Hi Databricks Team,
We are currently implementing Auto Liquid Clustering (ALC) on our Delta tables as part of our data optimization efforts. During this process, we have encountered several challenges and would appreciate your guidance on best practices and mitigation strategies tailored to our use case.
Issues we are facing: Data duplication occurs when we convert a partitioned external table into an unpartitioned managed Delta table with Auto Liquid Clustering enabled. We seek a detailed, step-by-step recommended approach to applying Auto Liquid Clustering in our environment, particularly when transitioning from external partitioned tables. We currently have jobs in place that handle deduplication via hash key-based merge operations. We want to understand how these should integrate with Auto Liquid Clustering for maximum efficiency.
Our key requests: Correct and detailed implementation steps for applying Auto Liquid Clustering safely in our setup. Recommended strategies and mitigation plans to handle issues like duplication and ensure data consistency.
Insights on the potential benefits of Auto Liquid Clustering in terms of latency reduction and storage optimization. Guidance on how Auto Liquid Clustering addresses latency and performance challenges in practical scenarios. We want to make sure we implement this feature correctly to fully leverage its advantages without compromising data quality or system performance. Looking forward to your expert advice and best practices.
