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Home/Questions/System Design/Architecture/How do you handle exceptions in data ingestion?

How do you handle exceptions in data ingestion?

System Design/Architectureeasy2.2 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-25

**Section 1 — The Context (The 'Why')** Exception handling in data ingestion determines whether the system fails safely or corrupts silently. Unbounded retries can DDoS sources (rate limits, IP bans); silent drops lose data forever; and missing circuit breakers allow cascading...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Gartner

Why This Question Matters

This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Gartner. While less common, it tests deeper understanding that distinguishes strong candidates.

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example. The expert answer includes a code example that demonstrates the implementation pattern.

Expert Answer
444 wordsIncludes code

Section 1 — The Context (The 'Why')
Exception handling in data ingestion determines whether the system fails safely or corrupts silently. Unbounded retries can DDoS sources (rate limits, IP bans); silent drops lose data forever; and missing circuit breakers allow cascading failures. At Gartner-scale advisory, heterogeneous sources (APIs, DBs, files) have wildly different failure modes: transient network blips vs permanent schema breaks. A naive approach—retry forever or drop on error—either overwhelms upstream or creates undetectable data loss. Key failure modes: connector OOM under backpressure, schema drift causing validation storms, and DLQ overflow when bad data volume spikes.

Section 2 — The Diagram

[Source]---->[Connector]---->[Validate]
| | |
v v v
[Retry+Circuit] [OK] [Fail->DLQ]
| | |
+--------------+----------->[Alert]

Section 3 — Component Logic
The connector pulls data with retry logic (exponential backoff, capped attempts—e.g., 5 retries with 2^n seconds) and a circuit breaker that opens after N consecutive failures, failing fast to protect the source. Validation (schema, format, business rules) separates good from bad; good records flow to the sink; bad records follow a fan-out pattern to the DLQ with full error context (record, error message, timestamp). The DLQ enables replay after fixing parsers. Idempotency in the sink ensures safe retries without duplicates. Backpressure handling: when the consumer lags, the connector slows or blocks rather than buffering unboundedly. Alerts fire on DLQ growth, circuit open, or validation failure rate spikes. Implement a batch job to periodically drain and reprocess the DLQ; tag records with error type for triage. Rate-limit retries per source to avoid hammering failing APIs.

Section 4 — The Trade-offs (The 'Senior' part)

  • CAP Theorem: We choose AP. Circuit breaker fails fast to preserve upstream; DLQ ensures no data loss; eventual processing of quarantined records. Availability of the ingestion path over blocking on perfection.
  • Cost vs. Performance: SQS DLQ ~$0.40/1M requests. S3 quarantine ~$0.023/GB. Retry storms cause Lambda/Glue cost spikes—cap retries. Dead letter processing: batch job on EMR Spot.
  • Blast Radius: Connector OOM: circuit opens; backpressure to source. DLQ full: alert; manual intervention. Schema change: validation failures spike—rollback or fix consumers; coordinate with source owners. Isolate connector processes to limit resource contention.
  • Design principles: Cap retries at 5–10; use exponential backoff (1s, 2s, 4s, 8s) to avoid thundering herd. Open circuit after 5 consecutive failures; half-open after 30s to test recovery. Always include record ID, timestamp, and error message in DLQ payload for debugging. Distinguish transient errors (retry) from permanent errors (DLQ, manual fix); use error classification to route appropriately.

    Section 5 — Pro-Tip

  • Pro-Move: Never drop—persist to DLQ with error context. Implement backoff and circuit breakers.

  • Red Flag: Silent drops or unbounded retries—both are production anti-patterns.
  • ⚡
    Pro Tip

    Pro-Move: Never drop—persist to DLQ with error context. Red Flag: Silent drops or unbounded retries.

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