YouTube Data Pipeline Design Flow | Interview Preparation #shorts - Video

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Real-time Data Pipeline Design For YouTube Creator Dashboard ????

➤ 1. Real-time data sources

• YouTube apps/web send live events (watch, heartbeat, stop)
• Backend services push likes, comments, subscribes
• Live streaming services push concurrent viewers & chat events

All events are continuously generated.

⸻

➤ 2. Ingestion layer (real-time pickup)

• Events are published to Kafka / PubSub / Kinesis
• Separate streams like watch_events, engagement_events
• Handles millions of events per second
• Buffers traffic spikes and supports replay

This is the entry point of the real-time pipeline.

⸻

➤ 3. Stream processing layer

• Flink / Spark Structured Streaming consumes streams
• Cleans and validates events
• Deduplicates events
• Applies real-time aggregations (per minute, per creator, per video)
• Outputs ready-to-serve real-time metrics

This is the real-time brain of the system.

⸻

➤ 4. Serving layer (powering the dashboard)

• Hot store (Redis / Dynamo / Cassandra)
– Used for instant dashboard tiles
– “Views now”, “Watch time last 5 min”, “Top videos now”

• Analytics store (ClickHouse / Druid / BigQuery)
– Used for charts and breakdowns
– Trends, geo, device, traffic source

• API layer
– Reads from hot store for speed
– Reads from analytics store for charts

This layer directly powers the creator dashboard.

⸻

➤ 5. Reliability & replay

• Raw events also land into a data lake
• Used for backfills, fixes, audits, and recomputation
• Ensures long-term correctness

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