Compare Eclipse Streamsheets vs. myDevices

Create advanced applications that streamline workflows, provide ongoing operational monitoring, and manage processes in real-time. Your solutions are designed to operate continuously on cloud servers as well as edge devices. Utilizing a familiar spreadsheet interface, you don't need to be a programmer; instead, you can simply drag and drop data, enter formulas into cells, and create charts in an intuitive manner. All the essential protocols required for connecting to sensors and machinery, such as MQTT, REST, and OPC UA, are readily available. Streamsheets specializes in processing streaming data, including formats like MQTT and Kafka. You can select a topic stream, modify it as needed, and send it back into the vast world of streaming data. With REST, you gain access to a multitude of web services, while Streamsheets enables seamless connections both ways. Not only do Streamsheets operate in the cloud and on your servers, but they can also be deployed on edge devices, including Raspberry Pi, expanding their versatility to various environments. This flexibility allows businesses to adapt their systems according to their specific operational needs.

Supports secure connections with devices that utilize HTTP and MQTT protocols, while receiving data from LoRa Network Servers as well as streams from various IoT Clouds. This versatile serverless computing environment, often referred to as function as a service (FaaS), offers online editing capabilities along with codecs and integrations. It processes and normalizes incoming data from devices, translating uplink messages and encoding downlink commands to streamline integration function deployment. The system efficiently manages device registration, configuration, provisioning, and facilitates Firmware Over The Air (FOTA) scheduling and batching. Users can easily deregister and reregister devices through the LNS Switch feature. It securely stores LoRaWAN keys and SSL/TLS certificates, providing access to real-time data insights. With exceptional performance, it can handle large volumes of data, allowing for quick queries across billions of telemetric and historical records. Capable of ingesting millions of data points in a second, it also offers vertical and horizontal scalability driven by a robust data streaming processing engine. Additionally, this architecture ensures that data management remains efficient and responsive, adapting to the ever-growing demands of IoT applications.