Migration of data from BigQuery to CloudSQL Postgres

[adilquraish]

We have a need to do a bulk load from BigQuery tables to a CloudSQL Postgres instance. In order to do this, there are 2 things that need to be migrated -

1. The table schema

2. The table data

I have come across a bunch of services like CloudFusion, Google Cloud Data Transfer Service, Apache beam with a google service (I am unable to recollect the name).

Would any of these services allow us to automate the schema creation in CloudSQL Postgres and then load the data?

[ms4446]

When migrating data and schemas from BigQuery to Cloud SQL PostgreSQL, it's essential to choose the right tools and services that align with your technical requirements, data volume, and operational preferences. Below is a breakdown of suitable services, along with their pros, cons, and key considerations:

1. Cloud Data Fusion

How it Works: Cloud Data Fusion is a fully managed, code-free data integration service that provides a visual interface to design and deploy data pipelines.

• Schema Creation: While it can facilitate schema understanding and transformation, automatic schema conversion from BigQuery to PostgreSQL might require manual adjustments due to differences in data types and constraints.

• Data Loading: Offers robust capabilities for data transformation and loading, making it suitable for complex ETL processes.

Pros:

• Intuitive visual interface simplifies pipeline design.

• Supports complex data transformations and integrations.

• Managed service reduces operational overhead.

Cons:

• May require manual intervention for schema conversion.

• Potentially higher costs for extensive data processing.

2. Google Cloud Dataflow (with Apache Beam)

How it Works: Dataflow is a fully managed service for stream and batch data processing, using Apache Beam for pipeline development. While there are templates for data movement, specific BigQuery to Cloud SQL migrations may need custom Apache Beam pipelines.

• Schema Creation: Direct schema migration isn't provided out-of-the-box. Custom Beam pipelines would be needed to extract BigQuery schemas and generate corresponding PostgreSQL schemas.

• Data Loading: Excellently suited for large-scale data migrations, with the ability to process and transfer vast datasets efficiently.

Pros:

• Highly scalable, handling large data volumes effectively.

• Customizable pipelines cater to specific migration needs.

Cons:

• Requires Beam programming knowledge for custom pipeline development.

• No direct template for BigQuery to Cloud SQL PostgreSQL schema migration.

3. Custom Solution with Cloud Functions and/or Cloud Run

How it Works: Develop custom scripts or applications that use Google Cloud client libraries to extract data from BigQuery, generate PostgreSQL schemas, and load data into Cloud SQL. Deploy these as Cloud Functions or on Cloud Run, with Cloud Scheduler for automation.

• Schema Creation and Data Loading: Offers full control over the migration process, allowing for customized schema conversion and data loading logic.

Pros:

• High degree of flexibility and customization.

• Can be cost-effective for smaller or less frequent migrations.

Cons:

• Significant development and maintenance effort required.

• Handling large datasets efficiently may be challenging.

Considerations for Choosing a Solution:

• Data Volume: Dataflow is preferred for large datasets due to its scalability. Cloud Data Fusion or custom solutions might be more manageable for smaller datasets or when complex transformations are needed.

• Update Frequency: For frequent data updates, solutions that can be easily automated and scaled, like custom Cloud Functions/Cloud Run applications, might be more suitable.

• Complexity and Transformation Needs: Cloud Data Fusion shines in complex ETL scenarios requiring data transformation. Custom solutions offer the most flexibility but require significant development effort.

• Cost: Evaluate the cost implications based on your data volume and processing needs. Managed services offer convenience but may incur higher costs, especially for large-scale or complex migrations.

The choice between Cloud Data Fusion, Cloud Dataflow, and custom solutions using Cloud Functions or Cloud Run depends on your specific migration needs, including the complexity of the data, the volume, and the need for data transformations. Each option has its strengths and considerations, and the best choice may involve a combination of these services to achieve your migration goals effectively.

[rdmeyersf]

Try Estuary (<URL removed by staff>). It's no-code ETL and ELT SaaS (built on OSS) and the free plan is up to 10GB of data moved from a source or to a target any two connectors including BigQuery and Postgres. It infers the schema and can create the destination schema for you. You can also store the data so you can reload, or load into another destination.