// AI news · 2026-10-30 · Databricks Blog
Databricks launches real-time mode in Spark Declarative Pipelines
Databricks has made Real-Time Mode (RTM) in Spark Declarative Pipelines generally available, enabling millisecond-end-to-end latency for operational streaming workloads.
Published 2026-10-30 06:30 (Beijing time)Source Databricks BlogAuto-summarised, fact-checked against source
01Summary
RTM unifies batch, ETL, and low-latency streaming under a single framework, eliminating the need for specialized engines like Flink. Benchmarks show up to 92% faster performance than Flink on real customer workloads. It supports direct writes to OLTP stores such as Redis or Lakebase with zero offline-to-online sync.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- RTM delivers millisecond end-to-end latency in streaming pipelines.
- Unifies batch, ETL, and low-latency streaming under one Spark framework.
- Up to 92% faster than Apache Flink on real customer workloads.
- Supports direct writes to OLTP stores like Redis or Lakebase with zero sync delay.
03Source
Announcing General Availability of Real-Time Mode for Spark Declarative Pipelines on Databricks
Databricks Blog · published 2026-10-30 06:30 Beijing time
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