Category Archives : Updates

24

Jul

Top feature requests added with Azure Blockchain Workbench 1.2.0

We’re excited to see a ton of engagement and positive feedback on Azure Blockchain Workbench since our initial public preview release in May. Last month, we made our first major update to the public preview release based on your feedback and feature requests. Today, we’re releasing our next update to Workbench, which we’re calling version 1.2.0. You can either deploy a new instance of Workbench through the Azure Portal or upgrade your existing deployment to 1.2.0 using our upgrade script.

This update includes the following improvements:

Enable/disable apps

Many of you have started to iterate and create multiple blockchain apps using Workbench. One of the most requested features we’ve heard is the ability to disable unused blockchain apps within the Workbench Web app. With 1.2.0, you will be able to enable or disable applications. In addition, the UI will allow you to filter the list of applications to only show enabled or disabled applications.

BYOB – Bring Your Own Blockchain

As part of the Workbench deployment, we deploy a set of Ethereum Proof-of Authority (PoA) nodes within a single member’s subscription. This topology works great for situations where it’s OK to have one member manage all the blockchain

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23

Jul

Accelerated and Flexible Restore Points with SQL Data Warehouse

We are thrilled to announce that SQL Data Warehouse (SQL DW) has released accelerated and flexible restore points for fast data recovery. SQL DW is a fully managed and secure analytics platform for the enterprise, optimized for running complex queries fast across petabytes of data.

The ability to quickly restore a data warehouse offers customers data protection from accidental corruption, deletion, and disaster recovery. We have seen scenarios where compliance requirements and having multiple test and development environments of a data warehouse enforce stricter capabilities in this area as well. To continue delivering first-class data protection and recovery, we have released the following critical improvements which are seamlessly integrated within the Azure Portal.

Finer granularity for cross region and server restores

You can now restore across regions and servers using any restore point instead of selecting geo redundant backups which are taken every 24 hours. Cross region and server restore is supported for both user-defined or automatic restore points enabling finer granularity for additional data protection. With more restore points available, you can be assured that your data warehouse will be logically consistent when restoring across regions.

Fast restore with Enhanced Restore Points

You can now restore your

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23

Jul

Accelerated and Flexible Restore Points with SQL Data Warehouse

We are thrilled to announce that SQL Data Warehouse (SQL DW) has released accelerated and flexible restore points for fast data recovery. SQL DW is a fully managed and secure analytics platform for the enterprise, optimized for running complex queries fast across petabytes of data.

The ability to quickly restore a data warehouse offers customers data protection from accidental corruption, deletion, and disaster recovery. We have seen scenarios where compliance requirements and having multiple test and development environments of a data warehouse enforce stricter capabilities in this area as well. To continue delivering first-class data protection and recovery, we have released the following critical improvements which are seamlessly integrated within the Azure Portal.

Finer granularity for cross region and server restores

You can now restore across regions and servers using any restore point instead of selecting geo redundant backups which are taken every 24 hours. Cross region and server restore is supported for both user-defined or automatic restore points enabling finer granularity for additional data protection. With more restore points available, you can be assured that your data warehouse will be logically consistent when restoring across regions.

Fast restore with Enhanced Restore Points

You can now restore your

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18

Jul

Speech services July 2018 update
Speech services July 2018 update

A lot has happened since we announced that Speech services is now in preview, we have released the Cognitive Services Speech SDK June 2018 update.

Today, we are excited to announce that we have just released the 0.5.0 version of the Speech SDK. With this update, we have added support for UWP (on Windows version 1709), .NET Standard 2.0 (on Windows), and Java on Android 6.0 (Marshmallow, API level 23) or higher. We have made some feature changes and done some bug fixes. Most notably, we now support long-running audio and automatic reconnection. This will make the Speech service more resilient overall, in the event of timeout, network failures or service errors. We’ve also improved the error messages to make it easier to handle the errors. Please visit the Release Notes page for details. We will continue to add support for more platforms and programming languages, as we work toward making the Speech SDK generally available this fall.

Besides the Speech SDK, Custom Voice has also released a new feature to support more training data formats. All ‘.wav’ files (RIFF) with a sampling rates equal to or higher than 16khz are now accepted. Furthermore, we have extended support to more

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18

Jul

Speech services July 2018 update
Speech services July 2018 update

A lot has happened since we announced that Speech services is now in preview, we have released the Cognitive Services Speech SDK June 2018 update.

Today, we are excited to announce that we have just released the 0.5.0 version of the Speech SDK. With this update, we have added support for UWP (on Windows version 1709), .NET Standard 2.0 (on Windows), and Java on Android 6.0 (Marshmallow, API level 23) or higher. We have made some feature changes and done some bug fixes. Most notably, we now support long-running audio and automatic reconnection. This will make the Speech service more resilient overall, in the event of timeout, network failures or service errors. We’ve also improved the error messages to make it easier to handle the errors. Please visit the Release Notes page for details. We will continue to add support for more platforms and programming languages, as we work toward making the Speech SDK generally available this fall.

Besides the Speech SDK, Custom Voice has also released a new feature to support more training data formats. All ‘.wav’ files (RIFF) with a sampling rates equal to or higher than 16khz are now accepted. Furthermore, we have extended support to

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18

Jul

Foretell and prevent downtime with predictive maintenance
Foretell and prevent downtime with predictive maintenance

The story of predictive maintenance (PdM) starts back in the 1990s. Technologies began to arrive that sense the world in new ways: ultrasound, infrared, thermal, vibration, to name a few. However, until recently the technology has not been available to make predictive maintenance a reality. But now, with advances in cloud storage, machine learning, edge computing, and the Internet of Things — predictive maintenance looms as the next step for the manufacturing industry.

What is predictive maintenance?

There are three strategies for machine maintenance:

Reactive — the “don’t fix what isn’t broken” approach. This means you extract the maximum possible lifetime from a machine. However, costs balloon with unexpected downtime and collateral damage from failures. Preventative — service on a fixed schedule based on the regularity of previous failures. You maximize up-time by fixing machines before they fail. The downside is that components may have life left, and there is still a chance that they will fail before the scheduled maintenance. Predictive — where we use data about previous breakdowns to model when failures are about to occur, and intervene just as sensors detect the same conditions. Until recently this has not been a realistic option, as modeling did not

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18

Jul

Foretell and prevent downtime with predictive maintenance
Foretell and prevent downtime with predictive maintenance

The story of predictive maintenance (PdM) starts back in the 1990s. Technologies began to arrive that sense the world in new ways: ultrasound, infrared, thermal, vibration, to name a few. However, until recently the technology has not been available to make predictive maintenance a reality. But now, with advances in cloud storage, machine learning, edge computing, and the Internet of Things — predictive maintenance looms as the next step for the manufacturing industry.

What is predictive maintenance?

There are three strategies for machine maintenance:

Reactive — the “don’t fix what isn’t broken” approach. This means you extract the maximum possible lifetime from a machine. However, costs balloon with unexpected downtime and collateral damage from failures. Preventative — service on a fixed schedule based on the regularity of previous failures. You maximize up-time by fixing machines before they fail. The downside is that components may have life left, and there is still a chance that they will fail before the scheduled maintenance. Predictive — where we use data about previous breakdowns to model when failures are about to occur, and intervene just as sensors detect the same conditions. Until recently this has not been a realistic option, as modeling did not

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17

Jul

Azure Monitor: Route AAD Activity Logs using diagnostic settings

Today in partnership with the Azure Active Directory (AAD) team we are excited to announce the public preview of AAD Activity Logs using Azure Monitor diagnostic settings. Azure Monitor diagnostic settings enable you to stream log data from an Azure service to three destinations: an Azure storage account, an Event Hubs namespace, and/or a Log Analytics workspace. This allows you to easily route logs from any Azure service to a data archive, SIEM tool, or custom log processing tool. With today’s announcement, you will now be able to route your AAD audit and sign in logs to these same destinations, centralizing all of your Azure service logs in one pipeline.

Until now, all log data handled by Azure Monitor came from an Azure resource deployed within an Azure subscription. We often describe this type of data as “resource-level log data,” and it is configured using a resource diagnostic setting. AAD log data is the first type of log data from a tenant-level service made available through Azure Monitor. Tenant-level services aren’t deployed as resources within an Azure subscription, rather they function across an entire AAD tenant. To handle this new type of “tenant-level log data,” Azure Monitor has introduced a

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17

Jul

Azure cost forecast API and other updates
Azure cost forecast API and other updates

As your cloud spend starts to increase, being able to accurately forecast usage becomes a critical part of your plan. As the first step in enabling a forward looking view to your spend on a subscription we’re launching a forecast API. The forecast API today supports a daily or monthly grain forecast at a 95 percent confidence interval. The API also returns the last two months of actual usage that should help in looking at any trending scenarios. For future iterations of the API, we plan to support multiple other scopes up and down the hierarchy, like resource groups and enrollments, options for confidence intervals and longer range forecasts. Check out the documentation page for more details on calling the forecast API.

Location and service data normalization

Effective cost management for enterprise customers requires accurate and granular data that can be dimensionalized to support ad hoc queries and accurate rollups of costs in the enterprise hierarchy. As the first step to make the usage data adding more dimensions, we are normalizing the location (InstanceLocation) and service (ConsumedService) fields as there are a few data quality issues with multiple variants of data in these columns. The dimensions currently supported are tags

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17

Jul

Azure cost forecast API and other updates
Azure cost forecast API and other updates

As your cloud spend starts to increase, being able to accurately forecast usage becomes a critical part of your plan. As the first step in enabling a forward looking view to your spend on a subscription we’re launching a forecast API. The forecast API today supports a daily or monthly grain forecast at a 95 percent confidence interval. The API also returns the last two months of actual usage that should help in looking at any trending scenarios. For future iterations of the API, we plan to support multiple other scopes up and down the hierarchy, like resource groups and enrollments, options for confidence intervals and longer range forecasts. Check out the documentation page for more details on calling the forecast API.

Location and service data normalization

Effective cost management for enterprise customers requires accurate and granular data that can be dimensionalized to support ad hoc queries and accurate rollups of costs in the enterprise hierarchy. As the first step to make the usage data adding more dimensions, we are normalizing the location (InstanceLocation) and service (ConsumedService) fields as there are a few data quality issues with multiple variants of data in these columns. The dimensions currently supported are tags

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