Insert/Update queries & BigQuery - Scheduled Queries, dbt, or something else?

[Dawid]

Apart from usual data transformations, I found myself in need to create data automatically. Mostly it revolves around GDPR compliance and creating AEAD keys for new incoming data. dbt can't handle this as it would require an incremental insert and sometimes an update/delete. 

Any suggestions what's the best way to solve it? At first I thought git repo (a must) with operational queries and use either Cloud Workflows or Scheduled Queries. Perhaps there are better ways?

[Roderick]

Hey @ms4446, do you have any expertise in this area?

[ms4446]

In BigQuery, managing data transformations for GDPR compliance and handling AEAD keys demands careful architectural design to ensure both data security and adherence to regulations. Here are some options and additional strategies that might fit your needs:

Scheduled Queries in BigQuery:

• Application: Use Scheduled Queries for regular, straightforward tasks like data cleaning, summarization, and basic reporting. These tasks are foundational for maintaining clean and compliant data sets.
• Limitations: While effective for scheduled, repetitive tasks, Scheduled Queries lack the flexibility for complex, conditional logic and multi-step data processing workflows that require external triggers or intricate decision-making.

Cloud Workflows:

• Strengths: Cloud Workflows excel in orchestrating complex, multi-step operations across Google Cloud services. They are particularly valuable for integrating tasks that involve BigQuery data manipulations, Cloud Functions for custom logic, and external APIs for enhanced data processing.
• Use Cases: Ideal for automating GDPR compliance processes, such as data discovery, classification, anonymization, and orchestrating the lifecycle management of AEAD encryption keys.

dbt: 

• Role: dbt is instrumental in transforming data within BigQuery, preparing it for GDPR compliance through sophisticated modeling, cleaning, and data structuring techniques.
• Considerations: dbt is best suited for batch processing and constructing data models. It is not designed for operational tasks requiring immediate data insertion, updates, or deletions at the row level.

Using Dataflow for Complex Transformations

• Capabilities: Dataflow provides a powerful platform for building custom, complex data processing pipelines, including real-time data streaming and intricate transformation logic.
• Integration: Seamlessly integrates with BigQuery for storing processed data and Cloud Functions for executing custom, GDPR-compliant operations, such as dynamic data masking or real-time anomaly detection.

Cloud Functions or Cloud Run:

• Functionality: These services offer the flexibility to execute custom logic, crucial for tasks like generating, updating, and managing AEAD encryption keys, and performing database operations based on specific conditions.
• Triggers: Both services can be dynamically invoked in response to events or on a schedule, providing precise control over when and how data operations are executed, which is essential for maintaining GDPR compliance.

Best Practices and Considerations

• Security and Encryption: Leverage Cloud KMS for robust key management, ensuring that encryption practices meet GDPR standards. Implement strict IAM policies to control access to sensitive operations and data.
• Adaptability for Compliance: Design your data architecture with the flexibility to adapt to GDPR requirements, such as efficiently handling data subject requests for access, portability, and erasure.
• Monitoring and Continuous Improvement: Utilize Google Cloud's monitoring tools to track compliance and security metrics. Regularly review and refine your data processing workflows, security measures, and compliance protocols to address evolving regulatory requirements and technological advancements.

Integrating Google Cloud's BigQuery, Cloud Workflows, dbt, Dataflow, Cloud Functions, and Cloud Run with a focus on operational best practices offers a comprehensive and robust framework for GDPR compliance and data security. This strategic approach ensures not only regulatory adherence but also fosters an environment of continuous improvement and adaptation to the evolving landscape of data protection and privacy.

[Dawid]

Is this an extract from Gemini?