RisingWave

Rust is becoming the language of modern data infrastructure! ✅ Memory safety ✅ Async support ✅ Performance ✅ Correctness ✅ Community These are some of the reasons why more and more database and data infrastructure projects are choosing Rust. Some of the Rust-based projects include Apache DataFusion, Neon Postgres, Greptime, SurrealDB, Polars, ParadeDB, HelixDB, LanceDB, Qdrant, Databend, turbopuffer, Turso, TiKV from TiDB, powered by PingCAP, SmithDB from LangChain, HydraDB, Readyset, and InfluxData. And the ecosystem keeps growing! RisingWave is a Rust-based real-time data platform that allows you to build Iceberg-based streaming lakehouses using a Postgres interface. What's your favorite Rust-based database system? #rust #rustlang #programming

How did a Rust project called Icelake evolve into iceberg-rust, the official Rust implementation of Apache Iceberg? It all begins with Hao Ding (Xuanwo) creating Icelake to bring native Rust support to Apache Iceberg while working at Databend. At that time, we were implementing Java-based sinks in RisingWave, but later joined Icelake and started contributing, along with Renjie Liu, Zentome, and other great contributors. Then, in July 2023, Brian Оlsen started a discussion on the Apache Iceberg dev mailing list about native Rust support. Jan Kaul, joined the conversation, pointed to Icelake, and proposed bringing the efforts together in an official Apache Iceberg Rust project. That led to the launch of iceberg-rust on July 20, 2023. At that time, Fokko Driesprong also helped set up the #rust Slack channel, giving the community a place to collaborate around the project. From there, the project grew quickly, with a growing community of contributors. In 2024, Icelake was sunset and archived as users moved to iceberg-rust. Today, iceberg-rust has: ✅ Around 1.4K stars ✅ 215 contributors ✅ 11 releases iceberg-rust is now used by RisingWave, Databend, Lakekeeper, Mooncake's Moonlink, Apache DataFusion Comet, Spice AI, Supabase ETL, LakeOps, and others for their Iceberg implementations. Our developer advocate, Fahad Shah, will share this story at the upcoming Apache Doris Summit, explaining how RisingWave became part of it and has used iceberg-rust since then to implement native Apache Iceberg support in RisingWave. He'll also show a demo of how to build a streaming Rust lakehouse using REST Catalog to create Apache Iceberg tables, then query those tables with Apache Doris. Register for the online Summit organized by VeloDB (Powered by Apache Doris), happening on October 21–22: https://lnkd.in/gqfRvn9P

What does it take to build an open Rust Lakehouse, and make it work across different data systems? It starts with open-source collaboration. What began as Icelake grew into iceberg-rust, the official Rust implementation of Apache Iceberg, through contributions from the RisingWave team and the wider Apache Iceberg community. Today, the project has grown to around 199 contributors and is being adopted across the data ecosystem. At Apache Doris Summit 2026, Fahad Shah, Developer Advocate at RisingWave, will share the story behind iceberg-rust and show how to put the open Lakehouse ecosystem into practice. The session will explore how RisingWave builds a streaming Rust Lakehouse, using the Lakekeeper REST Catalog to create Apache Iceberg tables, then querying those tables with Apache Doris. From the evolution of an open-source project to a hands-on demonstration of streaming data and cross-engine querying, this talk highlights what becomes possible when open technologies work together. 🤝 We're excited to have our ecosystem partner, RisingWave, join the Summit and share these practical insights with the Apache Doris community. A big thank-you to Fahad and the RisingWave team for contributing to the broader open Lakehouse ecosystem! 📅 October 21–22, 2026 🌐 Online & Free 🔗 Register: https://lnkd.in/g8dnDgnU #ApacheDorisSummit2026 #RisingWave #ApacheIceberg #Lakehouse #OpenSource

Data doesn't always fit a fixed schema, and that's where the VARIANT data type comes in, allowing databases to handle flexible, complex, and hierarchical data structures. With RisingWave 3.1, we're introducing VARIANT as a first-class data type for natively storing and handling semi-structured data. VARIANT is particularly useful for workloads where schemas can vary or evolve over time, such as IoT events, APIs, application telemetry, event streams, lakehouse data, AI workloads, and data exchanged between systems such as Snowflake, Apache Iceberg, and Doris. RisingWave now also supports reading Iceberg v3 VARIANT columns through Iceberg sources and refreshable Iceberg tables, though writing VARIANT data through Iceberg sinks is not yet supported. Similar to other data systems like Databricks, VARIANT cannot be used directly for operations such as primary keys, GROUP BY, ORDER BY, DISTINCT, or equality-based join keys. Have a stream processing or streaming lakehouse use case that could benefit from VARIANT support? Need any help, join our community: https://lnkd.in/eW_gjzqx

Your AI agents can write SQL. RisingWave turns that SQL into continuously running data pipelines. KLP Eiendom AS put that into practice. Truong Le rebuilt KLP’s Sesam hub connecting 16 systems with RisingWave at the core: the original estimate was at least a year of work. In fact, the core build took a couple of months, followed by month of testing and parallel operation before Sesam was switched off. What makes this interesting goes beyond the timeline. With RisingWave, the business logic lives in SQL. AI agents can help write and refine it. DBT supports building and deploying it. RisingWave’s MCP server and agent skills let those assistants work directly with the database. It's a practical path from requirements to SQL to running pipelines, with AI involved throughout. Read Truong’s story in kode24: https://lnkd.in/gz7qXJcd It’s in Norwegian, so let your AI assistant warm up by translating it before tackling your pipelines. 😉 Replacing Sesam or building data pipelines with AI? DM me — let’s explore where RisingWave fits.

Why is Apache Arrow becoming the universal columnar foundation for modern data and AI infrastructure? Apache Arrow is great because of its language-independent columnar memory format, zero-copy data interchange, efficient in-memory processing, and broad multi-language ecosystem. That’s why projects like Apache Spark, Apache Parquet, ClickHouse, Dremio, Polars, Pandas, InfluxData, OpenObserve, Greptime, LakeSail, Spice AI, IOMETE, Daft, Apache Doris, and so many other great systems use it. For data and AI systems, Arrow is not just another format. It is becoming a foundational layer for moving and processing data efficiently across the entire ecosystem. RisingWave, a unified data platform for real-time and Iceberg-based lakehouse workloads, also uses Arrow extensively, particularly for interoperability with Apache Iceberg, Apache DataFusion, Parquet, UDFs, and some connectors. Shoutout to our partner Columnar for advancing Apache Arrow and ADBC. They're really doing great work and redefining database access and connectivity. 🙌 Want to build your stream processing applications and streaming lakehouses? Join the community: https://lnkd.in/eW_gjzqx

Reading Apache Iceberg v3 VARIANT columns, including nested variants, through Iceberg sources and refreshable Iceberg tables is now supported in RisingWave v3.1. This builds on our multiple years of continuous investment in native Apache Iceberg support: ✅ Rust-based sinks and sources ✅ Iceberg Table Engine for native Iceberg tables ✅ Rust-based compaction ✅ Exactly-once delivery ✅ REST catalog support along with other popular catalogs ✅ Schema evolution ✅ Nimtable-based observability platform ✅ Apache DataFusion-based batch query engine for Iceberg tables RisingWave brings together: ➡️ Postgres compatibility ➡️ Streaming-native processing ➡️ Apache Iceberg as the open table layer ➡️ Support for all popular data sources and sinks ➡️ Stream processing and batch workloads on one platform Our goal is to make RisingWave a unified data platform for stream processing and lakehouse workloads. Writing VARIANT through the Iceberg sink and creating Iceberg tables with VARIANT columns are not yet supported. Want to build Streaming Lakehouses? Join our community: https://lnkd.in/eW_gjzqx

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Huge thanks to StreamNative for organizing Data Streaming Summit (DSS) 2026, and to all the members of the DSS program committee, the sponsors, speakers, and attendees who made it a success. Our CPO, Rayees Pasha, participated in the keynote and also co-presented a talk with Qiang Zhao of StreamNative about how to query your data streams using Postgres-style SQL in StreamNative's SQL Workspace. We're really looking forward to the upcoming Current conference in San Francisco on November 4–5 and hope to see you there!

That’s a wrap on Data Streaming Summit 2026! 🎉 A huge thank you to everyone who joined us in San Francisco for two incredible days of building, learning, sharing, and connecting. From the Data+Agent Hackathon to a full day of keynotes, technical sessions, and conversations around the future of data infrastructure and AI agents, DSS 2026 was made possible by an amazing community. Thank you to all of our attendees, speakers, sponsors, hackathon judges, program committees, partners, and team members who helped bring the event to life. We’re grateful to have shared DSS 2026 with all of you — and we can’t wait to see where these conversations and connections go next. See you at the next Data Streaming Summit! 🚀 #DSS2026 #DataStreaming #AI #DataInfrastructure #DataAndAgents