Question 1

Explain the process of setting up an ETL pipeline using AWS services.

Accepted Answer

Architecture: Extract (Glue/Lambda from S3, RDS, API) → Transform (Glue Spark) → Load (S3, Redshift). Orchestration: Step Functions or Glue Triggers. Flow: S3 raw → Crawler → Glue ETL (PySpark) → S3 Parquet + Redshift Spectrum....

Question 2

How do you manage data storage in AWS?

Accepted Answer

Architectural layers: S3 for lake, backups, archive. EBS for instance. EFS for shared. Storage classes: Standard, IA, Glacier. Best practice: S3 for analytics; partition + lifecycle; storage class by access; encrypt; version critical; Glacier for archive....

Question 3

How would you design a data pipeline using AWS Glue, S3, and Redshift?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in Cloud/Tools is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 4

How would you optimize cost when using AWS for large-scale data processing?

Accepted Answer

**Architectural cost logic**: At scale, 10–20% savings = millions. Prioritize by spend: compute (Glue/EMR/Lambda), storage (S3), and data transfer. **Compute**: Right-size—Glue bills by DPU-minute (10 DPU minimum); profile jobs and start at 10, scale only if I/O-bound. Use Spot for Glue/EMR where fault-tolerance exists (checkpoints, idempotent writes); we achieved 40% EMR savings. Reserved Instances/Savings Plans for steady-state (e.g., always-on clusters)....

Question 5

What is the difference between S3 and EFS? When would you use each?

Accepted Answer

**S3**: Object storage. Key-value; eventual consistency (strong for new overwrites). No filesystem. Use for data lake, backup, static assets. Cheaper at scale ($0.023/GB). **EFS**: File system (NFS). Shared across EC2/ECS/Lambda. Use when apps need POSIX, shared storage, or random write. **Trade-off**: EFS is more expensive; S3 is cheaper and scales infinitely. EFS when you need mount point, multi-writer, or legacy app compatibility. **Use S3**: Data lakes, ETL outputs, backups....

Question 6

Which cloud services (AWS or others) did you leverage in your project? Why?

Accepted Answer

**STAR**: S3 (durable, scalable lake), Glue (managed Spark, no cluster ops), Athena (pay-per-query), Lambda (orchestration). **Why**: Serverless—no cluster management, pay-per-use, auto-scale. S3 with lifecycle for cost. Glue bookmarks for incremental. Athena for analyst self-service. Evaluated EMR for heavy Spark; chose Glue for lower ops. **Result**: 50 pipelines, 100TB/mo, 2 engineers....

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