🚀 Lead Data Architect Interview Tool

Data Pipeline Design Questions

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Enterprise Data Lakehouse Architecture

Interviewer [00:00:05]: Design a modern data pipeline architecture for a large retail company that needs to combine transactional data from stores, e-commerce data, and third-party data for real-time analytics and ML use cases.

You [00:00:15]: I'd like to start by understanding the requirements better. What's the volume and velocity of data we're dealing with? What are the key analytics and ML use cases? What are the latency requirements for different data consumers? And are there any specific compliance or data governance requirements?

Interviewer [00:00:28]: Good questions. The company has 500 physical stores generating 10TB of transaction data daily, plus an e-commerce platform with similar volume. Key use cases include real-time inventory optimization, personalized recommendations, and sales forecasting. Some analytics need real-time data (sub-minute latency), while others can work with daily batches. GDPR and PCI compliance are required.

AI Suggestion:

Architecture Approach:
- Propose a modern data lakehouse architecture combining data lake and data warehouse capabilities
- Suggest a lambda architecture with batch and streaming layers
- Recommend a medallion architecture (bronze/silver/gold layers) for data quality progression
- Discuss data mesh principles for domain-oriented ownership
Ingestion Layer:
- Suggest Kafka/Confluent for real-time data streaming from stores and e-commerce
- Recommend Airbyte/Fivetran for third-party data integration
- Discuss change data capture (CDC) for transactional systems
- Explain data validation and schema enforcement at ingestion
Processing Layer:
- Propose Spark for batch processing with Delta Lake for ACID transactions
- Suggest Flink/Spark Streaming for real-time processing
- Discuss dbt for transformation and modeling in the warehouse
- Recommend feature store for ML feature computation and serving
Storage Layer:
- Suggest S3/ADLS as the foundational storage layer
- Recommend Iceberg/Delta Lake for table formats with ACID properties
- Discuss Snowflake/Redshift for serving analytics workloads
- Explain hot/warm/cold tiering strategy for cost optimization
Serving Layer:
- Propose real-time API layer for sub-minute latency use cases
- Suggest semantic layer (e.g., Cube.js) for consistent metrics
- Discuss ML model serving infrastructure
- Recommend visualization tools integration
Governance & Operations:
- Explain data catalog implementation for discovery
- Discuss data quality monitoring and lineage tracking
- Suggest privacy controls for GDPR compliance
- Recommend CI/CD for data pipelines

🏗️ Data Architecture Design

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⚡ ETL/ELT Pipeline Patterns

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🔄 Data Modeling Strategies

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🔍 Data Quality & Governance

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🔒 Scalability & Performance

Receive guidance on designing for scale, including partitioning strategies, distributed processing approaches, and techniques for optimizing performance of data pipelines.

📈 Real-Time Data Processing

Access expert advice on implementing real-time data processing architectures, including streaming platforms, event processing patterns, and approaches for low-latency analytics.