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Source channel @githubtrending · Post #15123 · Sep 6

#rust#artificial_intelligence#big_data#data_engineering#distributed_computing#machine_learning#multimodal#python#rust Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns. https://github.com/Eventual-Inc/Daft

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@githubtrending · Post #15204 · 10/08/2025, 11:30 AM

#java#cloud#coap#dashboard#iot#iot_analytics#iot_platform#iot_solutions#java#kafka#lwm2m#microservices#middleware#mqtt#netty#platform#snmp#thingsboard#visualization#websockets#widgets ThingsBoard is an open-source IoT platform that helps manage and analyze data from connected devices. It allows users to collect data, create real-time dashboards, and automate tasks using a powerful rule engine. This platform supports various protocols like MQTT and HTTP, making it easy to connect devices. Users can also define relationships between devices and assets, and trigger alarms based on specific conditions. The benefit is that it simplifies IoT project development, making it scalable and efficient for applications like smart farming, smart offices, and more. https://github.com/thingsboard/thingsboard