Apache Iceberg is an open table format revolutionizing data management. It aims to learn from Hadoop's mistakes. Its key features include dynamic schema adaptation, temporal data management, atomic transactions, and adaptive partitioning. The ecosystem around Iceberg is crucial, including optimized compute engines, automated maintenance, unified catalog management, seamless integration, and robust security and governance. When adopting Iceberg, consider clear use case definition, compatibility assessment, cloud vendor lock-in mitigation, build vs. buy decision, talent and expertise evaluation, and data governance framework establishment. Cloud vendor lock-in risks include limited flexibility, increased costs, data portability issues, and dependence on proprietary services. Strategies to overcome it include choosing open-source solutions, using cloud-agnostic services, implementing data portability, developing a multi-cloud strategy, monitoring and adjusting, negotiating with cloud providers, and investing in cloud-agnostic skills. An architecture for a multi-cloud strategy with a centralized Iceberg catalog and cloud-agnostic query engine is possible. It is well-suited for data warehousing, real-time analytics, machine learning, data integration, and cloud data lakes. It can help solve problems like slow query performance, data inconsistencies, data silos, data governance, and cloud vendor lock-in, leading to better decision-making and business outcomes.