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Pushing data from BQ to Pubsub in Real-time

Posted on 01-30-2024 10:10 PM
Rengavz
Bronze 2
Bronze 2
Reply posted on --/--/---- --:-- AM

Hi,

I would like understand the best way to capturing CDC data from bq to publish into Pubsub. Primarily, I want to perform in realtime.

Please suggest best approach todo.

Thanks,

Renganathan S

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Posted on --/--/---- --:-- AM
ms4446
Staff
Reply posted on --/--/---- --:-- AM

Implementing near real-time CDC from BigQuery to Pub/Sub requires a nuanced approach that harnesses the full spectrum of Google Cloud's capabilities. This guide offers an advanced blueprint for developing a CDC solution that is both robust and efficient.

Harnessing BigQuery's Advanced Features

  • Change Streams: Keep abreast of BigQuery Change Streams' developments. Their ability to capture changes in near real-time can significantly simplify your CDC architecture by reducing latency and complexity.

  • Streaming Inserts: Utilize streaming inserts for rapid data updates, planning for the inherent short delay before data becomes queryable.

  • Scheduled Queries: Complement Change Streams and streaming inserts with scheduled queries to ensure no data modifications are missed. Tailor the frequency of these queries to balance between operational costs and the need for timely data.

Selecting the Optimal Processing Tools

  • Dataflow: Opt for Dataflow when dealing with complex data transformations or managing large volumes of data, taking advantage of its scalability and processing power.

  • Serverless Options (Cloud Functions/Cloud Run): For simpler CDC tasks or when targeting specific workflows, serverless solutions offer a cost-effective and manageable approach.

Strategic Considerations for a Comprehensive CDC Solution

  • Hybrid Strategy: A combination of Change Streams, streaming inserts, and scheduled queries can offer a comprehensive solution that captures data changes with minimal latency.

  • Balancing Latency and Freshness: Evaluate the trade-offs between achieving near real-time updates and maintaining data freshness, and design your CDC architecture to meet these requirements.

  • Robust Error Handling: Develop a robust error management strategy that includes retries, exponential backoff, and dead-letter queues to maintain data integrity across your pipeline.

  • Proactive Monitoring and Security: Implement proactive monitoring using Google Cloud Monitoring and Logging and adhere to strict security protocols, including data encryption and access control, to safeguard your data pipeline.

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