Enterprise data warehouses (EDWs) have evolved significantly over the decades to accommodate the increasing volume, variety, and velocity of data, progressing from SQL-based systems to high-performance computing appliances, followed by the introduction of Hadoop for big data processing. Despite Hadoop's revolutionary impact, its complexity and requirement for specialized expertise led to the emergence of cloud-based EDWs and data lakes like Snowflake, BigQuery, and Databricks, which offer enhanced simplicity and management. The latest shift in the data landscape prioritizes cost reduction, avoidance of vendor lock-in, and the use of optimized compute engines tailored to specific workloads. Open-source technologies like Apache Iceberg are gaining traction as they decouple storage from compute layers, offering schema evolution, support for multiple compute engines, and ACID compliance, thus enabling a hybrid data architecture that blends the scalability of data lakes with the performance of traditional EDWs. This approach allows businesses to manage data more flexibly and cost-effectively by leveraging best-fit compute engines and reducing reliance on single vendors, while tools like New Relic provide observability and monitoring for these complex ecosystems.