Thought it might be useful to share my idea with fellow forum members. During the breaks, I worked on the *seamless integration of Spark Structured Streaming with Flask REST API for real-time data ingestion and analytics*. The use case revolves around a scenario where data is generated through REST API requests in real time. The Flask REST AP <https://en.wikipedia.org/wiki/Flask_(web_framework)>I efficiently captures and processes this data, saving it to a Spark Structured Streaming DataFrame. Subsequently, the processed data could be channelled into any sink of your choice including Kafka pipeline, showing a robust end-to-end solution for dynamic and responsive data streaming. I will delve into the architecture, implementation, and benefits of this combination, enabling one to build an agile and efficient real-time data application. I will put the code in GitHub for everyone's benefit. Hopefully your comments will help me to improve it.
Cheers Mich Talebzadeh, Dad | Technologist | Solutions Architect | Engineer London United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction.
