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

74 practiced

Technical (strategy): You manage ingestion schemas across Avro, Protobuf, and JSON clients used by many services. Propose a strategy for schema migration that supports zero-downtime rollouts: include compatibility governance, migration steps for producers and consumers, data backfill approaches, and tooling/CI checks to automate compatibility validation.

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

System design: Design an ingestion pipeline that converts documents into embeddings and upserts them into a vector database (e.g., Milvus or Pinecone). Include batching, deduplication of identical documents, update semantics for corrected documents, indexing strategies, and how you would ensure near-real-time availability for semantic search while controlling cost.

EasyTechnical

70 practiced

Explain the core components of Apache Kafka and how they relate to ingestion: topics, partitions, brokers, consumer groups, offsets, retention, and compaction. Then describe how partitioning affects ordering and parallelism and why partition-key choice matters for both throughput and correctness of real-time features used by models.

HardTechnical

71 practiced

Theoretical/system-design: Explain how you can achieve end-to-end exactly-once processing in stateful stream processing pipelines (producer -> Kafka -> Flink/Beam -> external DB sink). Discuss Kafka transactions, idempotent producers, two-phase commit sinks, Flink's two-phase commit sink, and practical limitations at cloud scale.

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