What is the difference between batch and streaming processing in Google Dataflow?
- Batch processing processes data in finite, bounded datasets, while streaming processing processes data continuously as it arrives.
- Batch processing requires manual intervention for data ingestion, while streaming processing automates data ingestion from external sources.
- Batch processing is more cost-effective but less scalable compared to streaming processing in Google Dataflow.
- Streaming processing supports only real-time data analysis, while batch processing supports both real-time and historical data analysis.
Understanding the differences between batch and streaming processing in Google Dataflow is essential for choosing the appropriate processing mode based on the nature of the data and the requirements of the application. Each mode has its advantages and use cases, and knowing when to use batch processing versus streaming processing is critical for building efficient data pipelines.
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