Spark RT Mode
Recent items mentioning Spark RT Mode across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Apache Spark 4.2 has officially introduced Real-Time (RT) Mode alongside Auto CDC to simplify real-time data processing 1. Data practitioners can leverage this new operational workload pattern to build declarative pipelines 2 and implement ultra-fast, low-latency anomaly and fraud detection 3.
Generated daily from the 3 most recent items mentioning Spark RT Mode. Click any [N] to jump to the source.
How Databricks Feature Store serves features with sub-second freshness
Databricks Feature Store now delivers streaming feature updates from Kafka to the online store at 200ms p99 latency, cutting feature staleness from minutes or hours down to milliseconds. The gains come from Spark Real-Time Mode's continuous per-event processing and amortized checkpointing paired with Lakebase's compute-storage separation, which together enable low-latency, high-throughput writes for real-time model inference.
Introducing Apache Spark 4.2
Apache Spark 4.2 introduces governed business definitions via metric views, AI-native analytics features like vector retrieval, and simplified real-time data processing through Auto CDC and Real-Time Mode. This release also expands Spark's accessibility from external services and AI agents by leveraging Spark Connect, Arrow-first Python execution, and Python Data Sources.
A Practitioner’s Guide to Real-Time Mode on Spark Declarative Pipelines
Ultra-Fast Anomaly Detection using Apache Spark Real-Time Mode
Databricks practitioners can now implement a reusable pattern for ultra-fast, real-time fraud and anomaly detection using Apache Spark Real-Time Mode. This operational workload pattern enables data engineers to process and detect anomalies at extremely low latencies for critical business use cases.
Lakeflow: A new era of agentic data engineering
Lakeflow unifies ingestion, transformation, and orchestration under Unity Catalog, providing a single source of trusted, real-time context for agentic AI. It offers high-performance ingestion from 100+ sources, real-time streaming, visual pipeline building with Lakeflow Designer, and AI-powered authoring and operations with Genie Code and Genie ZeroOps.
Apache Spark Real-Time Mode for Gaming: A Better Way to Do Real-Time Sessionization
Apache Spark Real-Time Mode now enables real-time gaming sessionization for millions of active device sessions, replacing custom applications with sub-second precision for both input processing and timer-driven output. Learn how transformWithState timers power proactive, timer-driven heartbeats, generating output on a schedule independent of incoming data.
How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase
Build real-time fraud detection with sub-second intervention using Spark Real-Time Mode and Lakebase. This unified platform processes high-throughput data streams, executes low-latency ML models, and serves explainable fraud scores to reduce detection lag and operational complexity.
TutorialsApache Spark Streaming Real-Time Mode - Latency Demo
The video demonstrates how to deploy and run Apache Spark Streaming in Real-Time Mode (RTM) using a declarative automation bundle. It shows that RTM significantly reduces P50 and P95 latencies compared to microbatch mode, achieving 26ms and 50ms respectively in a simplified setup without an external messaging bus.
TutorialsAir Traffic Control with Apache Spark Structured Streaming Real-Time Mode
The video demonstrates building a real-time air traffic control application using Apache Spark Structured Streaming Real-Time Mode, Lakehouse, and Databricks Apps. This system processes live flight telemetry, detects congestion, and generates alerts with sub-second end-to-end latency, all within a single Databricks platform.
NewsDatabricks: What’s new in September 2025? #databricks
Databricks now supports geospatial data types (geography and geometry) with new functions for visualization and spatial operations, and introduces serverless GPU clusters for distributed GPU code execution. The platform also offers enhanced notebook features like side-by-side editing and a notebook-specific search, along with new options for managing serverless environments, SQL warehouses, and access requests in Unity Catalog.
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