Outline a data pipeline for web events to a data warehouse

WHAT THIS TESTS:

This question evaluates whether you can design a distributed event pipeline that moves high-volume user interaction data from web producers to an analytical warehouse without coupling or data loss. Interviewers care that you understand backpressure, schema evolution, durability guarantees, and the tradeoff between streaming latency and batch warehouse efficiency.

A GOOD ANSWER COVERS:

First, client-side collection using lightweight SDKs that batch events and handle retries to avoid blocking the user experience. Second, an event broker such as Kafka, RabbitMQ, or Pulsar to decouple producers from consumers, provide buffering, and maintain ordering per partition. Third, a stream processing framework like Apache Flink or Spark Streaming to validate, filter, and enrich events in flight. Fourth, persistent object storage such as Amazon S3 or HDFS to retain raw events for replay, compliance, and recovery. Fifth, loading processed data into a columnar warehouse like Snowflake or BigQuery through micro-batch or streaming ingest jobs. A senior candidate also mentions schema registries such as Confluent Schema Registry with Avro or Protobuf to enforce compatibility, plus partitioning by user ID or session to balance throughput.

COMMON WRONG ANSWERS:

Proposing a single monolithic service that both collects and warehouses data without an intermediary broker, which collapses under load. Suggesting only scheduled batch cron jobs for a high-traffic site, which introduces minutes to hours of latency and misses real-time use cases. Ignoring backpressure mechanisms so that traffic spikes overwhelm downstream consumers. Omitting a schema registry and relying on loose JSON, which breaks consumers when producers evolve fields.

LIKELY FOLLOW-UPS:

How would you handle a ten-times traffic spike without dropping events? How do you guarantee exactly-once semantics from broker to warehouse? What retention policy would you set on the broker versus object storage? How would you evolve an event schema without breaking downstream dashboards?

ONE CONCRETE EXAMPLE:

Imagine a retail site generating fifty thousand click events per second. A JavaScript SDK batches clicks into one-hundred-event payloads and sends them via HTTP to a Kafka cluster with thirty partitions keyed by session ID. Flink consumes the stream to drop bot traffic and enrich clicks with product metadata, then writes Parquet files to S3 every five minutes. Snowpipe loads those files into Snowflake, while raw Kafka data is retained for seven days and S3 raw data for one year.

Source: sawmills.ai