Route data from Azure IoT Hub or Event Hubs directly to CrateDB
Ingest Industrial Time Series Data from Event Hubs
CrateDB integration with Microsoft Event Hubs makes it easier to integrate and analyze industrial time series data at scale.
CrateDB Event Hubs Connector is a new capability that allows users to route data from Azure IoT Hub or Azure Event Hubs directly to CrateDB. This makes it even easier to integrate and analyze IoT data in real time in order to monitor, predict, or control the behavior of smart systems.
The Connector can scale to accept millions of telemetry data readings per second from Event Hubs or IoT Hub and insert it into the CrateDB SQL DBMS. Dynamic Schema support in CrateDB enables new message structures, even nested JSON objects, to be inserted easily; CrateDB updates the table schema on the fly, making it easy to integrate new types of connected devices.
Microsoft’s Azure platform offers a wealth of different messaging services, starting from a reliable and feature-rich Azure ServiceBus to the lightweight and high-throughput Azure Event Hubs. CrateDB was built to analyze industrial time series data; it’s a perfect fit for ingesting streams of machine data out of Azure IoT Hub - which offers an Event Hubs endpoint.
Using Event Hubs with CrateDB
Azure Event Hubs is built for large scale messaging and handling streams of data, such as industrial IoT data from smart factories or smart cities infrastructure. Data streaming through Event Hubs can be passed to Azure Functions for further enrichment or transformation. Once that’s done, the processed data is captured into CrateDB for analysis.
Easier OT-IT convergence to give time series data context from ERP, CRM, HRIS, and other enterprise systems.
Build predictive ML/AI models with incoming stream of data
Analyze machine data in real-time using SQL
Scale out to handle orders of magnitude more time series data or concurrent queries
Putting Machine Data to Work
The success of large-scale Industry 4.0 initiatives depends completely on users’ systems’ abilities to ingest millions of data points from connected equipment and for them to analyze and action that data in real time.
This machine-generated data can be used to create analytical models that infer details about the device the sensor is monitoring. Using in-memory indexing, parallel processing, and an advanced concurrency model, CrateDB can perform these analytics on data as it flows into the database, and scale it out to support many concurrent users and inbound data flows.