Delta has become a widely used tool by data professionals to build effective and reliable Lakehouse’s. Yet, questions arise regarding its performance with large datasets, its ability to handle skewed data, and its concurrent write management. In this session, will dive deep into optimization options and methods that will improve your Lakehouse performance.

Delta files are not ordinary data files but are key in making Lakehouse efficient, optimal, and scalable. However, optimizing delta files and tables in Databricks can be challenging and even a daunting task. Techniques like partitioning and z-ordering can be limited, inflexible, and challenging to implement, especially when your data is constantly changing or growing.
This session will introduce you to the new liquid clustering technique, a cutting-edge approach that is more flexible and adaptable to data layout changes. This will not only enhance your query performance but also simplify your optimization process.
Furthermore, we will explore various other Delta file optimization techniques, such as data skipping, z-ordering, and vacuuming in Databricks. These techniques will help you maximize the value of your Delta files while minimizing resource utilization and costs.
By the end of this session, you'll have the necessary knowledge and tools to optimize Delta files and tables for your own Lakehouse.