![]() After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv.This executes the SQL query in our adobe analytics_hook.py file and export the results as a CSV to whichever file path we designated in our code. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e.Within the list of DAGs, you should see a new DAG titled "adobe analytics_hook". Save this file and refresh your Airflow instance.# Declare analytics_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=)ĭf.to_csv("/.csv",header=False, index=False, quoting=1) Insert the following code inside of this new file:įrom .jdbc import JdbcHook Next, create a new Python file and title it adobe analytics_hook.py.In here, we store Python files that convert into Airflow DAGs shown on the UI. Within there, we can create a new directory and title it "dags". To get started, in the Home directory, there should be an "airflow" folder.Our workflow is to simply run a SQL query against Adobe Analytics data and store the results in a CSV file. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections:Ī DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow.Test your new connection by clicking the Test button at the bottom of the form.GlobalCompanyId=myGlobalCompanyId RSID=myRSID OAuthClientId=myOauthClientId OauthClientSecret=myOAuthClientSecret CallbackURL=m圜allbackURL ) ![]() Connection URL: The JDBC connection URL from above, i.e.: jdbc:adobeanalytics:RTK=5246.Connection Id: Name the connection, i.e.: adobeanalytics_jdbc.In the Add Connection form, fill out the required connection properties:.Next, click the + sign on the following screen to create a new connection.On the navbar of your Airflow instance, hover over Admin and then click Connections.GlobalCompanyId=myGlobalCompanyId RSID=myRSID OAuthClientId=myOauthClientId OauthClientSecret=myOAuthClientSecret CallbackURL=m圜allbackURL ĬĮstablishing a JDBC Connection within Airflow The following are essential properties needed for our JDBC connection. For more information on obtaining this license (or a trial), contact our sales team. To host the JDBC driver in clustered environments or in the cloud, you will need a license (full or trial) and a Runtime Key (RTK). In the Adobe Analytics UI, navigate to Admin -> Report Suites and you will get a list of your report suites along with their identifiers next to the name.Īfter setting the GlobalCompanyId, RSID and OAuth connection properties, you are ready to connect to Adobe Analytics. Report Suite ID (RSID) is also a required connection property. Note your Global Company ID shown in the Request URL immediately preceding the users/me endpoint. Click the Try it out and Execute buttons. After logging into the Swagger UI Url, expand the users endpoint and then click the GET users/me button. If you do not know your Global Company ID, you can find it in the request URL for the users/me endpoint on the Swagger UI. GlobalCompanyId is a required connection property. See the "Getting Started" section of the help documentation for a guide. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. Either double-click the JAR file or execute the jar file from the command-line.įill in the connection properties and copy the connection string to the clipboard.Īdobe Analytics uses the OAuth authentication standard. Metadata querying allows you to work with and analyze Adobe Analytics data using native data types.Ĭonfiguring the Connection to Adobe Analyticsįor assistance in constructing the JDBC URL, use the connection string designer built into the Adobe Analytics JDBC Driver. Process unsupported operations client-side (often SQL functions and JOIN operations). SQL operations, like filters and aggregations, directly to Adobe Analytics and utilizes the embedded SQL engine to When you issue complex SQL queries to Adobe Analytics, the driver pushes supported Interacting with live Adobe Analytics data. With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for To and query Adobe Analytics data from an Apache Airflow instance and store the results in a CSV file. When paired with theĬData JDBC Driver for Adobe Analytics, Airflow can work with live Adobe Analytics data. Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows.
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