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Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // #302
Apr 4, 2025
Rohit Agarwal, Director of Engineering at Tecton, shares his expertise in streaming data management and ML challenges. He discusses the complexity of navigating both real-time and batch data systems. Rohit highlights the financial implications of tool fragmentation, the evolution of managed services, and the importance of collaboration among data teams. He also explores the emerging trend of Bring Your Own Cloud solutions for enhanced data security. Lastly, he touches on simplifying data processing paradigms and the future of data storage technologies.
48:51
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Quick takeaways
- The fragmentation of streaming data tools complicates effective data utilization and necessitates integrated solutions for machine learning operations.
- Cost management is critical, as organizations must balance data retention strategies in streaming versus batch processing to optimize expenses.
Deep dives
The Role of Streaming Data in Machine Learning
Streaming data plays a critical role in enhancing machine learning operations, enabling real-time and low-latency applications such as fraud detection and recommendation systems. Organizations often use streaming solutions like Kafka and Kinesis for data delivery, yet many struggle to utilize this data effectively for analytical purposes. This disconnect arises from the need to coordinate various fragmented technologies that require specialized expertise from different teams, leading to complexity and inconsistencies in data processing. Consequently, there is a growing need for integrated solutions that simplify the use of streaming data in machine learning applications, allowing companies to respond swiftly to real-time requirements.
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