Question 1

Describe the data pipeline architecture you've worked with.

Accepted Answer

**Section 1 — The Context (The 'Why')**
Data pipeline design must reconcile batch latency (hours) with streaming complexity (exactly-once, backpressure). A naive approach either over-engineers (Kafka for daily batch) or under-engineers (no idempotency, no lineage)....

Question 2

How would you design the schema for transactional data storage?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in General/Other 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 3

Describe your monitoring strategy for this pipeline.

Accepted Answer

**Section 1 — The Context (The 'Why')**
This system faces scale and failure challenges at production. A naive approach breaks under load, loses data, or violates compliance. The primary challenge varies by domain: notifications need preference respect; banking needs ACID; pipelines need idempotency....

Question 4

Design a scalable system for processing real-time sales data from multiple stores, storing it for analytics, and generating reports.

Accepted Answer

**Section 1 — The Context (The 'Why')**
This system design addresses "Design a scalable system for processing real-time sales data from multiple store..." at production scale. The primary challenges are throughput under variable load, fault tolerance across distributed components, and maintaining consistency guarantees that match business requirements....

Question 5

Discuss approaches for fault-tolerant data ingestion in real-time systems.

Accepted Answer

**Section 1 — The Context (The 'Why')**
This system design addresses "Discuss approaches for fault-tolerant data ingestion in real-time systems...." at production scale. The primary challenges are throughput under variable load, fault tolerance across distributed components, and maintaining consistency guarantees that match business requirements....

Question 6

How would you design a data pipeline to handle late-arriving data?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge in 'How would you design a data pipeline to handle late-arriving data?' centers on designing for production scale, correctness guarantees, and operational resilience. A naive or underspecified design fails under load: single points of failure cascade, non-idempotent operations cause duplicates on retry, and lack of observability blocks root-cause analysis....

Question 7

How would you handle schema evolution in a real-time data system?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge in 'How would you handle schema evolution in a real-time data system?' centers on designing for production scale, correctness guarantees, and operational resilience. A naive or underspecified design fails under load: single points of failure cascade, non-idempotent operations cause duplicates on retry, and lack of observability blocks root-cause analysis....

Fragma Data Systems System Design Interview Questions

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Fragma Data Systems System Design Interview Questions

Companies asking these questions

All 7 Questions

More Interview Prep Guides

Get All Answers in PDF Format