Show HN: Cheap OpenTelemetry lakehouses with Parquet, DuckDB, and Iceberg (clay.fyi)

A new approach to managing observability data has emerged, centered around utilizing lakehouses, columnar storage formats like Parquet, and tools such as DuckDB and Apache Iceberg. This innovative concept reimagines how logs, metrics, and traces can be stored and analyzed efficiently. By leveraging cheap object storage and a single source of truth, systems can avoid the complexities of multiple storage tiers and reduce costs significantly. The integration of DuckDB enables SQL querying directly on Parquet files, facilitating fast analytics without the need for extensive compute clusters. This development is vital for the AI/ML community, as it presents an affordable and scalable solution for long-term retention and analysis of vast amounts of observability data. As the demand for AI-driven anomaly detection grows, organizing this telemetry data into a lakehouse architecture makes it easier for data scientists and engineers to run complex workloads without moving data around unnecessarily. The emergence of managed services for Iceberg adds a layer of metadata management, allowing for efficient querying and schema evolution. Overall, this shift may herald a more collaborative future between observability and data engineering teams, forging robust pathways for advanced analytics in the rapidly evolving big data landscape.