Apache IoTDB

📣 Call for Proposals is now open! Community Over Code Asia 2026 (formerly The Apache Software Foundation) will take place in Beijing, August 7–9, 2026. As a global conference hosted by Apache Software Foundation, it brings together open-source contributors, developers, and communities across 350+ Apache projects to share real-world experiences and explore the future of open technology. From the Apache IoTDB community, we warmly welcome topics including: 🥣 Time-series data & data infrastructure 🥣 IoT & industrial data systems 🥣 Real-world use cases and architecture practices 🥣 Open-source community building If you have something you’ve built, learned, or explored, this is a great place to share it with the broader Apache ecosystem. 📍 Beijing, Aug 7–9, 2026 📝 Submit your proposal: https://lnkd.in/gjscg-wm Looking forward to seeing ideas from the community 🌱 #Apache #OpenSource #CommunityOverCode #CFP #Community #Proposal #Database #Apache #TimeSeries #Infra #Data

Big news in Time Series AI! 📈 The first 1B+ parameter Time Series Foundation Model, Timer-S1, has officially arrived! Key highlights of this breakthrough: 🔹 Extreme Scale: 1B+ parameters, breaking the scaling bottleneck for time series. 🔹 Long Memory: 11.5K context length to capture deep historical patterns. 🔹 Massive Data: Trained on 1 Trillion time points (TimeBench). 🔹 SOTA Performance: Set new records on the GIFT-Eval benchmark (MASE & CRPS). By introducing the Serial Scaling Paradigm with TimeMoE and TimeSTP, Timer-S1 solves the long-standing trade-off between efficiency and long-term accuracy. This marks a new era for industrial predictive intelligence and large-scale forecasting. 🚀 Check out the paper: "Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling": https://lnkd.in/g3c4-z2y #AI #TimeSeries #MachineLearning #Innovation #TimerS1 #DeepLearning #DataScience #FoundationModels #Database #Forecasting #Tsinghua

Join us at HANNOVER MESSE 2026 Hall 26 Booth C10B to see Apache IoTDB - the open-source time-series database powering industrial data management for smart factories and energy systems. #ApacheIoTDB #HannoverMesse2026 #TimeSeries #Database #Energy #OpenSource #Event #AI #Industry #Data #DevOps #DataManagement

⏰ A gentle reminder: The application deadline for Google Summer of Code 2026 is March 31 (18:00 UTC). If you're interested in contributing to Apache IoTDB, this is the final stage to submit your proposal. After this, organizations will begin reviewing and ranking submissions, with accepted projects announced in late April. 📌 You can find more details and guidance here: https://lnkd.in/g3kgbr5B If you’ve been preparing your proposal, now is a good time to finalize it. Wishing everyone the best of luck — hope you all get accepted and work on exciting projects! #GSoC2026 #ApacheIoTDB #OpenSource #Developers #Google #Application #Program #Database #AI

Unlock High-Performance Data Governance with Apache IoTDB Table Model 📊 Managing massive industrial datasets requires more than just storage; it requires structured analysis. Apache IoTDB’s Table Model is specifically optimized for device management and complex analysis scenarios. Why choose Table Model? 🔹 Standard SQL Support: Seamlessly migrate from relational databases and perform rich analytics. 🔹 Rich Metadata Management: Use Attributes for static info and Tags for high-efficiency device filtering. 🔹 Unmatched Scalability: Support for hundreds of thousands of columns (Fields) per table. 🔹 Template-Based Governance: Simplifies data management across thousands of similar device types. Whether you're handling DCS monitoring or deep historical trend analysis, IoTDB provides the dual-model flexibility to meet your needs. #ApacheIoTDB #IndustrialIoT #TimeSeriesDatabase #DataGovernance #Analytics #SmartFactory #TimechoDB #SQL #Query

🗞️ Here's your weekly ASF release roundup! 🗞️ 👉 Apache TsFile 2.2.1 is now available for download: https://buff.ly/DXMIAz9 TsFile is a high-performance columnar storage file format designed for industrial time-series data, featuring multi-language interfaces, high compression ratios, high read/write throughput, and fast random access capabilities. 👉 Apache Storm version 2.8.5 is now available for download: https://buff.ly/6KqmSOX Apache Storm is a distributed, fault-tolerant, and high-performance realtime computation system that provides strong guarantees on the processing of data. 👉 Apache ActiveMQ 6.2.2 has been released. This is a maintenance release on the ActiveMQ 6.2.x series, including: ✅️ improve FactoryFinder validation ✅️ fix Jolokia runtime ✅️ upgrade to Jackson 2.21.1 You can download ActiveMQ 6.2.2 here: https://buff.ly/FVMMdXs 👉 Apache ActiveMQ 5.19.3 has also been released and is now available for download: https://buff.ly/FVMMdXs 👉 Apache Tika is a toolkit for detecting and extracting metadata and structured text content from various documents using existing parser libraries. Tika 3.3.0 is now available for download: https://buff.ly/wdoXuIj 👉 Apache Tomcat 10.1.53 is now available for download: https://buff.ly/TUlWy4H Tomcat 10 is an open source software implementation of the Jakarta Servlet, Jakarta Pages, Jakarta Expression Language, Jakarta WebSocket, Jakarta Authentication and Jakarta Annotations specifications. 👉 Apache RAT is a release audit tool. It improves accuracy and efficiency when checking releases. It is heuristic in nature: making guesses about possible problems. It will produce false positives and cannot find every possible issue with a release. Apache Creadur RAT 0.18 is now available for download: https://buff.ly/LWBl7qI 👉 Apache Maven Daemon version 1.0.5 is now available for download: https://buff.ly/ClFCVgC This release provides binaries based on Maven 3.9.14. This release is functionally equivalent to 1.0.4, the only change is added Linux Arm64 support. 👉 Apache ShardingSphere ElasticJob-3.0.5 is now available for download: https://buff.ly/OQzZbT2 ElasticJob is a distributed scheduling solution. Through the functions of flexible scheduling, resource management and job management, it creates a distributed scheduling solution suitable for Internet scenarios, and provides diversified job ecosystem through open architecture design. It uses a unified job API for each project. Developers only need code one time and can deploy at will. #opensource #data #machinelearning #cloudcomputing #java #NoSQL #webserver #hadoop

Timer-S1: Scaling Time Series Forecasting to the Billion-Parameter Level Long-horizon forecasting is still a major challenge: error accumulation, non-stationarity, and heterogeneous data often break conventional models. Timer-S1 addresses this with a systematic approach: - Billion-scale model with up to 11.5K context length - TimeSTP module for multi-step forecasting in a single forward pass (no rolling inference) - Serial scaling paradigm integrating architecture, data, and training - TimeBench dataset: 1 trillion time points across IoT, finance, healthcare, and synthetic data - Multi-stage training pipeline optimized for both short- and long-horizon accuracy The result: state-of-the-art performance on GIFT-Eval, with particularly strong mid- and long-horizon forecasting results — the very scenarios where most models struggle. Timer-S1 represents a step toward general-purpose time series foundation models, signaling a shift from task-specific solutions to systematic, scalable approaches. Read the full report for details: https://lnkd.in/g3c4-z2y #Tsinghua #Bytedance #ApacheIoTDB #Benchmark #TimeSeries #Forecasting #Model #Timer #Machinelearning #SOTA

🌲 Explore the power of Apache IoTDB Tree Model for time-series data management: - Hierarchical paths correspond 1:1 with physical measurement points - Flexible like a file system for designing custom branches - Ideal for industrial monitoring scenarios such as DCS & SCADA 🪮 Maximize efficiency in multi-device data handling while keeping your structure intuitive and scalable. #Database #Datas #BigData #DataManagement #Monitoring #Infra #TimeSeries #Industry #Devices #Sensors

🧩 Scaling time-series data goes far beyond simply adding storage. In industrial environments—energy, manufacturing, transportation—the real challenges are: - Managing billions of data points - Maintaining query performance - Ensuring high availability under continuous ingestion This is where Apache IoTDB takes a systems-level approach. This article breaks down how IoTDB addresses these challenges through: - Two-dimensional partitioning (time + series) for efficient data layout - Load-balanced distribution across ConfigNode and DataNode - Hybrid consistency model (Ratis + IoTConsensus) balancing correctness and throughput - Streaming-based cross-cluster synchronization for disaster recovery and real-time processing It also walks through practical architectures, including: - Geo-redundant backup - Edge–cloud data pipelines - Integration with systems like Apache Flink If you're designing infrastructure for large-scale time-series workloads, this is a solid deep dive into the trade-offs behind partitioning, replication, and reliability. Full report: https://lnkd.in/gqhtMYXn #Database #ApacheIoTDB #TimeSeries #Industry #Data #Infra #Sensor #DataManagement #TimeSeries