Design a Data Pipeline for ML with Data Quality Gates — System Design Interview Practice
Design a data pipeline that processes raw data into ML-ready datasets with automated data validation, quality gates, schema enforcement, and data versioning. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Ingest data from multiple sources (databases, APIs, files)Requirement
- Validate data quality at each pipeline stageRequirement
- Enforce schema consistency and evolutionRequirement
- Detect data anomalies and completeness issuesRequirement
- Version datasets for reproducibilityRequirement
- Transform data into ML-ready feature tablesRequirement
- Generate data quality reportsRequirement
- Alert on data quality failures before trainingRequirement
- mlopsConcept to explore
- data pipelineConcept to explore
- data qualityConcept to explore
- data validationConcept to explore
- data versioningConcept to explore
- etlConcept to explore