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.

EasyTechnical

0 practiced

Describe change data capture (CDC) for database-to-stream ingestion: what it is, common approaches (log-based vs trigger-based), popular tools (Debezium, Maxwell, proprietary cloud services), and implications for AI pipelines that consume evolving transaction data. Explain how CDC helps with near-real-time feature generation and what challenges CDC introduces (DDL, snapshotting, ordering, initial load).

MediumTechnical

0 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.

HardTechnical

0 practiced

Technical: Create a comprehensive test plan for ingestion pipelines including unit tests, integration tests, property-based tests, synthetic workload tests, chaos tests (e.g., broker failure, network partitions), and data-quality tests. For each test type describe what it verifies, how to automate it in CI/CD, and how often to run heavier tests (e.g., chaos tests).

HardTechnical

0 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.

HardTechnical

0 practiced

Leadership / case study: You're leading a cross-functional initiative to migrate a legacy nightly-batch ingestion system to a streaming-first architecture to support real-time features. Propose a phased roadmap with milestones, metrics/KPIs to measure success, necessary staffing and skill changes, risk mitigation strategies (canaries, fallbacks), and an internal training plan to upskill teams.

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