TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license. 
 
TimescaleDB automatically scales and spreads data written to a hypertable into many dynamically-created chunks. Hypertables present the abstraction of a single table, yet transparently partitioned by time and space, they enable high insert rates even at billions of rows.

 TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license. 

Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

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

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

Apache Edgent is a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the continuous streams of data coming from equipment, vehicles, systems, appliances, devices and sensors of all kinds (for example, Raspberry Pis or smart phones).  Apache Edgent provides efficient and timely analytics across the whole IoT ecosystem: from the center to the edge.

Apache Edgent - Accelerating Analytics at the Edge

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Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

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

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

Apache Edgent - Accelerating Analytics at the Edge

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Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

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

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

Apache Edgent - Accelerating Analytics at the Edge

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