You are leading the development of a new distributed data processing platform that needs to handle petabytes of data daily with low latency for analytical queries. Detail your architectural choices for data ingestion, storage, processing, and serving layers, including considerations for data consistency, fault tolerance, and cost optimization.
final round · 15-20 minutes
How to structure your answer
MECE Framework: 1. Ingestion: Kafka/Pulsar for high-throughput, low-latency streaming. Schema registry for data governance. 2. Storage: S3 for cost-effective, scalable raw data lake. Parquet/ORC for columnar storage. DynamoDB/Cassandra for low-latency analytical queries (hot data). 3. Processing: Spark/Flink for real-time stream processing and batch transformations. Kubernetes for scalable orchestration. 4. Serving: Presto/Trino for ad-hoc queries, Druid/ClickHouse for OLAP. Consistency: Eventual consistency with CDC for updates. Fault Tolerance: Redundant Kafka brokers, S3 replication, Spark/Flink checkpoints. Cost Optimization: Spot instances, data tiering, efficient serialization.
Sample answer
For a petabyte-scale distributed data processing platform, my architectural choices would follow a MECE framework across layers. For Ingestion, Apache Kafka or Pulsar would handle high-throughput, low-latency streaming, ensuring durability and ordering, complemented by a schema registry for data governance. Storage would leverage AWS S3 for a cost-effective, scalable data lake of raw data, using Parquet or ORC formats for columnar efficiency. Hot data for low-latency analytical queries would reside in DynamoDB or Apache Cassandra. Processing would utilize Apache Spark or Flink for both real-time stream processing and batch transformations, orchestrated on Kubernetes for scalable, resilient execution. The Serving layer would employ Presto/Trino for ad-hoc SQL queries and Druid/ClickHouse for OLAP, providing sub-second query performance. Data consistency would primarily be eventual, with Change Data Capture (CDC) for critical updates. Fault tolerance would be achieved through redundant Kafka brokers, S3's inherent replication, and Spark/Flink's checkpointing and fault recovery mechanisms. Cost optimization would involve strategic use of S3's infrequent access tiers, Spark spot instances, and efficient data serialization formats like Avro or Protobuf.
Key points to mention
- • Polyglot Persistence
- • Lambda/Kappa Architecture (or hybrid)
- • Event-driven architecture (Kafka)
- • Columnar storage formats (Parquet/ORC)
- • Distributed processing frameworks (Spark/Flink)
- • Data consistency models (Eventual vs. Strong)
- • Fault tolerance mechanisms (Replication, Partitioning, Idempotency)
- • Cost optimization strategies (Managed services, Spot instances, Data lifecycle)
- • Orchestration (Airflow/Prefect)
- • Schema evolution and metadata management
Common mistakes to avoid
- ✗ Proposing a monolithic solution for all data needs.
- ✗ Ignoring data consistency models and their implications.
- ✗ Overlooking cost implications of chosen technologies.
- ✗ Not addressing schema evolution or data governance.
- ✗ Failing to consider operational overhead and maintainability.
- ✗ Suggesting technologies without justifying their fit for the specific requirements (petabytes, low latency).