Lakehouses utilizing open table formats like Apache Iceberg and Delta Lake are transforming data management by combining the scalability and low cost of object storage with database-like semantics, making them viable for observability workloads. These formats provide structured, queryable data storage solutions that reduce duplication and vendor lock-in, while enabling schema evolution, snapshots, and catalogs. Although challenges persist, such as partitioning tradeoffs, metadata growth, and limitations of the Parquet file format, innovations like liquid clustering and the introduction of new file formats like Lance are addressing these issues. As open table formats mature, they promise to offer a cost-effective, scalable solution for massive telemetry datasets, particularly when integrated with systems like ClickHouse that provide fast ingestion and low-latency analytics. This evolution suggests a future where the strengths of databases and lakehouses converge, delivering open, efficient storage with the performance and manageability of specialized analytical engines.