Posted on

How to Connect Azure IoT Hub and CrateDB Cloud to Ingest IoT Sensor Data

This article will describe how to launch a CrateDB cluster on Azure, connect it to Azure IoT Hub, and test it by ingesting simulated sensor data using an Azure IoT Solution Accelerator.

Step #1: Simulating Sensor Data

It’s necessary to first understand the type of data your IoT application will produce in order to accurately mimic this information for testing purposes. Smart factories, for example, will have myriad sensors that collect data in a variety of structures. CrateDB makes it possible to model different data structures in a single table through the use of dynamic objects, which can be queried to an arbitrary depth (this is not a recommended practice for production, but is helpful within the simple confines of this demonstration).

Posted on

Setting Up a CrateDB Cluster With Kubernetes to Store and Query Machine Data

Because of its horizontally scalable shared-nothing architecture, the CrateDB open source database is well-suited for working with Kubernetes. Setting up a CrateDB cluster with Kubernetes can be done in just a few steps, and scaling up and down is straightforward – making the cluster particularly flexible. This step-by-step tutorial will show you how to get CrateDB and Kubernetes working together.

CrateDB is used for real-time machine data processing, monitoring, and analytics. The open source database is suited for applications with high volumes of machine data (like anomaly detection), log data (like ecommerce), network data (like capacity planning), and IoT/IIoT data (like smart manufacturing, smart home products, and fitness gear). However, this database is probably not what you want to use if you require strong (ACID) transactional consistency or highly normalized schemas with many tables and joins.