20

Mar

Microsoft Azure for the Gaming Industry
Microsoft Azure for the Gaming Industry

This blog post was co-authored by Patrick Mendenall, Principal Program Manager, Azure. 

We are excited to join the Game Developers Conference (GDC) this week to learn what’s new and share our work in Azure focused on enabling modern, global games via cloud and cloud-native technologies.

Cloud computing is increasingly important for today’s global gaming ecosystem, empowering developers of any size to reach gamers in any part of the world. Azure’s 54 datacenter regions, and its robust global network, provides globally available, high performance services, as well as a platform that is secure, reliable, and scalable to meet current and emerging infrastructure needs. For example, earlier this month we announced the availability of Azure South Africa regions. Azure services enable every phase of the game development lifecycle from designing, building, testing, publishing, monetizing, measurement, engagement, and growth, providing:

Compute: Gaming services rely on a robust, reliable, and scalable compute platform. Azure customers can choose from a range of compute- and memory-optimized Linux and Windows VMs to run their workloads, services, and servers, including auto-scaling, microservices, and functions for modern, cloud-native games. Data: The cloud is changing the way applications are designed, including how data is processed and stored. Azure provides high availability,

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19

Mar

Power BI Embedded session at Power Platform Summit Europe: What’s new in Power BI Embedded

https://powerbi.microsoft.com/en-us/blog/power-bi-embedded-session-at-power-platform-summit-europe-whats-new-in-power-bi-embedded/Source: https://powerbi.microsoft.com/en-us/blog/power-bi-embedded-session-at-power-platform-summit-europe-whats-new-in-power-bi-embedded/           Join the Power BI Embedded team at the Power Platform Summit Europe for a 2-hour session. During the session, we will cover the newest features and capabilities in Power BI Embedded such as: READ MORE

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19

Mar

March 2019 changes to Azure Monitor Availability Testing

Azure Monitor Availability Testing allows you to monitor the availability and responsiveness of any HTTP or HTTPS endpoint that is accessible from the public internet. You don’t have to add anything to the web site you’re testing. It doesn’t even have to be your site, you could test a REST API service you depend on. This service sends web requests to your application at regular intervals from points around the world. It alerts you if your application doesn’t respond, or if it responds slowly.

At the end of this month we are deploying some major changes to this service, these changes will improve performance and reliability, as well as allow us to make more improvements to the service in the future. This post will highlight some of the changes, as well as describe some of the changes you should be aware of to ensure that your tests continue running without any interruption.

Reliability improvements

We are deploying a new version of the availability testing service. This new version should improve the reliability of the service, resulting in fewer false alarms. This change also increases the capacity for the creation of new availability tests, which is greatly needed as Application Insights

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19

Mar

Securely monitoring your Azure Database for PostgreSQL Query Store

A few months ago, I shared best practices for alerting on metrics with Azure Database for PostgreSQL. Though I was able to cover how to monitor certain key metrics on Azure Database for PostgreSQL, I did not cover how to monitor and alert on the performance of queries that your application is heavily relying on. As a PostgreSQL database, from time to time you will need to investigate if there are any queries running indefinitely on a PostgreSQL database. These long running queries may interfere with the overall database performance and likely get stuck on some background process. This blog post covers how you can set up alerting on query performance related metrics using Azure Functions and Azure Key Vault.

What is Query Store?

Query Store was a feature in Azure Database for PostgreSQL announced in early Fall 2018 that seamlessly enables tracking query performance over time. This simplifies performance troubleshooting by helping you quickly find the longest running and most resource-intensive queries. Learn how you can use Query Store on a wide variety of scenarios by visiting our documentation, “Usage scenarios for Query Store.” Query Store, when enabled, automatically captures a history of query runtime and wait statistics. It

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19

Mar

Reducing security alert fatigue using machine learning in Azure Sentinel

Last week we launched Azure Sentinel, a cloud native SIEM tool. Machine learning (ML) in Azure Sentinel is built-in right from the beginning. We have thoughtfully designed the system with ML innovations aimed to make security analysts, security data scientists, and engineers productive. The focus is to reduce alert fatigue and offer ML toolkits tailored to the security community. The three ML pillars in Azure Sentinel include Fusion, built-in ML, build your own ML.

Fusion

Alert fatigue is real. Security analysts face a huge burden of triage as they not only have to sift through a sea of alerts, but also correlate alerts from different products manually or using a traditional correlation engine.

Our Fusion technology, currently in public preview, uses state of the art scalable learning algorithms to correlate millions of lower fidelity anomalous activities into tens of high fidelity cases. Azure Sentinel integrates with Microsoft 365 solution and correlates millions of signals from different products such as Azure Identity Protection, Microsoft Cloud App Security, and soon Azure Advanced Threat Protection, Windows Advanced Threat Protection, O365 Advanced Threat Protection, Intune, and Azure Information Protection. You can learn how to turn Fusion on by visiting our documentation, “Enable Fusion.”

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18

Mar

Update on the New Workspace Experiences Preview Including GA Timeline

https://powerbi.microsoft.com/en-us/blog/update-on-the-new-workspace-experiences-preview-including-ga-timeline/Source: https://powerbi.microsoft.com/en-us/blog/update-on-the-new-workspace-experiences-preview-including-ga-timeline/           We launched the public preview of new workspace experiences in August 2018 to enable Power BI workspace admins to use security groups to manage access to workspaces, enable BI teams to create workspaces READ MORE

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18

Mar

The March release of Azure Data Studio is now available

https://cloudblogs.microsoft.com/sqlserver/2019/03/18/the-march-release-of-azure-data-studio-is-now-available/Source: https://cloudblogs.microsoft.com/sqlserver/2019/03/18/the-march-release-of-azure-data-studio-is-now-available/     Were excited to announce the March release of Azure Data Studio (formerly known as SQL Operations Studio) is now available. Download Azure Data Studioand review the Release notes to get started. Please note: If youre currently READ MORE

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18

Mar

Microsoft and NVIDIA extend video analytics to the intelligent edge

Artificial Intelligence (AI) algorithms are becoming more intelligent and sophisticated every day, allowing IoT devices like cameras to bridge the physical and digital worlds. The algorithms can trigger alerts and take actions automatically — from finding available parking spots and missing items in a retail store to detecting anomalies on solar panels or workers approaching hazardous zones.

Processing these state-of-the-art AI algorithms in a datacenter requires a stable high-bandwidth connection to deliver videos feeds to the cloud. However, these cameras are often located in remote areas with unreliable connectivity or it may not be sensible given bandwidth, security, and regulatory needs.

Microsoft and NVIDIA are partnering on a new approach for intelligent video analytics at the edge to transform raw, high-bandwidth videos into lightweight telemetry. This delivers real-time performance and reduces compute costs for users. The “cameras-as-sensors” and edge workloads are managed locally by Azure IoT Edge and the camera stream processing is powered by NVIDIA DeepStream. Once the videos are converted, the data can be ingested to the cloud using Azure IoT Hub.

The companies plan to offer customers enterprise-ready devices running DeepStream in the Azure IoT device catalog, and the NVIDIA DeepStream module will soon be made available

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18

Mar

Enterprise-scale Backup for SQL Server Databases
Enterprise-scale Backup for SQL Server Databases

More and more organizations are moving to the cloud to leverage the key benefits of agility and reduce overall cost. Its likely that at some point in time, your SQL Server will manifest as either of the three implementations in Azure: SQL Azure Database, SQL Managed Instance or SQL Server on Azure Virtual Machine (VM).

For the first two implementation types, backups are automatically managed by Azure internally. However, a significant number of customers choose to lift and shift SQL Server from on-premises physical or virtual environments to Azure VM to reduce friction and lower migration costs or risks. Should you choose to do so, youll still need to own and manage the backups for the SQL Server VM. Although you moved your SQL Server infrastructure from on-premises to Azure, the good news is that you dont have to move your backup infrastructure to cloud, thanks to Azure Backup!

Azure Backup fulfills the cloud promise of a cost-effective, elastic-scale, zero-infrastructure solution that eliminates the need to deploy or manage backup infrastructure, agents, or storage accounts. While it offers long term retention and central management capabilities to help IT admins govern and meet their compliance requirements, it lets SQL Admin continue

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18

Mar

Azure Machine Learning service now supports NVIDIA’s RAPIDS

Azure Machine Learning service is the first major cloud ML service to support NVIDIA’s RAPIDS, a suite of software libraries for accelerating traditional machine learning pipelines with NVIDIA GPUs.

Just as GPUs revolutionized deep learning through unprecedented training and inferencing performance, RAPIDS enables traditional machine learning practitioners to unlock game-changing performance with GPUs. With RAPIDS on Azure Machine Learning service, users can accelerate the entire machine learning pipeline, including data processing, training and inferencing, with GPUs from the NC_v3NC_v2, ND or ND_v2 families. Users can unlock performance gains of more than 20X (with 4 GPUs), slashing training times from hours to minutes and dramatically reducing time-to-insight.

The following figure compares training times on CPU and GPUs (Azure NC24s_v3) for a gradient boosted decision tree model using XGBoost. As shown below, performance gains increase with the number of GPUs. In the Jupyter notebook linked below, we’ll walk through how to reproduce these results step by step using RAPIDS on Azure Machine Learning service.

How to use RAPIDS on Azure Machine Learning service

Everything you need to use RAPIDS on Azure Machine Learning service can be found on GitHub.

The above repository consists of a master Jupyter Notebook that uses

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