Have you designed any pipeline for processing streaming data from an implementation perspective?
💡 Model Answer
S: In my previous role, we needed to process real‑time clickstream data for a marketing dashboard.
T: I was tasked with designing a scalable, fault‑tolerant pipeline that could ingest millions of events per day.
A: I chose Amazon Kinesis Data Streams as the ingestion layer, creating 10 shards to handle peak throughput. A consumer application written in Java used the Kinesis Client Library to read records, batch them, and forward to Apache Flink for real‑time aggregation. Flink performed windowed joins with a PostgreSQL reference dataset and wrote results to Amazon S3 in Parquet format for downstream BI tools. I added CloudWatch metrics and alarms for shard lag, and used Kinesis Data Firehose to deliver a copy of the raw stream to Elasticsearch for search.
R: The pipeline achieved <200 ms latency, handled 5 M events per hour, and scaled automatically during traffic spikes. It reduced dashboard refresh time from 10 minutes to 2 minutes and was adopted company‑wide.
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