This chapter explores a comprehensive set of workload optimization strategies designed to help data teams maximize performance while maintaining control over compute and storage costs. From leveraging query patterns to mitigating query skew and optimizing the consumption layer, this chapter provides actionable techniques for tuning Snowflake workloads. It also compares ETL and ELT approaches, introduces Snowpark for in-platform data processing, and outlines best practices for consumption layer and intra-row calculations. Whether you manage batch pipelines or support real-time analytics, these strategies are essential for sustainable and scalable operations.