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Provide Data Pipeline for GCP Data Engineering

Cloud/Toolshard0.6 min readPremium

**Architecture**: Medallion or layered: Raw (Cloud Storage) → Curved (Dataflow/Dataproc) → Curated (BigQuery). **Ingest**: Pub/Sub for streaming; Cloud Storage for batch (Scheduled transfer, gsutil). **Processing**: Dataflow (Apache Beam) for streaming and batch—unified API....

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in Cloud/Tools
Difficulty Split
104E|27M|48H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Tech Mahindra
Key Concepts Tested
airflowbigquerypartitionsparksql

Why This Question Matters

This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like Tech Mahindra. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow, bigquery, partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
115 words

Architecture: Medallion or layered: Raw (Cloud Storage) → Curved (Dataflow/Dataproc) → Curated (BigQuery). Ingest: Pub/Sub for streaming; Cloud Storage for batch (Scheduled transfer, gsutil). Processing: Dataflow (Apache Beam) for streaming and batch—unified API. Dataproc for Spark when you need MLlib or custom libs. Orchestration: Cloud Composer (managed Airflow). Storage/Analytics: BigQuery for SQL; GCS for object storage. Why this stack: Serverless where possible; Dataflow auto-scales; BigQuery separates compute and storage. Scalability: Dataflow scales workers; BigQuery scales slots. Partition and cluster BigQuery tables by date and key columns. Cost: Dataflow and BigQuery are pay-per-use; over-provisioning Dataproc clusters is costly—use autoscaling and preemptible. Best practice: Idempotent pipelines; schema registry for streaming; RBAC via IAM and BigQuery column-level security.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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