Hello there folks! Just posted about 🪟 and flink in

The case for a Custom Window in Flink

https://pedromazala.substack.com/p/the-case-for-a-custom-window-in-flink

Collection of interesting stuff about Flink and beyond from the last month: https://rmoff.net/2025/04/22/interesting-links-april-2025/

Hi Flink Team, Is anyone interested in a native accelerator for Apache Flink? I am Hongze from the Apache Gluten (incubating) community. We are currently working on a native Flink accelerator backed by Meta Velox. The work is in its earliest stage and we are aiming to get a PoC delivered in the next phase. Please let me know if anyone is interested with the topic. Thanks! Could view the project page to check what we have achieved on Apache Spark: https://github.com/apache/incubator-gluten

And this is why OLake (Open Source) is fast! Here's something for your weekend read: Exploring OLake's Architecture. If you're diving into real-time data replication or building modern data lakehouse architectures with Apache Iceberg, we've just shared an in-depth look at how OLake actually works behind the scenes. Whether your stack includes MongoDB, PostgreSQL, or MySQL, and you're targeting formats like Apache Iceberg or Parquet, this article has practical insights on designing scalable, efficient data pipelines. OLake is an open-source tool specifically built for high-speed data ingestion. Key Highlights: ⚡ Speed: Load data 4x to 10x faster compared to traditional ETL tools. 🕒 Real-Time CDC: Minimal-lag Change Data Capture from MongoDB, PostgreSQL, and MySQL. 🧩 Plug-and-Play Architecture: Cleanly separated core, drivers, and writers make extending OLake straightforward. 📊 Schema Flexibility: Seamlessly handles schema evolution and type changes compatible with Apache Iceberg. 🔄 Reliable Syncs: Built-in state management means your sync operations can resume effortlessly if interrupted. https://olake.io/blog/olake-architecture-deep-dive

_With apologies for writing a long blog, as I didn't have time to write a shorter one_—here are my notes as I tried to learn about watermarks in Apache Flink SQL: https://rmoff.net/2025/04/25/its-time-we-talked-about-time-exploring-watermarks-and-more-in-flink-sql/ Please let me know if you spot any errors 🙂

📣 Early Bird tickets for Flink Forward are now on sale! Ververica is thrilled to announce that Early Bird ticket sales are open for Flink Forward 2025, taking place October 13–16, 2025 in Barcelona. Secure your spot today and save 30% on conference and training passes! That means that you could get a conference-only ticket for €699 or a combined conference + training ticket for €1399! 👉🏻 ❗ Early Bird tickets will only be sold until May 31. Grab your discounted ticket before it's too late! Why Attend Flink Forward Barcelona? • Cutting‑edge talks: Learn from top engineers and data architects about the latest Apache Flink® features, best practices, and real‑world use cases. • Hands-on learning: Dive deep into streaming analytics, stateful processing, and Flink’s ecosystem with interactive, instructor‑led sessions. • Community connections: Network with hundreds of Flink developers, contributors, PMC members and users from around the globe. Forge partnerships, share experiences, and grow your professional network. • Barcelona experience: Enjoy one of Europe’s most vibrant cities—sunny beaches, world‑class cuisine, and rich cultural heritage—all just steps from the conference venue. 📌*Grab your Flink Forward Insider ticket today and see you in Barcelona!*

Does anyone have a need for tensor rt inference on flink pipeline?

Im curious to see if this is common or ever needed. Any thoughts are appreciated?

🚀 Announcing factorhouse-local from the team at Factor House! 🚀 Our new GitHub repo offers pre-configured Docker Compose environments to spin up sophisticated data stacks locally in minutes! It provides four powerful stacks: 1️⃣ Kafka Dev & Monitoring + Kpow: ▪️ Includes: 3-node Kafka, ZK, Schema Registry, Connect, Kpow. ▪️ Benefits: Robust local Kafka. Kpow: powerful toolkit for Kafka management & control. ▪️ Extras: Key Kafka connectors (S3, Debezium, Iceberg, etc.) ready. Add custom ones via volume mounts! 2️⃣ Real-Time Stream Analytics: Flink + Flex: ▪️ Includes: Flink (Job/TaskManagers), SQL Gateway, Flex. ▪️ Benefits: High-perf Flink streaming. Flex: enterprise-grade Flink workload management. ▪️ Extras: Flink SQL connectors (Kafka, Faker) ready. Easily add more via pre-configured mounts. 3️⃣ Analytics & Lakehouse: Spark, Iceberg, MinIO & Postgres: ▪️ Includes: Spark+Iceberg (Jupyter), Iceberg REST Catalog, MinIO, Postgres. ▪️ Benefits: Modern data lakehouses for batch/streaming & interactive exploration. 4️⃣ Apache Pinot Real-Time OLAP Cluster: ▪️ Includes: Pinot cluster (Controller, Broker, Server). ▪️ Benefits: Distributed OLAP for ultra-low-latency analytics. ✨ Spotlight: Kpow & Flex ▪️ Kpow simplifies Kafka dev: deep insights, topic management, data inspection, and more. ▪️ Flex offers enterprise Flink management for real-time streaming workloads. 💡 Boost Flink SQL with factorhouse/flink! Our factorhouse/flink image simplifies Flink SQL experimentation! ▪️ Pre-packaged JARs: Hadoop, Iceberg, Parquet. ▪️ Effortless Use with SQL Client/Gateway: Custom class loading (CUSTOM_JARS_DIRS) auto-loads JARs. ▪️ Simplified Dev: Start Flink SQL fast with provided/custom connectors, no manual JAR hassle-streamlining local dev. Explore quickstart examples in the repo! 🔗 Dive in: https://github.com/factorhouse/factorhouse-local

Good talk from OpenAI at Current in London about their use of Flink. Thread: https://bsky.app/profile/rmoff.net/post/3lpmfuimlqk2r

New blog: https://rmoff.net/2025/05/20/exploring-joins-and-changelogs-in-flink-sql/

Hi Apache Flink Community! flink As you might know 'Flink Forward Barcelona 2025' is accepting session proposals for 3 days more! Until the 25th you can submit you Apache Flink story on this link! https://sessionize.com/flink-forward-2025-barcelona/ If you have any questions feel free to reach out to me! Good luck for those, who already submitted their session!

Hello folks 👋 For those interested we will be running our first webinar around Fluss to talk about the latest release, what's next and a live Q&A - if you are interested to better understand how Fluss (soon to be Apache) makes Apache Flink more efficient 🐿️ 🦦 After 6 months of open sourcing the project 💻, we are opening our small community to the world 🚀, so feel free to join us in our slack channel and talk Fluss and Flink 🤝 For those that don't know Fluss: https://github.com/alibaba/fluss https://www.ververica.com/webinars/fluss-0.7-a-new-stream-of-possibilities?utm_campai[…]tm_medium=social&utm_source=linkedin&hss_channel=lcp-5385380

The Current New Orleans 2025 Call for Papers is open until 15th June. We're looking for talks on technologies including Apache Flink.

Hello community! Sharing a blog on how our team used Apache Flink to enhance our experimentation platform with statistically significant real-time A/B test monitoring. 🧪 https://medium.com/expedia-group-tech/preventing-revenue-loss-with-real-time-a-b-test-monitoring-605698817457

🚀 The journey continues! Part 4 of my "𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐢𝐧 𝐊𝐨𝐭𝐥𝐢𝐧" series is here: "𝐅𝐥𝐢𝐧𝐤 𝐃𝐚𝐭𝐚𝐒𝐭𝐫𝐞𝐚𝐦 𝐀𝐏𝐈 - 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐄𝐯𝐞𝐧𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐟𝐨𝐫 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐒𝐭𝐚𝐭𝐬"! Having explored the lightweight power of Kafka Streams, we now level up to a full-fledged distributed processing engine: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸. This post dives into the foundational DataStream API, showcasing its power for stateful, event-driven applications. In this deep dive, you'll learn how to: • Implement sophisticated event-time processing with Flink's native 𝐖𝐚𝐭𝐞𝐫𝐦𝐚𝐫𝐤𝐬. • Gracefully handle late-arriving data using Flink’s elegant 𝐒𝐢𝐝𝐞 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 feature. • Perform stateful aggregations with custom 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 and 𝐖𝐢𝐧𝐝𝐨𝐰𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧. • Consume Avro records and sink aggregated results back to Kafka. • Visualize the entire pipeline, from source to sink, using 𝐊𝐩𝐨𝐰 and 𝐅𝐚𝐜𝐭𝐨𝐫 𝐇𝐨𝐮𝐬𝐞 𝐋𝐨𝐜𝐚𝐥. This is post 4 of 5, demonstrating the control and performance you get with Flink's core API. If you're ready to move beyond the basics of stream processing, this one's for you! Read the full article here: https://jaehyeon.me/blog/2025-06-10-kotlin-getting-started-flink-datastream/ In the final post, we'll see how Flink's Table API offers a much more declarative way to achieve the same result. Your feedback is always appreciated! 🔗 𝐂𝐚𝐭𝐜𝐡 𝐮𝐩 𝐨𝐧 𝐭𝐡𝐞 𝐬𝐞𝐫𝐢𝐞𝐬: 1. Kafka Clients with JSON 2. Kafka Clients with Avro 3. Kafka Streams for Supplier Stats

🌊 𝐃𝐢𝐯𝐞 𝐃𝐞𝐞𝐩 𝐢𝐧𝐭𝐨 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 – 𝐋𝐨𝐜𝐚𝐥𝐥𝐲! 🌊 Ready to master the world of Kafka, Flink, data pipelines, and real-time analytics without the headache of complex cloud setups or resource contention? 🚀 Introducing the 𝐍𝐄𝐖 𝐅𝐚𝐜𝐭𝐨𝐫 𝐇𝐨𝐮𝐬𝐞 𝐋𝐨𝐜𝐚𝐥 𝐋𝐚𝐛𝐬 – your personal sandbox for building and experimenting with sophisticated data streaming architectures, all on your local machine! We've designed these hands-on labs to take you from foundational concepts to building complete, reactive applications: 🔗 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐅𝐮𝐥𝐥 𝐒𝐮𝐢𝐭𝐞 𝐨𝐟 𝐋𝐚𝐛𝐬: https://github.com/factorhouse/examples/tree/main/fh-local-labs 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐠𝐞𝐭 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐰𝐢𝐭𝐡: 💧 𝐋𝐚𝐛 1 - 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞: ◦ Master producing and consuming Avro data using Schema Registry. Ensure data integrity and build robust, schema-aware Kafka streams. 🔗 𝐋𝐚𝐛 2 - 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐰𝐢𝐭𝐡 𝐊𝐚𝐟𝐤𝐚 𝐂𝐨𝐧𝐧𝐞𝐜𝐭: ◦ Discover the power of Kafka Connect! Learn to stream data from sources to sinks (e.g., databases, files) efficiently, often without writing a single line of code. 🧠 𝐋𝐚𝐛𝐬 3, 4, 5 - 𝐅𝐫𝐨𝐦 𝐄𝐯𝐞𝐧𝐭𝐬 𝐭𝐨 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: ◦ Unlock the potential of your event streams! Dive into building real-time analytics applications using powerful stream processing techniques. Transform raw data into actionable intelligence, instantly. 🏞️ 𝐋𝐚𝐛𝐬 6, 7, 8, 9, 10 - 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐭𝐨 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞: ◦ Build modern data lake foundations. Learn to ingest Kafka data into highly efficient and queryable formats like Parquet and Apache Iceberg, setting the stage for powerful batch and ad-hoc analytics. 💡 𝐋𝐚𝐛𝐬 11, 12 - 𝐁𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐋𝐢𝐟𝐞: ◦ See your data in motion! Construct reactive client applications and dashboards that respond to live data streams, providing immediate insights and visualizations. 𝐖𝐡𝐲 𝐝𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞𝐬𝐞 𝐥𝐚𝐛𝐬? • 𝐃𝐞𝐦𝐲𝐬𝐭𝐢𝐟𝐲 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Break down intricate data streaming concepts into manageable, hands-on steps. • 𝐒𝐤𝐢𝐥𝐥 𝐔𝐩: Gain practical experience with essential tools like Kafka, Flink, Spark, Kafka Connect, Iceberg, and Pinot. • 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐅𝐫𝐞𝐞𝐥𝐲: Test, iterate, and innovate on data architectures locally before deploying to production. • 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Fast-track your journey to becoming proficient in real-time data engineering. Stop just dreaming about real-time data – start building it! Clone the repo, pick your adventure, and transform your understanding of modern data systems.

Interesting blog post about how Grab are using Flink SQL: https://engineering.grab.com/the-complete-stream-processing-journey-on-flinksql

I’ve been saying this for a while: data streaming needs to be polyglot. I’m addressing this with Iron Functions: a WebAssembly-based runtime that enables writing ApacheFlink transformations in several languages like TypeScript, Rust, and Go. I’ve just published a detailed post about creating User-Defined Functions (UDFs) with Iron Functions, covering: • Developer experience and language support • Sandboxing • Portability There are several demos showcasing different languages and how easy it is to package an Iron Functions project as a UDF JAR. I also deployed these UDFs to managed Flink platforms like Confluent and DeltaStream. https://irontools.dev/blog/polyglot-flink-udfs/

🚀 The journey concludes! I'm excited to share the final installment, Part 5 of my "𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐢𝐧 𝐊𝐨𝐭𝐥𝐢𝐧" series: "𝐅𝐥𝐢𝐧𝐤 𝐓𝐚𝐛𝐥𝐞 𝐀𝐏𝐈 - 𝐃𝐞𝐜𝐥𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐒𝐭𝐚𝐭𝐬 𝐢𝐧 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞"! After mastering the fine-grained control of the DataStream API, we now shift to a higher level of abstraction with the 𝗙𝗹𝗶𝗻𝗸 𝗧𝗮𝗯𝗹𝗲 𝗔𝗣𝗜. This is where stream processing meets the simplicity and power of SQL! We'll solve the same supplier statistics problem but with a concise, declarative approach. This final post covers: • Defining a 𝐓𝐚𝐛𝐥𝐞 over a streaming 𝐃𝐚𝐭𝐚𝐒𝐭𝐫𝐞𝐚𝐦 to run queries. • Writing declarative, 𝐒𝐐𝐋-𝐥𝐢𝐤𝐞 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 for windowed aggregations. • Seamlessly 𝐛𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞 𝐓𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚𝐒𝐭𝐫𝐞𝐚𝐦 𝐀𝐏𝐈𝐬 to handle complex logic like late-data routing. • Using Flink's built-in Kafka connector with the avro-confluent format for declarative sinking. • Comparing the declarative approach with the imperative DataStream API to achieve the same business goal. • Demonstrating the practical setup using 𝐅𝐚𝐜𝐭𝐨𝐫 𝐇𝐨𝐮𝐬𝐞 𝐋𝐨𝐜𝐚𝐥 and 𝐊𝐩𝐨𝐰 for a seamless Kafka development experience. This is the final post of the series, bringing our journey from Kafka clients to advanced Flink applications full circle. It's perfect for anyone who wants to perform powerful real-time analytics without getting lost in low-level details. Read the article: https://jaehyeon.me/blog/2025-06-17-kotlin-getting-started-flink-table/ Thank you for following along on this journey! I hope this series has been a valuable resource for building real-time apps with Kotlin. 🔗 𝐒𝐞𝐞 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐬𝐞𝐫𝐢𝐞𝐬 𝐡𝐞𝐫𝐞: 1. Kafka Clients with JSON 2. Kafka Clients with Avro 3. Kafka Streams for Supplier Stats 4. Flink DataStream API for Supplier Stats

cool blog from Simon Aubury: https://simonaubury.substack.com/p/when-plans-change-at-500-feet-complex-event-processing[…]n-data-with-apache-flink-52b6381d6a20?triedRedirect=true

New blog: https://rmoff.net/2025/06/24/writing-to-apache-iceberg-on-s3-using-flink-sql-with-glue-catalog/

🎉 Exciting News for Apache Flink Developers! Get ready to dive into the future of real-time computing at #FlinkForward Asia 2025 in Singapore on July 3rd! This one-day event will explore the transformative theme "The Future of AI is Real-Time" with 20+ expert-led talks covering 🔥 Real-time AI 🔥 Real-time Lakehouse 🔥 Real-time Analytics 💡 Featuring cutting-edge projects like Apache Flink 2.0, Flink Agents, Apache Paimon, Flink CDC, and Fluss, you’ll hear insights from leading companies that are adopting streaming technology, including Alibaba, GoTo, Confluent, Shopee, TikTok, Lalamove, Grab, LinkedIn, Ververica, Lazada, and Salesforce. Whether you're attending in person or virtually, this is the ultimate opportunity to connect with the Flink community and shape the future of stream processing! 👉 Register Now: https://asia.flink-forward.org/singapore-2025 📺 Can’t attend onsite? Watch Live Streaming: • YouTube: https://www.youtube.com/live/pzT6vCCmwq8 • LinkedIn: https://www.linkedin.com/events/7339127785510051844/comments/ • Alibaba Cloud: https://resource.alibabacloud.com/webinar/detail.html?id=8851

hey! just wrote this blog post some days ago regarding the HTTP full cache lookups https://www.datanutshell.com/posts/flink_http_full_cache_connector

It's official .. yet another big milestone 🎉 Apache Fluss the streaming storage layer that skyrockets 🚀 Apache Flink is donated to ASF https://github.com/apache/fluss https://fluss.apache.org/ Check it out and don’t forget to give Apache Fluss some ❤️ via ⭐ on Github

𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐢𝐧𝐠 𝐅𝐚𝐜𝐭𝐨𝐫 𝐇𝐨𝐮𝐬𝐞 𝐋𝐨𝐜𝐚𝐥 𝐯2.0: 𝐀 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 & 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦! We're excited to launch a major update to our local development suite. While retaining our powerful Apache Kafka and Apache Pinot environments for real-time processing and analytics, this release introduces our biggest enhancement yet: a new Unified Analytics Platform. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: 🚀 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦: We've merged our Flink (streaming) and Spark (batch) environments. No more separate stacks! Develop end-to-end pipelines on a single Apache Iceberg lakehouse, simplifying management and eliminating data silos. 🧠 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 𝐰𝐢𝐭𝐡 𝐇𝐢𝐯𝐞 𝐌𝐞𝐭𝐚𝐬𝐭𝐨𝐫𝐞: The new system of record for the platform. It saves not just your tables, but your analytical logic—permanent SQL views and custom functions (UDFs)—making them instantly reusable across all Flink and Spark jobs. 💾 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐅𝐥𝐢𝐧𝐤 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Flink checkpoints and savepoints are now persisted directly to MinIO (S3-compatible storage), ensuring robust state management and reliable recovery for your streaming applications. 🌊 𝐂𝐃𝐂-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: The included PostgreSQL instance is pre-configured for Change Data Capture (CDC), allowing you to easily prototype real-time data synchronization from an operational database to your lakehouse. This update provides a more powerful, streamlined, and stateful local development experience across the entire data lifecycle. Ready to dive in? ⭐️ Explore the project on GitHub: https://github.com/factorhouse/factorhouse-local 🧪 Try our hands-on labs: https://github.com/factorhouse/examples/tree/main/fh-local-labs

Hey Flink community! I've been exploring Flink and created beginner-friendly examples using Kafka, SQL, etc. Just wanted to share them here in case it's useful: https://github.com/Ashfaqbs/Apache-Flink Would love feedback and collaboration!

Hi again Flink community 👋 In addition to the beginner-friendly Flink examples I shared earlier, I recently built an Apache Flink MCP Server 🔧 It's a bridge between AI assistants and Apache Flink clusters using the Model Context Protocol (MCP) — now you can monitor and debug Flink jobs using natural language. ✅ Real-time cluster monitoring ✅ Natural language job debugging ✅ Instant metrics and exception access ✅ TaskManager resource monitoring Instead of navigating complex UIs, just ask questions like: “What’s wrong with my cluster?” GitHub: https://github.com/Ashfaqbs/Apache-Flink-MCP-Server Would love feedback and suggestions! Also, I’m interested in contributing this to the wider Flink ecosystem — any advice on the best way to do that? 🙏 Thanks in advance!

Hi Flink community 👋 Join us on July 24, 2025, for the Apache Iceberg Meetup! Dive into the latest trends in streaming and data analytics with industry leaders from AutoMQ and Decodable - exploring how Apache Iceberg is reshaping data lakehouses. 📅 Time: July 24th, 9:00am PDT | 12:00pm EDT | 5:00pm BST 📺 Format: Live 👥 Save your seat: https://lu.ma/akwa6uzh Sign up now, and we’ll send the YouTube live stream link directly to your inbox before the event. Don’t miss insights on: ✅ Building Kafka on S3, by Xinyu Zhou, Co-Founder & CTO at AutoMQ. ✅ Incremental Change Processing with Flink & Iceberg, by Sharon Xie, Head of Product at Decodable. Register today to secure your spot!