From my private conversations with several Iceberg PMC members, it’s clear that full equality delete support across major query engines will be slow — not due to lack of will, but due to complexity.
Although the last time I touched Debezium in 2020, it was too immature to adopt, I thought surely its problems had been solved by now. Apparently not. I really appreciate this in-depth list of real-world problems encountered by clients trying to pipe CDC-captured changes.
Key takeaways:
Rust 2021's closure minimal capture breaks RAII patterns when only Copy-type fields are used in structs with Drop impl.
Even with impl Drop, closures may capture Copy fields instead of the whole struct — a surprising edge case.
Fix requires explicit ownership transfer via let stats = self.stats to override closure's partial capture.
etcd is primarily designed for bare-metal deployments, and its performance often suffered in cloud environments due to the relatively slower disk performance compared to on-premise setups.
Kafka has dominated data streaming for years, but cloud-native platforms (Snowflake, Redshift) now ingest data directly, batch-streaming convergence (Iceberg, lakehouses) is reshaping architectures, and cost-efficient alternatives (WarpStream, Redpanda) are cutting costs by 10x. This article explores whether Kafka can adapt—or if the streaming ecosystem is moving beyond it.
The core idea: an LLM subscribes to event-driven triggers defined in Streaming SQL (e.g., stock price surges, security alerts, IoT signals). When a trigger fires, the database pushes relevant context to the LLM, enabling instant decision-making without constant polling.
