How to Optimize Data Pipeline Development on Databricks for Large-Scale Workloads?

[tarunnagar]

Hi everyone,
I’m working on building and optimizing data pipelines in Databricks, especially for large-scale workloads, and I want to learn from others who have hands-on experience with performance tuning, architecture decisions, and best practices.

I’d appreciate insights on the following:

• Best practices for designing scalable pipelines in Databricks
• How to optimize Spark jobs (partitioning, caching, cluster configs, shuffling, etc.)
• Recommended cluster settings for heavy workloads
• How to reduce runtime and cost while processing massive datasets
• Tips for handling data skew, shuffle issues, and memory errors
• Which Delta Lake features help most (Z-order, Optimize, Auto Compaction, etc.)
• Workflow orchestration approaches — using Jobs, Workflows, or external tools
• Monitoring & debugging strategies (metrics, logs, Ganglia, event logs)
• Libraries, patterns, or design approaches that improved your pipeline performance
• Common bottlenecks you've faced and how you solved them

Basically, I’m asking experienced Databricks users to share optimization tips, common pitfalls, and real-world strategies that make large-scale data pipeline development more efficient.

Looking forward to your input and practical advice!