How to Import Google BigQuery Tables to AWS Athena: The Definitive Guide

Moving data between cloud platforms can unlock powerful new analytics use cases. Google BigQuery and AWS Athena are two of the most popular serverless query engines for big data. While they have similar functionality, you may want to import data from BigQuery into Athena to:

• Join BigQuery data with other datasets in Amazon S3 to gain new insights
• Use Athena‘s capabilities like machine learning inference and federated queries
• Access BigQuery data from BI tools and SQL clients that are compatible with Athena
• Take advantage of AWS cost savings like spot pricing for ETL workloads

In this guide, I‘ll walk through how to export data from BigQuery, transfer it to S3, and make it accessible as tables in Athena. I‘ll discuss automation options and potential issues to be aware of. By the end, you‘ll have a repeatable process to open up your BigQuery datasets to the full AWS analytics ecosystem.

Overview of the BigQuery to Athena Migration Process

At a high level, the steps to import BigQuery tables to Athena are:

1. Export the data from BigQuery to Google Cloud Storage (GCS)
2. Transfer the exported files from GCS to Amazon S3
3. Use an AWS Glue Crawler to infer the schema and create table definitions
4. Query the data in Athena using the Glue Data Catalog tables

Here are the key tools we‘ll be using:

• BigQuery Export – Web UI and command-line tool to export tables
• Google Cloud Storage – Staging area for exported BigQuery data
• gsutil – Command-line tool to transfer files from GCS to S3
• Amazon S3 – Durable storage for imported BigQuery tables
• AWS Glue – Fully-managed ETL service to categorize and enrich data
• AWS Athena – Serverless interactive query service to analyze data in S3

I‘ll be using a public BigQuery sample dataset to demonstrate the process end-to-end. Make sure you have a Google Cloud Platform (GCP) project with BigQuery enabled and an AWS account set up.

Step 1: Exporting Data from BigQuery

To export a table from the BigQuery web UI:

1. Open your GCP project and go to the BigQuery page
2. Expand your project dataset and hover over the table you want to export
3. Click the Export button and select "Export to GCS"
4. Choose the GCS bucket to export to, and give the output file a name
5. Select the export format (Avro or Parquet) and compression (None or GZIP)
6. Click "Export" to start the export job

For larger tables or automated exports, use the bq command-line tool:

bq extract --destination_format=AVRO mydataset.mytable gs://mybucket/mydata.avro

This exports the table in Avro format, which is recommended for Athena compatibility. You can also specify GZIP compression and use wildcards to export multiple files.

Repeat this process for each table you want to migrate to Athena. Take note of the GCS path for each exported file.

Step 2: Transferring Data from GCS to S3

Now that the BigQuery data is staged in GCS, we need to move it to S3 to make it accessible to Athena. The gsutil command-line tool makes this straightforward.

First, ensure you‘ve installed and configured the Google Cloud SDK and AWS CLI. Set up a new S3 bucket in your desired AWS region to store the exported data.

To transfer the files with gsutil:

gsutil -m cp -r gs://mybucket/bigquery_export s3://my-athena-bucket/bigquery_import  

The -m flag enables parallel copying to speed up the transfer. Adjust the GCS and S3 paths based on your export location and desired S3 directory structure.

For recurring BigQuery exports, you can automate the transfer to S3 using AWS DataSync. This fully-managed service automatically handles scheduling, retries, and validating data consistency.

To set up a BigQuery to S3 transfer job in DataSync:

1. Open the AWS DataSync console and click "Create task"
2. Select "Google Cloud Storage location" as the source and enter your GCS credentials
3. Select "Amazon S3 bucket" as the destination and specify the S3 bucket and path
4. Configure the task options like schedule, bandwidth limits, and filters
5. Click "Create task" to activate the recurring transfer

With the automated transfer in place, new BigQuery exports will flow into S3 without manual intervention. You can monitor the status of your transfer tasks in the DataSync console.

Step 3: Crawling the Exported BigQuery Data

To query the exported data in Athena, we first need to create table definitions in the Glue Data Catalog. An AWS Glue Crawler can automate this process by scanning the files in S3, inferring the schema, and creating the tables.

First, ensure you have the necessary IAM permissions for Glue and Athena. You‘ll need to create an IAM role for the Glue Crawler that allows access to the S3 bucket.

To set up a Glue Crawler for the exported BigQuery data:

1. Open the AWS Glue console and click "Crawlers" in the left sidebar
2. Click "Add crawler" and enter a name and description
3. Choose "Data stores" as the crawler source and select the IAM role you created
4. Add the S3 path where you transferred the exported BigQuery files
5. Select "Create a single schema for each S3 path" for the crawler scope
6. Choose an existing or new database to store the tables created by the crawler
7. Set a schedule for the crawler or choose "Run on demand"
8. Review the configuration and click "Finish" to create the crawler

Now run the crawler by selecting it in the Glue console and clicking "Run crawler". It will scan the S3 files, infer the Avro schema, and create Athena-compatible table definitions in your specified database.

The tables will be in the "Parquet" format optimized for analytics even though the files are in Avro format. This allows you to take advantage of Athena‘s performance improvements for Parquet.

You can view the generated table schemas in the Glue console under "Tables" in your database. The crawler will pick up any new files added to the S3 path when it runs, keeping the Athena tables in sync with your BigQuery exports.

Step 4: Querying the Imported Data in Athena

With the tables created by the Glue Crawler, you can now run ad-hoc queries on the imported BigQuery data in Athena. Athena uses a familiar SQL syntax and integrates with a variety of BI and reporting tools.

To query the data in the Athena console:

1. Open the Athena Query Editor and ensure your imported database is selected
2. Compose your SQL query in the editor pane, referencing the tables created by Glue
3. Click "Run query" to execute and retrieve the results

For example, to get the row count of an imported table:

SELECT COUNT(*) AS row_count 
FROM mybigquerydataset.mytable;

Adjust the query based on your table names and the analytics you want to perform. You can join the imported BigQuery tables with data from other sources in S3 to enrich your analysis.

Remember that Athena queries data directly from S3, so you‘re charged based on the amount of data scanned per query. Use partitioning, bucketing, and compression to reduce the query cost and improve performance.

You can save your Athena queries for future use and share query results with others. Set up Workgroups to manage query access control and track usage costs.

With the BigQuery data now accessible in Athena, you can integrate it into your existing AWS analytics workflows. Use tools like Amazon QuickSight to build visualizations and dashboards on top of Athena.

Automation and Other Considerations

While the steps outlined above enable a basic BigQuery to Athena import, there are additional automation opportunities and edge cases to consider.

To fully automate the import workflow, you could:

• Use BigQuery scheduled queries to export data incrementally to GCS
• Trigger a Lambda function on each export to initiate the S3 transfer
• Run the Glue Crawler on a schedule to pick up new partitions
• Kick off an Athena query automatically when the crawler completes to refresh downstream datasets

This eliminates manual intervention beyond the initial setup. Terraform or AWS CloudFormation templates can codify the resource configuration.

If your BigQuery tables use nested or repeated fields, ensure your Glue Crawler and Athena table definitions properly handle the complex types. ARRAY, STRUCT, and MAP types in Athena can represent more advanced schemas.

Also consider how you‘ll handle schema evolution over time. If your BigQuery table schema changes, you may need to update the corresponding Glue and Athena table definitions and deal with different versions of the data.

Finally, monitor your S3 usage and Athena query costs to avoid unexpected charges. Implement Requester Pays on the S3 bucket and set up billing alarms to proactively manage your spending.

Conclusion

Importing BigQuery tables into Athena opens up new possibilities for combining and analyzing datasets across GCP and AWS. By exporting the data to GCS, transferring it to S3, crawling it with Glue, and querying it in Athena, you can unlock powerful insights while controlling your costs.

The key steps are:

1. Export BigQuery tables to GCS in Avro format
2. Transfer the exported files to S3 using gsutil or DataSync
3. Crawl the S3 data with Glue to create Athena tables
4. Query the imported data in Athena and integrate it with other datasets

Remember to automate and monitor each step of the process for a scalable, reliable pipeline between two of the most advanced serverless query engines available today. The unlimited potential of BigQuery and Athena is now at your fingertips!

Additional Resources

To dive deeper into BigQuery exports, S3 transfers, Glue Crawlers, and Athena best practices, check out these resources:

• Exporting table data from BigQuery
• gsutil command-line tool documentation
• AWS DataSync for online data transfer
• Defining crawlers in AWS Glue
• Creating tables in Athena using Glue

I hope this guide gives you the confidence to establish an efficient BigQuery to Athena workflow for all your cross-cloud analytics needs. Reach out in the comments with any questions!