Data Ingestion Strategies and Tools Questions

Covers patterns, approaches, and technologies for moving data from source systems into downstream storage and processing platforms. Candidates should understand pull based and push based ingestion models including periodic polling of application interfaces, event driven webhooks, log collection, file based batch uploads, database replication using change data capture, and streaming ingestion. Evaluate trade offs for latency, throughput, ordering, delivery semantics such as at least once and exactly once, backpressure and flow control, idempotency, fault tolerance, and cost. Be familiar with common ingestion technologies and platforms such as Apache Kafka, Amazon Kinesis, Google PubSub, and Apache NiFi as well as managed cloud ingestion and extract transform load services. Topics also include schema management and evolution, data formats such as JavaScript Object Notation and columnar file formats, data validation and cleansing at ingress, security and authentication for ingestion pipelines, monitoring and observability, and operational concerns for scaling and recovery.

HardTechnical

61 practiced

Problem solving: Design a cost-optimized ingestion strategy to handle highly spiky workloads (10x peak bursts for a few minutes each day) where sustained throughput is low but burst capacity must be supported without paying for peak 24/7. Consider serverless components, reserved capacity, burst buffers, and throttling. Explain trade-offs and how you'd measure cost vs latency.

MediumTechnical

60 practiced

SQL (technical): Given the events table below, write a query to flag late-arriving events per user where an event's ingested_at timestamp is more than 24 hours after event_ts and compute the daily count of late events per user.

Table: events (event_id varchar PK, user_id bigint, event_ts timestamp, ingested_at timestamp, payload jsonb)

Provide a query using window functions or aggregations and explain assumptions about timezones and nulls.

MediumTechnical

81 practiced

Compare Apache Kafka, Amazon Kinesis, and Google Pub/Sub for AI ingestion use-cases. Evaluate them on throughput, latency, ordering guarantees, regional/global replication, ecosystem integrations (connectors, managed services), operational burden, and cost considerations for both training data collection and real-time feature streams.

HardTechnical

75 practiced

Technical (coding + design): Propose and provide pseudocode for a streaming deduplication strategy that combines an approximate Bloom filter for quick filtering and a persistent state store for exact checks. Explain how you handle false positives from the Bloom filter, the memory trade-offs, and how to expire old keys in a high-throughput stream.

HardSystem Design

83 practiced

Design a multi-region ingestion architecture that provides low read and write latency for users across two continents, preserves event ordering per user, and supports exactly-once semantics for feature updates across regions. Discuss replication strategies, conflict resolution for concurrent writes, and tools or patterns you would consider (active-active with CRDTs, geo-replication, central coordinator).

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