Part 3/13:

• Schema Evolution and Data Latency: Source systems frequently change schemas without notice, leading to broken pipelines and delayed data availability. For instance, schema updates in CDC (Change Data Capture) tools or rebalancing in Kafka can cause data flow disruptions.

• Reactive Monitoring and Manual Data Quality Checks: Often, data issues are detected only after users report missing or inconsistent data. Manual checks, while necessary, are time-consuming and delay issue resolution—sometimes taking days to fix, as experienced by large companies like Walmart.

• Scalability and Onboarding Delays: Scaling pipelines to accommodate new sources or adjusting to increased data volume demands significant engineering effort, often taking days to set up and validate.