GCP Data Architect Series - Part I
GCP Data Architect interviews focus on designing scalable, secure data pipelines and warehouses using services like BigQuery, Dataflow, Pub/Sub, and Cloud Storage. Key areas include optimizing storage costs, selecting between transactional (Spanner/SQL) and analytical (BigQuery) databases, ensuring data governance, and implementing hybrid/multi-cloud solutions.
BigQuery: How do you optimize BigQuery costs and performance (partitioning, clustering, slot management)? Explain the difference between slots and slots contention.
Storage: When would you use Cloud SQL vs. Cloud Spanner vs. Bigtable?
Data Ingestion: Explain the differences between Pub/Sub, Storage Transfer Service, and Data Transfer Service.
- :Compare Dataflow (Apache Beam) with Dataproc (Apache Spark/Hadoop). When to use which?
Streaming vs. Batch: Design a real-time analytics dashboard for IoT data.
Data Migration: How would you migrate 100TB of on-premises data to GCP?
Hybrid Cloud: Design a solution that combines on-premise databases with GCP for analytics.
Data Security: How do you secure sensitive data (PII) at rest and in transit in GCP?
How do you implement IAM policies and data lineage across data pipelines?
What is Object Versioning in Cloud Storage, and why is it used?
Explain the usage of Cloud Composer (Airflow) for orchestrating workflows.
BigQuery ML: Training and predicting using SQL.
Vertex AI: Integrating machine learning models into data pipelines.
Multi-Cloud: Using BigQuery Omni or Anthos.
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