Creation of Data pipeline between server based MySQL db to cloud based PostgreSQL.

Google Cloud Database
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1

Hello @All ,
I have a use case for creating a Data pipeline for migration of Data using CDC approach from MySQL database which is in Virtual Machine to the PostgreSQL database which is a GCP SQL instance.
I have done an POC on this First I need to setup a Datastream from MySQL db to the Cloud bucket, and in the next step I use Dataflow : Datastream to SQL template for the creation of Data pipeline.
Is this approach correct or are there any other options for this?
Also I need to apply some transformations on the data but Dataflow doesn’t provide it.

Please suggest me an good approach for the pipeline creation from MySQL db to PostgreSQL db.

2

Here are some approaches to create a data pipeline from server-based MySQL to cloud-based PostgreSQL in Google Cloud, using CDC and incorporating data transformations:

Approach 1: Datastream to Pub/Sub to Dataflow

Pros:

  • Enhanced Control Over Transformations: Offers extensive capabilities for data transformations using Dataflow, ideal for complex processing needs.
  • Support for Complex Logic: Facilitates the implementation of intricate processing logic, leveraging Apache Beam SDK within Dataflow.
  • Efficient Handling of Large Datasets: Dataflow is optimized for processing large volumes of data, ensuring scalability and performance.
  • Seamless Integration with GCP Services: Offers excellent compatibility and integration with the broader GCP ecosystem.

Cons:

  • Increased Complexity: Involves a more intricate setup process, requiring a deeper understanding of multiple GCP services.
  • Potential for Higher Costs: Utilizing multiple services may lead to increased operational costs.

Steps:

  1. Datastream Configuration: Set up Datastream for MySQL to stream changes to a Pub/Sub topic.
  2. Dataflow Pipeline Creation: Develop a Dataflow pipeline using either the SQL template or Java/Python SDK for reading from Pub/Sub, applying necessary transformations, and writing to PostgreSQL.
Approach 2: Datastream to Cloud Storage to Dataflow

Pros:

  • Simplicity in Setup: Offers a straightforward and less complex pipeline setup.
  • Cost-Effectiveness: Potentially more economical, especially for simpler data migration tasks with minimal transformation requirements.

Cons:

  • Limited Transformation Capabilities: The use of Dataflow templates may restrict the extent of data transformations that can be performed.

Steps:

  1. Datastream to Cloud Storage: Configure Datastream to write MySQL database changes to Cloud Storage.
  2. Dataflow for Data Migration: Utilize Dataflow (with SQL template or SDK) to read from Cloud Storage, apply basic transformations if necessary, and load into PostgreSQL.
Additional Considerations:
  • Dataflow Transformations: Utilize SQL for straightforward transformations and Apache Beam for more complex data processing.
  • Error Handling and Monitoring: Implement robust error handling, retries, and continuous monitoring to ensure pipeline integrity and performance.
  • Alternative Transformation Methods: For simpler transformations, consider using Cloud Functions or Cloud Run before data processing in Dataflow.
Choosing the Best Approach:

When selecting the most suitable approach, consider the following:

  • Transformation Complexity: Approach 1 is preferable for intricate and complex data transformations.
  • Data Volume: Approach 1 is more adept at handling larger datasets efficiently.
  • Cost Considerations: Approach 2 may be more cost-effective for simpler migration scenarios.
  • Integration with GCP Services: Approach 1 offers better integration capabilities with other GCP services.
3

Thanks for the suggestions Sir, I want to go with approach 1 as I need to apply some transformation in the Data but right now the Datastream doesn’t support the Pub/Sub as a target. So, can you please explain the steps for approach 1?