TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.
 
<ul><li>10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.</li><li>1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.</li><li>Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust.</li><li>Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.</li><li>Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.</li><li>Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.</li></ul><div> </div>

TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.

TDengine -  Big data platform designed and optimized for the Internet of Things

PLC4X is a set of libraries for communicating with industrial programmable logic controllers (PLCs) using a variety of protocols but with a shared API. It allows you to communicate directly with your industrial Hardware, without having to retrofit it. It implements drivers for the most common industrial communication protocols.
  

Apache PLC4X - Create a set of libraries, that allow unified access to any type of PLC

Eclipse Ditto is an open source framework helping you to build digital twins of devices connected to the internet. It is by design domain agnostic and thus may be used in
industrial, residential, agricultural and many other IoT domains. A digital twin is a virtual, cloud based, representation of his real world counterpart (real world “Things”, e.g. devices like sensors, smart heating, connected cars, smart grids, EV charging station)

Eclipse Ditto - An open source framework helping you to build digital twins of devices connected to the internet

Apache StreamPipes is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. It can Integrate data sets and data streams using the built-in StreamPipes Connect library with support for generic protocols such as HTTP, Kafka, MQTT, OPC-UA, Files or specific adapters for open data sources.
 
It enables flexible modeling of stream processing pipelines by providing a graphical modeling editor on top of existing stream processing frameworks. It empowers non-technical users to quickly define and execute processing pipelines based on an easily extensible toolbox of data sources, data processors and data sinks. StreamPipes has an exchangeable runtime execution layer and executes pipelines using one of the provided wrappers, e.g., standalone or distributed in Apache Flink.

Apache StreamPipes is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. It can Integrate data sets and data streams using the built-in StreamPipes Connect library with support for generic protocols such as HTTP, Kafka, MQTT, OPC-UA, Files or specific adapters for open data sources.

Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams

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TDengine - Big data platform designed and optimized for the Internet of Things

Apache PLC4X - Create a set of libraries, that allow unified access to any type of PLC

Eclipse Ditto - An open source framework helping you to build digital twins of devices connected to the internet

Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams

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TDengine - Big data platform designed and optimized for the Internet of Things

Apache PLC4X - Create a set of libraries, that allow unified access to any type of PLC

Eclipse Ditto - An open source framework helping you to build digital twins of devices connected to the internet

Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams

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