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Automate BigQuery Data Replication to Azure Data Lake
Use CData Sync to customize and automate BigQuery data replication to Azure Data Lake.
Always-on applications rely on automatic failover capabilities and real-time data access. CData Sync integrates live BigQuery data into your Azure Data Lake instance, allowing you to consolidate all of your data into a single location for archiving, reporting, analytics, machine learning, artificial intelligence and more.
About BigQuery Data Integration
CData simplifies access and integration of live Google BigQuery data. Our customers leverage CData connectivity to:
- Simplify access to BigQuery with broad out-of-the-box support for authentication schemes, including OAuth, OAuth JWT, and GCP Instance.
- Enhance data workflows with Bi-directional data access between BigQuery and other applications.
- Perform key BigQuery actions like starting, retrieving, and canceling jobs; deleting tables; or insert job loads through SQL stored procedures.
Most CData customers are using Google BigQuery as their data warehouse and so use CData solutions to migrate business data from separate sources into BigQuery for comprehensive analytics. Other customers use our connectivity to analyze and report on their Google BigQuery data, with many customers using both solutions.
For more details on how CData enhances your Google BigQuery experience, check out our blog post: https://www.cdata.com/blog/what-is-bigquery
Getting Started
Configure Azure Data Lake as a Replication Destination
Using CData Sync, you can replicate BigQuery data to Azure Data Lake. To add a replication destination, navigate to the Connections tab.
- Click Add Connection.
- Select the Destinations tab and locate the Azure Data Lake connector.
- Click the Configure Connection icon at the end of that row to open the New Connection page. If the Configure Connection icon is not available, click the Download Connector icon to install the Azure Data Lake connector. For more information about installing new connectors, see Connections in the Help documentation.
- To connect to Azure Data Lake, set the following connection properties:
- Connection Name: Enter a connection name of your choice for the Azure Data Lake connection.
- File Format: Select the file format that you want to use. Sync supports the CSV, PARQUET, and AVRO file formats.
- URI: Enter the path of the file system and folder that contains your files (for example, abfss://MyFileSystem/FolderName).
- Azure Storage Account: Enter the name of your Azure storage account.
- Auth Scheme: Select the Auth Scheme as AccessKey. The available auth schmes for Azure Data Lake are Azure Active Directory, Azure Service Principal, Azure Service Principal Certificate, Azure Managed Service Identity, Access Key, and Azure Storage SAS.
- Azure Access Key: Enter the access key that is associated with your storage account.
- Once connected, click Create & Test to create, test and save the connection.
You are now connected to Azure Data Lake and can use it as both a source and a destination.
NOTE: You can use the Label feature to add a label for a source or a destination.
In this article, we will demonstrate how to load BigQuery data into Azure Data Lake and utilize it as a destination.
Configure the BigQuery Connection
You can configure a connection to BigQuery from the Connections tab. To add a connection to your BigQuery account, navigate to the Connections tab.
- Click Add Connection.
- Select a source (BigQuery).
- Configure the connection properties.
Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.
OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.
In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.
- Click Connect to BigQuery to ensure that the connection is configured properly.
- Click Save & Test to save the changes.
Configure Replication Queries
CData Sync enables you to control replication with a point-and-click interface and with SQL queries. For each replication you wish to configure, navigate to the Jobs tab and click Add Job. Select the Source and Destination for your replication.
Replicate Entire Tables
To replicate an entire table, navigate to the Task tab in the Job, click Add Tasks, choose the table(s) from the list of BigQuery tables you wish to replicate into Azure Data Lake, and click Add Tasks again.
Customize Your Replication
You can use the Columns and Query tabs of a task to customize your replication. The Columns tab allows you to specify which columns to replicate, rename the columns at the destination, and even perform operations on the source data before replicating. The Query tab allows you to add filters, grouping, and sorting to the replication with the help of SQL queries.
Schedule Your Replication
Select the Overview tab in the Job, and click Configure under Schedule. You can schedule a job to run automatically by configuring it to run at specified intervals, ranging from once every 10 minutes to once every month.
Once you have configured the replication job, click Save Changes. You can configure any number of jobs to manage the replication of your BigQuery data to Azure Data Lake.
Run the Replication Job
Once all the required configurations are made for the job, select the BigQuery table you wish to replicate and click Run. After the replication completes successfully, a notification appears, showing the time taken to run the job and the number of rows replicated.
Free Trial & More Information
Now that you have seen how to replicate BigQuery data into Azure Data Lake, visit our CData Sync page to explore more about CData Sync and download a free 30-day trial. Start consolidating your enterprise data today!
As always, our world-class Support Team is ready to answer any questions you may have.
