Bobur Umurzokov,
Datox.ai, Developer Advocate
The rise of real-time data processing has transformed business operations, yet navigating its technical challenges remains complex. Organizations often wrestle with managing distinct batch and streaming data workflows, each presenting unique difficulties. Batch processing, while effective for large datasets, can be costly, slow, and not well-suited for streaming API integration. On the other hand, streaming, despite its speed and low latency, often has restricted functionality.
This talk is prepared for developers, data engineers, and tech visionaries eager to explore how to build an efficient, dynamic, and unified data pipeline for both scenarios using streaming platforms in Python. You will see with examples how simple it is to make your batch code run in streaming with serverless infrastructure from day 1.
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