DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/System Design/Architecture/How would you handle data quality issues in a real-time ingestion pipeline?

How would you handle data quality issues in a real-time ingestion pipeline?

System Design/Architectureeasy0.2 min readPremium

**Strategy**: (1) Validate at source—schema registry, API validation; (2) Stream validation—Flink/Spark checks (nulls, types, ranges); (3) DLQ—quarantine invalid for triage; (4) Monitoring—alert on DLQ growth; (5) Reprocess from DLQ after fix. Don't block stream; use...

🤖 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
Goldman Sachs
Interview Pro Tip

Pro-Move: 'Flink job with side-output for bad records. DLQ to S3; daily report. Reprocess job after schema fix. <0.01% to DLQ.'

Key Concepts Tested
spark

Why This Question Matters

This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Goldman Sachs. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.

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.

Expert Answer
43 words

Strategy: (1) Validate at source—schema registry, API validation; (2) Stream validation—Flink/Spark checks (nulls, types, ranges); (3) DLQ—quarantine invalid for triage; (4) Monitoring—alert on DLQ growth; (5) Reprocess from DLQ after fix. Don't block stream; use side-outputs; fail fast. Goldman: quality critical for trading.

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

This answer is partially locked

Unlock the full expert answer with code examples and trade-offs

Recommended

Start AI Mock Interview

Practice real interviews with AI feedback, track progress, and get interview-ready faster.

  • Unlimited AI mock interviews
  • Instant feedback & scoring
  • Full answers to 1,800+ questions
  • Resume analyzer & SQL playground
Create Free Account

Pro starts at $24/mo - cancel anytime

Just need answers for quick revision?

Download curated PDF interview packs

Interview Packs
1,800+ real interview questions sourced from 5 top companies
AmazonGoogleDatabricksSnowflakeMeta
This answer is in the DE Mastery Vault 2026
1,863 questions with expert answers across 7 categories →

Related System Design/Architecture Questions

hardWhat architecture are you following in your current project, and why?FreeeasyCDC During Migration - explain approaches for real-time Change Data CaptureFreehardBriefly explain the architecture of Kafka.FreehardDescribe the data pipeline architecture you've worked with.FreehardExplain the trade-offs between batch and real-time data processing. Provide examples of when each is appropriate.Free

Want to know if YOUR answer is good enough?

Paste your answer and get instant AI feedback with a FAANG-level improved version.

Analyze My Answer — Free

According to DataEngPrep.tech, this is one of the most frequently asked System Design/Architecture 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.

← Back to all questionsMore System Design/Architecture questions →