the Azure SQL database.@pipeline().Pipeline
@pipeline().RunId
Success
@utcnow()
NOTE
You can also use the Add dynamic content functionality to fill in the values. For each one, put your cursor into the field and then click on the little blue Add dynamic content link that appears underneath the field. You will see a blade that gives you a selection of system variables, functions, and activity outputs to choose from.
On Failure
, and for the Status parameter, enter Failure
:
Figure 2.24: A full pipeline with On Success and On Failure branches
Figure 2.25: Entries in PipelineLog after successful and failed pipeline runs
ADF offers another option for branching out on a condition during pipeline execution: the If Condition
activity. This activity is another example of a compound activity (like the ForEach
activity in the previous recipe): it contains two activity subgroups and a condition. Only one of the activity subgroups is executed, based on whether the condition is true or false.
The use case for the If Condition
activity is different than the approach we illustrated in this recipe. While the recipe branches out on the outcome (success or failure) of the previous activity, you design the condition in the If Condition
activity to branch out on the inputs from the previous activity. For example, let’s suppose that we want to retrieve metadata about a file, and perform one stored procedure if the file is a CSV and another stored procedure if the file is of a different type.
Here is how we would configure an If Condition
activity to accomplish this:
Figure 2.26: Configuring the If Condition activity
The full formula used in the Expression field is: @not(endswith(activity('CsvDataFolder Metadata').output.itemName, 'csv'))
.
In this recipe, we shall implement error-handling logic for our pipeline similar to the previous recipe, but with a more sophisticated design: we shall isolate the error-handling flow in its own pipeline. Our main parent pipeline will then call the child pipeline. This recipe also introduces three very useful activities to the user: Lookup, Web, and Execute Pipeline. The recipe will illustrate how to retrieve information from an Azure SQL table and how to invoke other Azure services from the pipeline.
We shall be using all the Azure services mentioned in the Technical requirements section at the beginning of the chapter. In addition, this recipe requires a table to store the email addresses of the status email recipients. Please refer to the Technical requirements section for the table creation scripts and instructions.
We shall be building a pipeline that sends an email in the case of failure. There is no activity in ADF capable of sending emails, so we shall be using the Azure Logic Apps service. Follow these steps to create an instance of this service:
ADF-Email-Logic-App
and fill in the Subscription, Resource Group, and Region information fields.Figure 2.27: HTTP trigger
{
“subject”: “<subject of the email message>”,
“messageBody”: “<body of the email message >”,
“emailAddress”: “<email-address>”
}
Enter the code in the box as shown in the following figure:
Figure 2.28: Configuring a logic app – The capture message body
NOTE
Even though we use Gmail for the purposes of this tutorial, you can also send emails using Office 365 Outlook or Outlook.com. In the See also section of this recipe, we include a link to a tutorial on how to send emails using those providers.
Figure 2.29: Configuring a logic app – Specifying an email service
Figure 2.30: Configuring a logic app – specifying the Body, Subject, and Recipient fields
@{triggerBody()['emailAddress']}
.@{triggerBody()['subject']}
in the Subject text field.@{triggerBody()['messageBody']}
. You should end up with something similar to the following screenshot:
Figure 2.31: Configuring a logic app – Specifying the To, Subject, and Body values
First, we shall create the child pipeline to retrieve the email addresses of the email recipients and send the status email:
pl_orchestration_recipe_5_child
.SELECT * FROM [dbo].[EmailRecipient]
into the text box. Make sure to uncheck the First row
only
checkbox at the bottom. Your Settings tab should look similar to the following figure:
Figure 2.32: The Get Email Recipients activity settings
In the Settings tab, enter @activity('Get Email Recipients').output.value
into the Items textbox.
We shall now configure the Web activity. First, go to the General tab, and rename it Send Email
. Then, in the URL text field, paste the URL for the logic app (which you created in the Getting ready section):
application/json
into the Value textbox.@json(concat(‘{“emailAddress”: “‘, item().emailAddress, ‘”, “subject”: “ADF Pipeline Failure”, “messageBody”: “ADF Pipeline Failed”}’))
Your Settings tab should look similar to Figure 2.33:
Figure 2.33: The Send Email activity settings
EmailRecipients
table in order to test your pipeline. You can also verify that the email was sent out by going to the ADF-Email-LogicApp UI in the Azure portal and examining the run in the Overview pane:
Figure 2.34: Logic Apps portal view
pl_orchestration_recipe_5_parent
.Send Email On Failure
.pl_orchestration_recipe_5_child
.Figure 2.35: Parent pipeline after modifying the On Failure activity
In this recipe, we introduced the concept of parent and child pipelines and used the pipeline hierarchy to incorporate the error-handling functionality. This technique offers several benefits:
To craft the child pipeline, we started by adding a Lookup activity to retrieve a list of email recipients from the database table. This is a very common use for the Lookup activity: fetching a dataset for subsequent processing. In the configuration, we specified a query for the dataset retrieval: SELECT * from [dbo].[EmailRecipient]
. We can also use a more sophisticated query to filter the email recipients, or we can retrieve all the data by selecting the Table radio button. The ability to specify a query gives users a lot of choice and flexibility in filtering a dataset or using field projections with very little effort.
The list of email recipients was processed by the ForEach activity. We encountered the ForEach activity in the previous recipe. However, inside the ForEach activity, we introduced a new kind of activity: the Web activity, which we configured to invoke a simple logic app. This illustrates the power of the Web activity: it enables the user to invoke external REST APIs without leaving the Data Factory pipeline.
There is another ADF activity that offers the user an option to integrate external APIs into a pipeline: the Webhook activity. It has a lot of similarities to the Web activity, with two major differences:
callBackUri
property to the external service, along with the other parameters you specify in the request body. It expects to receive a response from the invoked web application. If the response is not received within the configurable timeout period, the Webhook activity fails. The Web activity does not have a callBackUri
property, and, while it does have a timeout period, it is not configurable and is limited to 1 minute.
This feature of the Webhook activity can be used to control the execution flow of the pipeline – for example, to wait for user input into a web form before proceeding with further steps.
Often, it is convenient to run a data movement pipeline in response to an event. One of the most common scenarios is triggering a pipeline run in response to the addition or deletion of blobs in a monitored storage account. Azure Data Factory supports this functionality.
In this recipe, we shall create an event-based trigger that will invoke a pipeline whenever new backup files are added to a monitored folder. The pipeline will move backup files to another folder.
backups
.backups
container. Call it Backups
.Event.Grid Provider
with your subscription:First, we create the pipeline that will be triggered when a new blob is created:
pl_orchestration_recipe_7_trigger
.Filter for Backup
. In the Settings tab, change Condition to @endswith(item().name, '.backup')
:
Figure 2.36: Configuring the Filter for Backup activity
@activity('Filter For Backup').output.Value
in the Settings tab:
Figure 2.37: Updating the ForEach activity
Copy from Data to Backup
CsvData
(the parameterized dataset created in the first recipe)@item().name
Backups
datasetDelete1
). Configure it in the following way.
In the Source tab, specify Source Dataset as CsvData
. In the Filename field, enter @item().name
.
In the Logging Settings tab, uncheck the Enable Logging checkbox.
NOTE
In this tutorial, we do not need to keep track of the files we deleted. However, in a production environment, you will want to evaluate your requirements very carefully: it might be necessary to set up a logging store and enable logging for your Delete activity.
Figure 2.38: The ForEach activity canvas and configurations for the Delete activity
Figure 2.39: Trigger configuration
After you select Continue, you will see the Data Preview blade. Click OK to finish creating the trigger.
We have created a pipeline and a trigger, but we did not assign the trigger to the pipeline. Let’s do so now.
pl_orchestration_recipe_7
). Click the Add Trigger button and select the New/Edit option.
In the Add trigger blade, select the newly created trigger_blob_added trigger. Review the configurations in the Edit trigger and Data preview blades, and hit OK to assign the trigger to the pipeline:
Figure 2.40: Assigning a trigger to the pipeline
pl_orchestration_recipe_1
pipeline. That should create the backup files in the data container. The trigger we designed will invoke the pl_orchestration_recipe_7
pipeline and move the files from the data
container to the backups
container.Under the hood, Azure Data Factory uses a service called Event Grid to detect changes in the blob (that is why we had to register the Microsoft.EventGrid
provider before starting with the recipe). Event Grid is a Microsoft service that allows you to send events from a source to a destination. Right now, only blob addition and deletion events are integrated.
The trigger configuration options offer us fine-grained control over what files we want to monitor. In the recipe, we specified that the pipeline should be triggered when a new file with the.backup
extension is created in the data container in our storage account. We can monitor the following, for example:
airlines/
)..backup
files within any container: To accomplish this, select all containers in the container field and leave .backup
in the blob name ends with field.To find out other ways to configure the trigger to monitor files in a way that fulfills your business needs, please refer to the documentation listed in the See also section.
In the recipe, we worked with event triggers. The types of events that ADF supports are currently limited to blob creation and deletion; however, this selection may be expanded in the future. If you need to have your pipeline triggered by another type of event, the way to do it is by creating and configuring another Azure service (for example, a function app) to monitor your events and start a pipeline run when an event of interest happens. You will learn more about ADF integration with other services in Chapter 7, Extending Azure Data Factory with Logic Apps and Azure Functions.
ADF also offers two other kinds of triggers: a scheduled trigger and a tumbling window trigger.
A scheduled trigger invokes the pipeline at regular intervals. ADF offers rich configuration options: apart from recurrence (number of times a minute, a day, a week, and so on), you can configure start and end dates and more granular controls for the hour and minute of the run for a daily trigger, the day of the week for weekly triggers, and the day(s) of the month for monthly triggers.
A tumbling window trigger bears many similarities to the scheduled trigger (it will invoke the pipeline at regular intervals), but it has several features that make it well suited to collecting and processing historical data:
trigger().outputs.WindowStartTime
trigger().outputs.WindowEndTime
A tumbling window trigger also offers the ability to specify a dependency between pipelines. This feature allows users to design complex workflows that reuse existing pipelines.
Both event-based and scheduled triggers have a many-to-many relationship with pipelines: one trigger may be assigned to many pipelines, and a pipeline may have more than one trigger. A tumbling window trigger is pipeline-specific: it may only be assigned to one pipeline, and a pipeline may only have one tumbling window trigger.
To learn more about all three types of ADF triggers, start here:
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