For years, Airflow's answer to "which data changed?" was a shrug.
Every Spark job I've inherited has the same skeleton: read from source, filter the garbage, join against a dimension table, write to a target, then wire...
Somewhere right now, a support chatbot is confidently quoting a refund policy that was updated at 2 PM yesterday.
dbt Labs dropped their annual State of Analytics Engineering report on Tuesday, and one number keeps rattling around my head: 72% of data teams now use...
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.
Airflow 2 end-of-life lands on April 22. That's nine days from now.
You ship a daily feature pipeline.
Every team running both dbt and Flink has had the same conversation at some point: why are we maintaining two completely separate transformation stacks?
Every data team has That Person — the one who knows that the user_activity_v2 table actually feeds three downstream jobs through an intermediate field called...
Last Tuesday at 2:47 AM, a freshness SLA breach on our orders_enriched table woke up the on-call engineer.
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?
If you've been duct-taping Oracle CDC into Flink pipelines using the DataStream API and custom Debezium wrappers, version 3.6.
Every quarter, someone on the team asks: "Do we really need this Spark cluster?" For most of the jobs running on it, the answer in 2026 is no.
Twenty days from now, Apache Airflow 2.x reaches end of life.
#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...