What are the best practices for migrating from Redshift to BigQuery?

1 Assess your current data warehouse

Before you start the migration process, you need to understand the size, structure, and complexity of your current data warehouse. You should also identify the data sources, pipelines, and dependencies that feed into your Redshift cluster. This will help you estimate the time, cost, and resources required for the migration. You should also review your data quality, governance, and security policies and make sure they are compatible with BigQuery.

2 Choose a migration strategy

There are different ways to migrate your data from Redshift to BigQuery, depending on your needs and preferences. You can use a one-time or incremental approach, or a combination of both. A one-time migration involves moving all your data at once, which can be faster and simpler, but also riskier and more disruptive. An incremental migration involves moving your data in batches, which can be safer and more flexible, but also slower and more complex. You can also use a hybrid approach, where you migrate some data in bulk and some data incrementally.

3 Prepare your data for migration

Before you transfer your data from Redshift to BigQuery, you need to prepare it for the new environment. This involves converting your data types, formats, and schemas to match BigQuery's requirements. You should also optimize your data for performance and efficiency, such as by partitioning, clustering, and compressing it. You can use tools like AWS Schema Conversion Tool (SCT) and AWS Database Migration Service (DMS) to help you with this step.

4 Transfer your data to BigQuery

Once your data is ready, you can use various methods to transfer it from Redshift to BigQuery. You can use native tools like AWS Data Pipeline or AWS Glue to export your data from Redshift to Google Cloud Storage (GCS) and then load it into BigQuery. You can also use third-party tools like Fivetran, Stitch, or Matillion to automate and simplify the data transfer process. You should monitor the progress and performance of the data transfer and verify the accuracy and completeness of the data in BigQuery.

5 Validate and optimize your data in BigQuery

After you have transferred your data to BigQuery, you need to validate and optimize it for your analytics and reporting needs. You should run queries and tests to check the data quality, integrity, and consistency. You should also tune your queries and tables to improve the performance and cost-effectiveness of BigQuery. You can use tools like BigQuery Data Transfer Service, BigQuery BI Engine, and BigQuery Reservations to help you with this step.

6 Update your data pipelines and applications

The final step of the migration is to update your data pipelines and applications that rely on your data warehouse. You need to redirect your data sources, transformations, and destinations to BigQuery. You also need to update your dashboards, reports, and visualizations to use BigQuery as the data source. You should test your data pipelines and applications to ensure they work as expected and meet your business requirements.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?