Part 8 | Boundaries, Collaboration, and Best Practices Between Apache DolphinScheduler and Flink & Spark
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Learn to set boundaries and best practices for Apache DolphinScheduler with Flink and Spark to avoid unconscious responsibility creep and improve efficiency
Action Steps
- Identify the responsibilities of your scheduling system and computing engines
- Configure Apache DolphinScheduler to focus on scheduling tasks
- Use Flink and Spark for complex business logic and computation
- Set up separate control systems for computation parameters and execution details
- Monitor and adjust the boundaries between systems to ensure efficiency and scalability
Who Needs to Know This
Data engineers and architects can benefit from understanding the boundaries and collaboration best practices between scheduling systems and computing engines to design more efficient and scalable data pipelines
Key Insight
💡 Avoid letting your scheduling system take on too many responsibilities to prevent efficiency losses in the long run
Share This
🚀 Improve data pipeline efficiency by setting boundaries between scheduling systems and computing engines! #ApacheDolphinScheduler #Flink #Spark
Key Takeaways
Learn to set boundaries and best practices for Apache DolphinScheduler with Flink and Spark to avoid unconscious responsibility creep and improve efficiency
Full Article
In the continuous evolution of data platforms, a very common yet subtle misconception is that teams unconsciously allow the scheduling system to take on more and more responsibilities that do not belong to it, such as writing complex business logic in the scheduling layer, controlling computation parameters, and even attempting to centrally manage execution details across different computing engines. In the short term, this may seem to improve efficiency, but in the long run, such a de
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