Design a data pipeline.
💡 Model Answer
A robust data pipeline typically consists of four stages: ingestion, processing, storage, and monitoring. 1) Ingestion: Use a streaming platform like Kafka or a batch tool like AWS S3 to collect raw data from sources (APIs, logs, databases). 2) Processing: Apply validation, enrichment, and transformation using a distributed engine such as Apache Spark or Flink. 3) Storage: Persist processed data in a data lake (S3, HDFS) and a data warehouse (Redshift, BigQuery) for analytics. 4) Monitoring: Instrument the pipeline with metrics (latency, throughput) and alerts (Kafka consumer lag, job failures). Key decisions include choosing batch vs. stream, data format (Parquet, Avro), and fault‑tolerance (checkpointing). Complexity is linear in the amount of data per batch, O(n), but distributed execution reduces wall‑clock time. Scalability is achieved by adding more workers, partitioning topics, and sharding storage.
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