MEAN.js with Cosmos DB on Azure

(a YouTube series by John Papa)

Cosmos DB is of significant interest to myself for projects I have been engaged in for the past couple of years which use MongoDB and MEAN in several ways. Scaling for us has always been a bit of a pain with MongoDB, and Cosmos DB on Azure looks to be relieving a lot of the headaches we have had.

MEAN stands for MongoDB, Express, Angular and Node.

I am not the author of these – this is a reference list to a YouTube series by John Papa introducing MEAN with Cosmos DB on Azure. I would normally just link directly to the creators blog or post for a series such as this, but it seems to be offline just now so I thought I would share a full list of current videos here – hopefully the original link will work again soon – which is https://johnpapa.net/angular-cosmosdb-1/.


MEAN.js with Cosmos DB – Part 1: Introduction

John builds a lot of apps with MongoDB, Express, Angular and Node (MEAN). MongoDB just works so well with these, but recently he has been using Cosmos DB on Azure in its place because it’s easy to use, scale, is super fast, and he does not have to change how he codes.


MEAN.js with Cosmos DB – Part 2: Creating the Node.js and Express App

Creating a Node.js and Express App along with the Angular CLI. Then create a web API endpoint and try it out.


MEAN.js with Cosmos DB – Part 3: Angular and Express APIs

The A in MEAN stands for Angular. This video shows how to build an Angular UI that talks to the Express API, with GET, POST, PUT, and DELETE.


MEAN.js with Cosmos DB – Part 4: Creating and Deploying Cosmos DB

Using the Azure CLI, to create the Cosmos DB account to represent a MongoDB model database and deploy it to Azure. Then view what we created in the Azure portal.


MEAN.js with Cosmos DB – Part 5: Querying Cosmos DB

How to connect to the MongoDB database with Azure Cosmos DB, using Mongoose, and query it for data.

You can subscribe to John’s YouTube series at https://www.youtube.com/playlist?list=PLbnXt_I6OfBWU9JiDNewZm11-7eFQf70M or follow him on twitter @John_Papa

Second version of HoloLens HPU will incorporate AI coprocessor for implementing DNNs

HPU_2.0_1260x539-1024x438.png

Posted July 23, 2017 | by Microsoft Research Blog

By Marc Pollefeys, Director of Science, HoloLens

It is not an exaggeration to say that deep learning has taken the world of computer vision, and many other recognition tasks, by storm. Many of the most difficult recognition problems have seen gains over the past few years that are astonishing.

Although we have seen large improvements in the accuracy of recognition as a result of Deep Neural Networks (DNNs), deep learning approaches have two well-known challenges: they require large amounts of labelled data for training, and they require a type of compute that is not amenable to current general purpose processor/memory architectures. Some companies have responded with architectures designed to address the particular type of massively parallel compute required for DNNs, including our own use of FPGAs, for example, but to date these approaches have primarily enhanced existing cloud computing fabrics.

But I work on HoloLens, and in HoloLens, we’re in the business of making untethered mixed reality devices. We put the battery on your head, in addition to the compute, the sensors, and the display. Any compute we want to run locally for low-latency, which you need for things like hand-tracking, has to run off the same battery that powers everything else. So what do you do?

You create custom silicon to do it.

First, a bit of background. HoloLens contains a custom multiprocessor called the Holographic Processing Unit, or HPU. It is responsible for processing the information coming from all of the on-board sensors, including Microsoft’s custom time-of-flight depth sensor, head-tracking cameras, the inertial measurement unit (IMU), and the infrared camera. The HPU is part of what makes HoloLens the world’s first–and still only–fully self-contained holographic computer.

Today, Harry Shum, executive vice president of our Artificial Intelligence and Research Group, announced in a keynote speech at CVPR 2017, that the second version of the HPU, currently under development, will incorporate an AI coprocessor to natively and flexibly implement DNNs. The chip supports a wide variety of layer types, fully programmable by us. Harry showed an early spin of the second version of the HPU running live code implementing hand segmentation.

The AI coprocessor is designed to work in the next version of HoloLens, running continuously, off the HoloLens battery. This is just one example of the new capabilities we are developing for HoloLens, and is the kind of thing you can do when you have the willingness and capacity to invest for the long term, as Microsoft has done throughout its history. And this is the kind of thinking you need if you’re going to develop mixed reality devices that are themselves intelligent. Mixed reality and artificial intelligence represent the future of computing, and we’re excited to be advancing this frontier.

Source: https://www.microsoft.com/en-us/research/blog/second-version-hololens-hpu-will-incorporate-ai-coprocessor-implementing-dnns/

AI for security: Microsoft Security Risk Detection makes debut

Full details at: – https://blogs.microsoft.com/next/2017/07/21/ai-for-security-microsoft-security-risk-detection-makes-debut/

Microsoft is making a cloud service that uses artificial intelligence to track down bugs in software generally available, and it will begin offering a preview version of the tool for Linux users as well.

Microsoft Security Risk Detection, previously known as Project Springfield, is a cloud-based tool that developers can use to look for bugs and other security vulnerabilities in the software they are preparing to release or use. The tool is designed to catch the vulnerabilities before the software goes out the door, saving companies the heartache of having to patch a bug, deal with crashes or respond to an attack after it has been released.

New M Functionality And Behaviour In Power BI Custom Data Connectors

Chris Webb's BI Blog

Over the past few weeks I’ve spent some time playing around with Power BI custom data connectors and while I don’t have anything to share publicly yet (other people are way ahead of me in this respect – see the work of Igor Cotruta, Miguel Escobar and Kasper de Jonge among others) I have learned some interesting things that are worth blogging about.

First of all, the data privacy rules around combining data from different data sources do not apply in custom data connector code. As the docs say here:

Data combination checks do not occur when accessing multiple data sources from within an extension. Since all data source calls made from within the extension inherit the same authorization context, it is assumed they are “safe” to combine. Your extension will always be treated as a single data source when it comes to data combination rules. Users would…

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Azure SQL Data Warehouse: Troubleshoot with the Resource Health check

Azure DW Resource HealthAzure DW Resource Health2 New update for Azure SQL Data Warehouse…

Reduce troubleshooting time with the upgraded Resource Health check for SQL Data Warehouse.

This upgrade considers the health status of all components of the SQL Data Warehouse architecture, which includes each SQL database distribution and the SQL Data Warehouse engine on each compute node. Login and heartbeat signals of each component are emitted at least once every 2 minutes, providing you a low-latency, holistic view of the health status of your data warehouse. If your instance is Unavailable, we will provide the reason along with recommended actions that you should perform.

The Resource Health check can detect unavailability reasons, such as when your instance is pausing, scaling, or upgrading. This feature also detects when there are any connection issues, whether they are user connections or inner SQL database connections.

You check the health of SQL Data Warehouse by signing in to the Azure portal and clicking the Resource Health blade.

Source: – https://azure.microsoft.com/en-us/updates/azure-sql-data-warehouse-troubleshoot-with-the-resource-health-check/