In a landscape marked by macroeconomic uncertainty, businesses increasingly rely on data-driven decision-making to maintain agility, with DevOps teams at the forefront leveraging cloud-based analytics to fuel sustainable growth. Despite their potential, cloud-based analytics present challenges due to the growing data volumes that complicate orchestration, processing, and analysis, creating burdens of cost and capacity that hinder DevOps maturity. The complexity of cloud-native environments, characterized by data silos and multicloud architecture, further complicates data management and analytics, often requiring costly and time-consuming data warehouse strategies. A data lakehouse approach, combining the capabilities of a data warehouse and a data lake, offers a solution by providing cost-effective, scalable, and rapid access to high-cardinality data, maintaining full data fidelity and context for accurate predictive analytics. This approach allows DevOps teams to unify observability data, including logs, metrics, and traces, to enhance their maturity and optimize digital services, while enabling DevSecOps practices by integrating security into the software delivery lifecycle. By utilizing platforms like Dynatrace's Grail data lakehouse, organizations can effectively manage data complexity and derive actionable insights, thereby enhancing their operational efficiencies and security measures in increasingly intricate digital environments.