The CrateDB enterprise database for time series, documents and vectors is working with German company HiveMQ on a joint offering that collects large volumes of IoT data via MQTT.

MQTT is a lightweight messaging protocol suitable for IoT application. The offering stores the IoT data in a scalable distributed time series database.

This way, users can communicate reliably across connected devices, and control their IoT data with confidence to increase the efficiency of business initiatives across multiple locations. It provides historical and real-time data and can handle various data formats securely, thus increasing the effectiveness of use cases especially in manufacturing, automotive, energy, utilities, transportation and logistics.

“The collection and storage of real-time data are the number one priority for IoT companies, so they can leverage the power of those data for improved business decisions and RoI,” said Matt Dowling, head of global alliances at HiveMQ. “We want to make this process as easy as possible for our customers, and partnering with CrateDB gives them a plug-and-play option to handle their IoT data and use them to power advanced use cases like AI, ML and predictive maintenance.”

HiveMQ and CrateDB’s joint offering delivers a data management architecture beyond pure time series, able to handle structured, semi-structured and unstructured data, while using standard protocols such as MQTT and query languages such as SQL. Engineered for optimal performance across edge, on-premises and multi-cloud environments, this ensures high availability and reliability for continuous operations, with HiveMQ’s security protocols safeguarding data integrity.

Organisations seek improved methods to integrate IoT infrastructure software and data within current enterprise and industrial systems. Enhancing this integration facilitates the deployment of IoT applications and enhances the benefits derived from in-depth, real-time analysis of IoT data.

HiveMQ (hivemq.com) and CrateDB (cratedb.com) can jointly scale to meet growing business demands, while real-time data processing enables instantaneous insights, crucial for agile decision-making. This synergy offers data observability and granularity, transforming how businesses leverage data for operational excellence for real-time analytics, digital-twin initiatives, and AI and ML applications.

Predictive maintenance with AI and ML increases the efficiency of building and training of models that power AI and machine learning. It analyses both historical and real-time data and enables predictive maintenance in multiple industries.

A seamless flow of high-velocity data is essential for accurate digital twin modelling. The integration ensures digital representations of physical assets, supporting complex and dynamic datasets with scalable architecture.

“We are very excited to have HiveMQ joining our expanding partner ecosystem,” said Steve van den Berg, senior vice president at CrateDB. “Together we will deliver incremental value to the IoT sector with real-time analytics bringing hours to minutes, and minutes to single digit milliseconds.”