595: Data Engineering 101
Super Data Science: ML & AI Podcast with Jon Krohn
Understanding Trade-offs in Data Engineering
The chapter emphasizes the importance of considering trade-offs around latency in data engineering, explaining the pitfalls of using transactional databases and the benefits of columnar databases for efficient data processing. It discusses the significance of reducing latency in the data-insuring lifecycle, choosing appropriate tools for speed optimization, migrating to cloud environments for collaboration, and utilizing SQL for faster data manipulation. Additionally, it touches on the competition and collaboration between data engineers and ML engineers, the concept of the live data stack, and the integration of analytics with applications to shape the future of data science and engineering.
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