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.
Founder of a data streaming startup (a PhD in database systems, ex-AWS Redshift/IBM researcher) explains why AI will fundamentally reshape data engineering within 24 months. Key insights:
How text-to-SQL is becoming a commodity (with Snowflake hitting 90% accuracy)
Why vector databases are a dead-end business model
When AI replaces feature engineering (and what that means for Spark/RisingWave)
The surprising way AI acts as "lossy compression" for storage
Why database vendors must now answer: "Do we even need databases anymore?"
roaring-rs is a Rust implementation of the Roaring bitmap data structure, originally introduced as a Java library for efficiently representing large sets of integers. This crate offers memory-efficient, high-performance compressed bitsets for Rust, and is compatible with the Roaring format used in other languages. Benchmarks and real-world datasets are included, and contributions are welcome!