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🚀 Understanding Stateless vs. Stateful Stream Processing in Databricks

As real-time data pipelines become essential to modern analytics, knowing how Databricks handles stateless and stateful stream processing can dramatically improve the way we build scalable streaming applications.

🔹 Stateless Stream Processing
Stateless operations don’t retain information between micro-batches.
Examples include:

Filtering events
Mapping or transforming fields
Dropping/renaming columns

Each event is processed independently—fast and simple, ideal for lightweight transformations.

🔹 Stateful Stream Processing
Stateful streams remember information across events, enabling more complex real-time analytics. Databricks maintains this state reliably and incrementally.

Time Windows
Windows allow aggregation over fixed intervals
Use cases: website traffic patterns, IoT sensor bursts, transaction monitoring.

Watermarks
Watermarks help manage late data and control state size by defining how long the engine should wait for delayed events:

Time-based Aggregations
These combine windows + watermarks to compute rolling metrics in real-time—key for fraud detection, operational dashboards, and alerting systems.

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