Data optimization is a critical process for improving the performance and efficiency of data-driven applications. Several techniques can be used to optimize data, including partitioning, sorting, compaction, and row group sizing.

In this presentation, we will explore the myriad ways of optimizing data. We will discuss the different techniques available and the benefits and drawbacks of each technique. We will also provide practical advice on choosing the right optimization techniques for your needs.

By the end of this presentation, you will better understand data optimization and how it can be used to improve the performance and efficiency of your data-driven applications.

Key takeaways:

- Data optimization is a critical process for improving the performance and efficiency of data-driven applications.

- Several techniques can be used to optimize data, including partitioning, sorting, compaction, and row group sizing.

- The best optimization techniques for a particular dataset will depend on the application's specific requirements.

Data optimization can be a complex process, but it can be well worth the effort in improving performance and efficiency.