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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.

HardSystem Design
60 practiced
System design: Architect an ingestion pipeline to ingest, validate, deduplicate, store, and index 1 PB of raw image data per day for training a large vision model. Cover ingestion protocols from edge clients, transfer acceleration, metadata extraction, deduplication (content-addressable IDs), storage layout (object store vs distributed filesystem), indexing for sampling and retrieval, and how you'd ensure operational cost-control while keeping data discoverable.
MediumSystem Design
104 practiced
Design question: Your streaming inference service receives events faster than the downstream model-serving cluster can process, causing queue growth and increased latency. Describe concrete backpressure and flow-control strategies (producer-side, broker-side, and consumer-side) to keep the system healthy while meeting SLAs. Discuss trade-offs between dropping, throttling, autoscaling, and buffering with disk spillover.
MediumTechnical
73 practiced
Coding (Python): Implement a resilient batch uploader that uploads large files to S3 using multipart uploads with resumability and exponential backoff on failures. Describe how you persist upload progress (upload_id, parts completed) so the uploader can resume after a crash and ensure that retries do not create duplicate final objects.
HardSystem Design
108 practiced
Design a metadata and lineage system for ingestion pipelines so that every dataset in your data lake and feature store can be traced back to original source events, code version, and transformation steps. Describe the events and metadata you would capture at ingest, technologies to store/query lineage (e.g., Apache Atlas, Data Catalog), and how you would integrate this with model provenance and audits.
MediumTechnical
85 practiced
Scenario: Design the monitoring and alerting plan for a production ingestion pipeline that feeds both a feature store and a data lake. List the critical metrics (infra and data-quality), logs and traces to collect, alert thresholds, dashboards you would build, and how you would set SLOs/SLA for freshness and completeness.

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