How to load data from BigQuery to Databricks Lakehouse
Learn how to use Airbyte to synchronize your BigQuery data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from BigQuery to Google Cloud Storage (GCS)
Begin by exporting the desired dataset from BigQuery to Google Cloud Storage. You can do this by executing a BigQuery export job. Use the SQL `EXPORT DATA` statement to specify the dataset and target GCS bucket. Ensure the file format is compatible, like CSV or JSON.
Step 2: Set Up Google Cloud Storage (GCS) Access
Ensure that you have the necessary permissions to access the GCS bucket. You'll need to create a service account with roles that provide read access to the GCS bucket. Generate a JSON key file for this service account, which will be used to authenticate access from Databricks.
Step 3: Configure a Databricks Cluster
Set up a Databricks cluster if you don't already have one. Configure the cluster with the necessary Spark version and ensure that it has enough resources to handle the data transfer and processing tasks.
Step 4: Install Google Cloud Storage Connector in Databricks
In your Databricks cluster, install the Google Cloud Storage connector. You can do this by navigating to the "Libraries" section and selecting "Install New". Choose the "Maven Coordinate" option and enter the coordinates for the GCS connector, such as `com.google.cloud.bigdataoss:gcs-connector:hadoop2-2.1.7`.
Step 5: Authenticate Databricks to Access GCS
Upload the service account JSON key file to Databricks. You can do this by storing it as a Databricks secret or directly on the Databricks file system in a secure location. Use Spark configuration to set up the authentication details using the key file. For example:
```python
spark.conf.set("spark.hadoop.google.cloud.auth.service.account.enable", "true")
spark.conf.set("spark.hadoop.google.cloud.auth.service.account.json.keyfile", "/dbfs/path/to/your/keyfile.json")
```
Step 6: Load Data from GCS into Databricks
Use Spark to read the data files from GCS into a DataFrame in Databricks. You can utilize the `spark.read.format("csv")` or `spark.read.format("json")` functions to load the data, depending on the file format used during export. Specify the `gs://` path to your data files in GCS.
Step 7: Save Data to Databricks Lakehouse
Finally, write the DataFrame into the Databricks Lakehouse (Delta Lake) format. You can use the `write` method with the `format("delta")` option to save the DataFrame. Specify the target path in the Databricks Lakehouse where you want to store the data:
```python
df.write.format("delta").mode("overwrite").save("/mnt/datalake/your_target_table")
```
This will ensure the data is stored in a structured and queryable format within the Databricks Lakehouse.
By following these steps, you can successfully transfer data from BigQuery to the Databricks Lakehouse using built-in capabilities and configurations.