Using AWS Appflow to pull data from Google Bigquery results in a response too large error

[ashishpadave]

I've established a workflow in Appflow that relies on a connection to BigQuery. However, when I execute the workflow, I come across an issue outlined below:

The request failed because the service Source Google BigQuery returned the following error: Details: The response size of 14.50 MB is too large. Responses can be up to 5.50 MB., ErrorCode: ClientError.

According to bigquery response limits the response could be in GB's. But in my case it fails with only 14.5MB.

Thanks

[ms4446]

Here are some potential solutions to address this issue:

1. Workaround Appflow limitations:

• S3 as intermediate storage: Export BigQuery data to S3 and then read it using Appflow, bypassing the response size limit.
• Optimize BigQuery query: Remove unnecessary columns, apply filters, and utilize partitioning/clustering to reduce data size.
• Split the workflow: Divide the workflow into smaller flows targeting specific subsets of data.

2. Utilize BigQuery's streaming API:

• For real-time data updates, consider the streaming API, but be aware that it requires custom development or integration with other services like Lambda.

3. Alternative solutions:

• AWS Glue: If Appflow's limitations are too restrictive, explore AWS Glue for large-scale data integration.
• Stay updated: Regularly check BigQuery and Appflow documentation for any updates affecting your workflow.

Additional Points:

• The provided links were placeholders and have been corrected to point to the official Google Cloud BigQuery and AWS Appflow documentation.
• Remember that the most effective solution depends on your specific needs and data volume.

[moq]

How do you suggest the export to s3 should happen? I tried an export query in BigQuery but found that it's not possible if my dataset is not in the same region as the S3 connection, which is in an aws region.

[ms4446]

One common method is to first export your BigQuery data to GCS, which is a more straightforward process and doesn't have the same regional restrictions. Once your data is in GCS, you can then transfer it to S3. Here's how you can approach this:

Export to GCS:

• Command Insight: Utilize the bq extract command with specific flags to tailor the export to your needs. For instance, --destination_format for the file format, --compression for reducing file size, and --field_delimiter for specifying how fields are separated. This customization is crucial for ensuring compatibility and optimizing the subsequent transfer to S3.

• Permissions Check: Confirm that the initiating service account or user possesses the required IAM roles or permissions for accessing both BigQuery datasets and the GCS bucket. This step is vital to prevent any access-related interruptions during the export process.

Transfer to S3:

• Google Cloud Storage Transfer Service: Leverage this managed service for its simplicity in setting up either one-time or scheduled data transfers from GCS to S3. Pay special attention to configuring IAM roles and permissions on both ends (GCS and AWS) to facilitate a seamless transfer.

• AWS S3 Batch Operations: Ideal for executing bulk operations on the data once it's in S3. Whether it's for data transformation, tagging, or format conversion, keep in mind the associated costs with batch operations and plan your budget accordingly.

• Custom Script Execution: For granular control over the transfer process, consider executing a custom script within the Google Cloud environment (e.g., on a Compute Engine instance). This approach can minimize egress costs and latency. Tools like Python with the boto3 and google-cloud-storage libraries are recommended for their efficiency in handling large datasets.

2. Consider Data Transfer Services

• AWS DataSync: This service is an excellent solution for automated and ongoing data transfers. It not only facilitates the movement of data but also ensures that your datasets in AWS are consistently synchronized with your GCS bucket. When setting up DataSync, you'll define both the source (GCS bucket) and the destination (S3 bucket), along with any specific synchronization preferences.

3. Direct BigQuery to S3 Export (Considering Future Updates)

• Stay Updated: Given the dynamic nature of cloud services, it's crucial to stay informed about new features or enhancements that could streamline the BigQuery to S3 export process. Regularly consulting Google Cloud and AWS release notes or subscribing to their update channels can provide timely insights into new capabilities.

Important Considerations

• Cost Management: Be mindful of the costs associated with each step of the process, from GCS storage fees (even if temporary) to network egress charges and S3 operations. Utilizing the respective cloud providers' cost calculators can help estimate and manage these expenses effectively.

• Data Transformation Strategy: Consider the most efficient point in your workflow for data transformation. Depending on the complexity of the transformations and your familiarity with the tools, it may be more advantageous to perform these operations within Google Cloud using services like Dataflow or Dataproc before the transfer, or within AWS post-transfer.

Adopting GCS as an intermediary offers a versatile and effective strategy for navigating regional compatibility challenges during the BigQuery to S3 data export process. By carefully planning each step and considering both the technical and cost-related aspects, you can establish a reliable, efficient pipeline that ensures your data is accurately transferred, transformed, and ready for further analysis or processing in AWS.