Batch vs Streaming Data in AI & Artificial Intelligence

Batch processing is about Volume. It processes large datasets that have been collected over a period of time. It's cost-effective because you can run it during off-peak hours and it doesn't require the system to be 'Always-On'. It's perfect for historical analysis, training massive ML models, and monthly financial reconciliation.

Streaming is about Velocity. It processes data as it is generated (Event Streams). For AI, this is critical in Online Inference scenarios, such as detecting a cyber-attack as it happens or updating a navigation route based on traffic sensors. The challenge is 'State Management'—tracking what happened a second ago while the new data is flying in.

Data moves in two speeds: Batch and Streaming. Knowing when to use each is the difference between a system that's efficient and one that's always behind.

Batch processing handles large blocks of data at scheduled intervals. Think 'Daily Reports' or 'Weekly Payroll'. It's efficient for massive volume, but it's not real-time.

Streaming processing handles data record-by-record as it arrives. Think 'Fraud Detection' or 'Stock Tickers'. It's low latency, but requires more complex infrastructure.

Checkpoint: If you are building a 'Credit Card Fraud Detection' system, which data processing mode should you use?

• →Batch
• →Streaming

Modern AI often uses a 'Lambda Architecture'—using Batch for heavy accuracy and Streaming for immediate action.

Temporal logic mastered. Now let's explore the heavy lifter of Batch data: Apache Spark.

Classify Batch vs Streaming. Finish the rule that decides whether a data source counts as streaming based on its latency.