Databricks recently held a webinar on how to use Delta Lake to unify batch and streaming data processing, ensure data is up-to-date with Structured Streaming, and manage data for GDPR compliance
Have you ever tried to transfer 1M of data when your IoT device has only 2K of RAM?
If so, then this IoT show is for you! (aka.ms/iotshow/ulib)
Marcos Perez Mokarzel and Dane Walton, Software Developers on the Azure Device SDK team, appear in this episode to talk about the uStream library, written in the C language.
uStream is an Azure utility library for embedded and constrained devices. It enables developers to expose and use large amounts of data without the need for large amounts of memory.
Marcos gives an overview of the constrained memory use cases that IoT device developers face and Dane steps through how the uStream library solves these issues. Dane presents a system level overview of the uStream library and demos ustream_read(), ustream_clone(), and more .
In our previous episodes of the AI Show, we’ve learned all about the Azure Anomaly detector, how to bring the service on premises, and some awesome tips and tricks for getting the service to work well for you.
In this episode of the AI Show, Qun Ying shows us how to build an end-to-end solution using the Anomaly Detector and Azure Databricks. This step by step demo detects numerical anomalies from streaming data coming through Azure Event Hubs.
Anomaly Detection on Streaming Data Using Azure Databricks Related Links
- Check out the detailed tutorial for the solution we’ve built in this episode from https://aka.ms/adTutorialADB.
- Check out a simple demo on https://aka.ms/adDemo
- Check out the overview of the API service on https://aka.ms/AnomalyDetector.
- Create your first Anomaly Detector resource on Azure: https://aka.ms/adNew.
- Join Anomaly Detector Containers preview through https://aka.ms/adContainer.
- Join “Anomaly Detector Advisors” public community to connect with the product team and other members in the community through https://aka.ms/adAdvisorsJoin.
Azure Stream Analytics is a fully managed serverless offering on Azure. With the new Anomaly Detection functions in Stream Analytics, the whole complexity associated with building and training custom machine learning (ML) models is reduced to a simple function call resulting in lower costs, faster time to value, and lower latencies.
Patrick LeBlanc looks at how to create a Power BI streaming dataset and use that to create a real-time dashboard. You can easily use something like PowerShell to push data into the Power BI streaming dataset. [Demo files]
To help securely connect to and manage IoT devices located behind firewalls or inside private networks, Azure IoT Hub team introduces Azure IoT Hub device streams offering secure general-purpose communication tunnels through the Azure cloud to IoT devices.
Check out this latest episode of the Internet of Things Show., Reza Sherafat Kazemzadeh, Sr PM for Azure IoT Hub, showcasing the new capabilities.
Learn more about IoT Hub Device Streams: https://azure.microsoft.com/en-us/blog/introducing-iot-hub-device-streams-in-public-preview/
Check out the docs: https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-device-streams-overview
Create a Free Account (Azure): https://aka.ms/aft-iot
Here’s a great ten minute video from Hortonworks explaining the purpose of Apache Storm for Hadoop.