Here are my points as a suummary:

- Try to use managed tables, as recommended now by Databricks. Use Delta Lake over Parquet/CSV for large datasets in order to get more efficient update, delete and merge actions. Besides, you will get further benefits as Transactional ACID, schema evolution, time travel or even further built-in optimizations while platform evolves.

- Try to use liquid clustering on large delta tables. Liquid clustering is a data layout optimization technique that replaces table partitioning and. It simplifies table management and optimizes query performance by automatically organizing data based on clustering keys. More here: https://docs.databricks.com/aws/en/delta/clustering#when-to-use-liquid-clustering

- Combine Spark Caching and Disk Caching (before known as delta cache acceleration) More here: https://www.youtube.com/watch?v=_vWnH4kmF60

- Try to optimize computation: cluster autoscaling, photon for SQL/Delta Workloads, broadcast joins, etc.

- Try to optimize queries and workflows: avoid collect() if possible, filter early and often, avoid full scans, vacuum to remove old versions, optimize delta tables regularly, etc.

- Monitor your queries and workflows, and try to understand execution plans to detect gaps and bottlenecks.