How to load data from Looker to S3 Glue

Learn how to use Airbyte to synchronize your Looker data into S3 Glue within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Looker connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up S3 Glue for your extracted Looker data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Looker to S3 Glue in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Export Data from Looker

Begin by exporting the required data from Looker. Use the Looker interface to create the desired report or visualization, ensuring it contains all necessary fields and data points. Once your data is prepared, export it in a CSV or JSON format, which are both compatible with AWS services.

Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket, providing a unique name and selecting the appropriate region. Configure the bucket settings, ensuring it allows for object uploads and has the necessary permissions for your use case.

With your data exported from Looker, upload the CSV or JSON files to your S3 bucket. Use the AWS Management Console for a manual upload, or automate the process using the AWS CLI or SDKs, making sure the files are stored in the correct directory structure within the bucket.

Access AWS Glue from the AWS Management Console and create a new Crawler. Configure the Crawler to point to your S3 bucket, specifying the location of your data files. Set the Crawler to detect the data schema automatically to prepare it for further processing.

Execute the Glue Crawler to scan the S3 bucket and extract the metadata. This step will create a table in the AWS Glue Data Catalog that represents the structure of your data. Review the Data Catalog to ensure the schema has been correctly inferred and adjust if necessary.

In AWS Glue, create a new ETL (Extract, Transform, Load) job. Set the source to the table created by the Crawler in the Data Catalog. Define any transformations needed on the data, such as cleaning or reformatting fields, using the Glue Studio interface or by writing custom scripts in Python or Scala.

Execute the Glue ETL job to process the data and store the output in your desired destination, whether it be another S3 bucket, an AWS RDS database, or other storage solutions. Once the job completes, verify the output to ensure data integrity and correctness, making adjustments to the ETL process if needed.