Google Professional Data Engineer Course Notes

Plan Beam pipelines considering unbounded streaming and recovery. Building streaming pipelines involves careful consideration of data volume, latency, and processing guarantees. Pub/Sub decouples publishers and subscribers. Deploying Cloud Composer pipelines involves automated validation, CI/CD, and Pub/Sub configuration.

• Confusing bounded and unbounded processing models.

• Focus on the data stream type (bounded or unbounded) when selecting windowing and recovery options.

Secure data processing systems through IAM and key management. Design for exactly-once processing within Dataflow pipelines. External writes require idempotency strategies. Use envelope encryption with a data encryption key and key encryption key. Design for portability based on explicit business needs. Connectivity impacts portability; plan accordingly. Separate data keys from key encryption keys (envelope encryption). Data migration planning: compatibility, initial load, replication, validation, cutover. Envelope encryption protects data and its key. Key management: access, rotation, location, recovery.

• Assuming all data requires envelope encryption.

• Focus on the core components of IAM and key management.

Storage selection depends on workload characteristics. Planning a data warehouse with BigQuery involves optimizing for SQL analysis and managing data retention. BigLake is the storage engine for governed data lakes. Governance needs cataloging, access control, and processing coordination. Design data platforms around metadata governance and data quality. Dataplex is key for metadata discovery.

• Assuming a single storage system can meet all requirements.

• Focus on the core requirements driving storage selection.

BigQuery cost optimization centers on balancing performance with spend. Design Airflow DAGs for reliable task dependencies. Ensure task code is idempotent for safe re-execution. Configure Cloud Monitoring for workflow health insights. Organize workloads based on isolation and performance needs using BigQuery reservations and assignments. Separate workloads to avoid performance bottlenecks. Troubleshooting BigQuery involves correlating job/query failures with available data. Maintain awareness of failures and mitigate impact through proactive monitoring and tested recovery plans.

• Ignoring performance requirements in favor of solely minimizing cost.

• Consider the entire workflow, not just individual queries.

Optimize BigQuery BI Engine dashboards by focusing on data model, query design, and resource allocation. Prepare data for BigQuery ML aligning with business goals and data protection. BigQuery Analytics Hub sharing uses a defined publisher/subscriber model. Sharing rules must detail data, access, and update behavior.

• Assuming BI Engine automatically solves performance issues – a thorough diagnosis is still required.

• Review data model design principles for BI workloads.