Top Tweets for #streamProcessing
Backlog doesn’t start at the publisher. It builds at the subscriber first, then backpressures upstream. Monitor the whole chain with #DolphinDB to understand where delays really begin. #StreamProcessing #DevOps
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Theory is useless if you cannot orchestrate it in production. 🛑
Here is the exact architecture we use to build a real-time Clickstream Analyzer from scratch using Kafka, Avro, and Stream Processing. 🧵👇
#DataEngineering #ApacheKafka #StreamProcessing
Check out the full breakdown by @rmoff here: https://t.co/eMFK0gcOiJ 🔗
#ApacheFlink #StreamProcessing #BigData #SQL #DataArchitecture
The same telemetry cleanup code gets rewritten in every subscriber.
monoblok moves it into the broker with a simple DSL
raw → round → squelch → clean
https://t.co/KrelTu74DZ
#nats #ziglang #streamprocessing
Our experts, Krzysztof Grajek and Grzegorz Kocur, will be speaking at the @confluentinc Data Streaming Meetup, co-organized by @VirtusLab. View the agenda and join the event: https://t.co/SgiN76bPVU
#ApacheKafka #DataStreaming #Confluent #Krk #Meetup #StreamProcessing #Rustlang #DevOps
#ComplexEventProcessing is the most underused capability in #ApacheFlink.
Detect event sequences, catch missing events, know when NOT to use it.
Practical guide covering #FlinkCEP vs #StreamProcessing, vendor landscape, and best practices.
New blog:
https://t.co/cg5DRQZE39
Near-real-time #streamprocessing for personalisation — 6 months in production with #ApacheFlink + SPP:
⚡ 10K+ msg/sec, p99 <100ms
✅ 99.9% uptime
💰 40% lower ops costs
Read the full blog to discover what we built and what we learnt: https://t.co/GsGUQ0wX3V
#DataEngineering #AI
Right record arrives with timestamp ≥ latest left record → join fires.
Or set delayedTime to auto‑trigger after a timeout.
#DolphinDB puts you in control.
#RealTimeAnalytics #StreamProcessing
peekAppend(engine, data) — preview calculation results without updating engine state or output! 👀
Perfect for testing, validation, and “what‑if” analysis on streaming engines.
#DolphinDB #StreamProcessing
Module 7 complete: Stream Processing with Redpanda + PyFlink ✅
- Produced 49,416 Green Taxi Oct 2025 trips to Redpanda in ~3s
- TUMBLE(5 min) window: top zone PULocationID 74 with 15 trip
#DataEngineering #ApacheFlink #Redpanda #StreamProcessing
streamEngineParser(name, metrics, dummyTable, outputTable, keyColumn, ...) — unify cross‑sectional, stateful, and time‑series streaming logic into one auto‑pipelined engine! ⚙️
#DolphinDB #StreamProcessing
getLeftStream(joinEngine) — retrieve the left‑table schema handle for cascading stream engines! 🔗
Chain reactive/join engines together for complex streaming pipelines.
#DolphinDB #StreamProcessing #JoinEngine #CascadeProcessing #RealTimeAnalytics
#DolphinDB Need to add new data columns on the fly for IIoT or real-time risk monitoring? Our stream processing supports dynamic schema changes for DFS tables, stream tables, and engines. #StreamProcessing #IIoT #RealTimeAnalytics
Chain time-series engines with an equi join engine to build end-to-end streaming analytics—from raw ticks to unified features.
#DolphinDB #StreamProcessing #BigData
Big update from the #ApacheFlink community:
Flink Agents 0.2.0 is here. It unifies #streamprocessing with #AgenticAI for always-on, event-driven #AI. Now with Java API parity, durable execution, multi-LLM support, and more. Built for #IoT, #finance, #ecommerce, and beyond.
appendMsg(engine, msgBody, msgId) — inject streaming messages with IDs into engines that have snapshot enabled (RaftGroup disabled)! 📥
Must be used as the handler in subscribeTable when snapshotDir is set for state recovery.
#DolphinDB #StreamProcessing
Jay Kreps (Kafka co-creator & Confluent CEO) keynote: Events Everywhere!
#ApacheKafka #EventStreaming #KafkaSummit #Confluent #StreamProcessing #RealTimeData #DataArchitecture #BigData #TechTalk
https://t.co/Tn8qYqc8f0
#ApacheKafka and #ApacheFlink are not rivals. They’re a powerful combo for real-time #datastreaming and #streamprocessing.
Kafka handles messaging. Flink powers advanced processing, CEP, and ML/AI.
Use both for global, hybrid, exactly-once workloads.
https://t.co/bfe6SCj7Rg
Directly subscribe stream tables to the anomaly engine—no external processor required.
#DolphinDB #StreamProcessing #Architecture
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