Databricks just GA'd Zerobus Ingest, and its pitch is blunt: for most streaming ingestion workloads, the message bus is dead weight.
Every streaming team has a state budget, and joins eat most of it.
Half the money you spend running a Kafka cluster isn't paying for throughput.
DuckDB Labs shipped DuckLake 1.
Apache Flink Agents hit 0.2.
Everyone covered the Slackbot makeover — 30+ AI features, email drafting from chat, agentic everything.
Somewhere right now, a support chatbot is confidently quoting a refund policy that was updated at 2 PM yesterday.
Every major streaming vendor shipped a dbt adapter in the last six weeks. Confluent released dbt-confluent for Flink SQL on their cloud.
Every major streaming vendor shipped a Kafka-to-Iceberg feature in the last twelve months.
Most RAG teams treat embedding freshness the same way they treat data warehouse freshness — schedule a nightly batch job and hope nothing changes too fast.
Every conference slide deck from the last six months has featured the same number: 5 milliseconds.
Every team running both dbt and Flink has had the same conversation at some point: why are we maintaining two completely separate transformation stacks?
StreamNative shipped Lakestream last week. Confluent has Tableflow in GA.
Every data team running Kafka eventually hits the same wall: how do I get these events into my lakehouse so analysts can actually query them?
#dbt on Flink Won't Unify Your Data Stack Three days ago Confluent dropped the dbt-confluent adapter, and the data engineering corner of the internet lost...