da Agency

New Microsoft Azure training and discounted certifications

This post is authored by Julia White, Corporate Vice President, Azure + Security Marketing.

As I recently discussed in my Top Cloud Myths of 2016 blog post, the use of cloud technology has become mainstream. Over 90% of the Fortune 500 companies now use at least one of Microsoft’s enterprise-grade services, and more than 60% are using three or more Microsoft cloud technologies. Azure compute usage has more than doubled year-over-year, and across a variety of industries, companies like Rolls Royce, Uber, and Coca-Cola are using Azure to transform their businesses.

With this rapid adoption of Azure, we have consistently heard from developers and IT operators about the need for Azure training and certification to help ensure they have the latest and greatest information. With the rapid innovation cycle of Azure, it’s critical to stay up to date on the latest technology and methodologies.

To make sure you have up-to-date technical skills and best practices using Azure, we are introducing new training resources and discounted access to certification. Announcing today, we have three training offers that combine free access to our library of flexible online courses, discounts on our industry-standard Microsoft Certified Professional exams, and a discount on Linux certification offered through the Linux Foundation. In case you missed the recent news, Microsoft joined the Linux Foundation, further demonstrating our commitment to open source for Azure, and as a company.

We are offering a broad range of learning resources – from a Massively Open Online Course, or MOOC, to full certification offerings. The MOOCs offer online videos, demos, labs, graded assessments, office hours, and more.  When you complete a MOOC you get digital certificates for completion, and you get access to reduced-cost Microsoft Certified Professional exams for formal certification.

Check out all the learning options for Azure, and read my colleague Gavriella Schuster’s blog post to dive into more details about our new resources and discounted certifications!
Quelle: Azure

da Agency

Azure Redis Cache diagnostic improvements

Early next year, we will transition Azure Redis Cache’s telemetry infrastructure to the new Azure Monitor service, thereby enhancing its monitoring and alerting experiences. Customers will enjoy the following benefits from this upgrade:

Getting service metrics out of the box – Today you need to create an Azure Storage account and configure Azure Redis to write metrics data to that account in order to monitor or alert for your cache. After the change, this is no longer required. All caches will automatically display metrics in the Azure service portal. These new Redis metrics data also will be accessible through Azure Monitor service’s REST API. If you have a need to retain metrics for longer than 30 days, you may still export it to your own storage account for archiving and offline analysis.
Managing alerts more effectively – With alert rules, Azure Monitor service allows you to add trigger conditions on metrics and events and it will notify you using standard channels (e.g., email, webhook) when any of the conditions is met. In addition, you will have more granular control over how alerts are configured. You will be able to set up alerts for each cache instance separately, instead of on a region and subscription basis currently.
Integrating with 3rd-party tools – Logs from Azure Redis will be streamed in near realtime to an Event Hub. This makes events from your cache instance available to 3rd party logging analytics systems, SIEMs, or custom telemetry pipelines almost instantaneously. Azure Monitor has a set of partners today who can ingest this data and the list of ecosystem partners is growing continuously.

We will update the Azure Redis service in multiple phases starting at the beginning of January 2017. We plan to complete the rollout for all Azure regions within the month.

The update will be seamless for most users, who will simply see the new metrics appearing in the portal. You can start setting up alerts should you so choose. If you are reading the Azure Redis metrics through the Azure Insights library, you just need to upgrade to the latest library version. If you access the metrics data directly from a storage account, however, you will need to change your tool to use either the Azure Insights library or Azure Monitor REST API, or to reconfigure a storage account for exporting and archiving the data. Azure Monitoring REST API Walkthrough provides a good overview of how to programmatically access Azure resource metrics. To facilitate migrating your tool, Azure Redis will continue to write metrics data to your current account until the end of February 2017. It will only publish data to Azure Monitor service after that time. We would encourage you to make the necessary modifications as early as possible.

We hope that you will like the new Azure Redis metrics. Please share with us your thoughts when you have a chance to try them out.
Quelle: Azure

da Agency

Kafka Connect for Azure IoT Hub

In Azure IoT, we believe in empowering developers to build IoT solutions with the technologies of their choice.  Today I’m excited to announce the release of Kafka Connect for Azure IoT Hub. This new Kafka Source Connector can be used to read telemetry data from devices connected to the Azure IoT Hub; this open source code can be found on GitHub.
 
Azure IoT Hub provides secure two way communication with devices, device identity and device management at extreme scale and performance. Kafka Connect for Azure IoT Hub enables developers to connect IoT Hub to open source systems using Kafka for a powerful, secure and performant IoT solution. Kafka Connect for IoT Hub can also be used with the new managed Kafka solution available in Azure HDInsight.
 
Stay tuned for more announcements and follow us on GitHub to see what is coming next for Kafka Connect for Azure IoT Hub.
Quelle: Azure

da Agency

Secondary Indexes on Column Store accelerate SQL Data Warehouse look up queries

We are pleased to announce that Azure SQL Data Warehouse now supports the creation of secondary B-Tree indexes (also referred to as non-clustered indexes or NCI) on column store tables (also referred to as clustered column store indexes or CCI). Most analytic queries aggregate large amounts of data and are served well by scanning the column store segments directly. However, there is often a need to look for a ‘needle in a haystack’ which translates to a query that does a lookup of a single row or a small range of rows. Such look up queries can get orders of magnitude (even 1000x) improvement in response time and potentially run in sub-second if there is a B-Tree index on the filter column.

SQL Data Warehouse is your go-to SQL-based view across data, offering a fast, fully managed, petabyte-scale cloud solution. It is highly elastic, enabling you to provision and scale up to 60 times larger in seconds. You can scale compute and storage independently, allowing you to range from burst to archival scenarios, and pay based off what you're using instead of being locked into a confined bundle. Plus, SQL Data Warehouse offers the unique option to pause compute, giving you even more freedom to better manage your cloud costs.

Prior to the availability of secondary B-Tree indexes on column store tables, users could meet response time requirements for their point look up queries by duplicating column store data in a clustered B-Tree index. However, the duplication of data adds implementation complexity, storage cost as well as latency. Some of these users have now tried the new secondary indexes over column store and are delighted that they can get the same interactive response time without the data duplication.

How to Create a Secondary Index on a Column Store Table

This follows the same syntax as the generic Create Index Transact-SQL statements. A simple test on 1TB TPC-H data demonstrated that the query time for selecting orders for a given orderkey from lineitem went down from 41 seconds to under a second after a secondary index on orderkey was added. 

Best Practices for Using Secondary Indexes

Here are some guidelines to bear in mind when using secondary indexes on column store tables.

Use them for high cardinality columns that are used as filters in queries returning a small number of rows.
Don’t be heavy handed with secondary indexes as there is an overhead to maintaining them during loads. Best to limit to 1 or 2 secondary indexes per table.

Secondary indexes can be created on partitioned column store tables as well. However, as they are local to each distribution and partition, they cannot be used to implement UNIQUE constraint.

Next Steps

In this blog post we talked about benefits of the new functionality offered by secondary B-Tree indexes on column store tables. This is now available in all SQL Data Warehouse Azure regions worldwide. We encourage you to try it out if you have a use case for point lookups.

Learn More

Check out the many resources for learning more about SQL Data Warehouse, including:

What is Azure SQL Data Warehouse?
SQL Data Warehouse best practices
Video library
MSDN forum
Stack Overflow forum
Quelle: Azure

da Agency

Live, online, step-by-step guidance from Cortana Intelligence Suite experts

Learn how to build intelligence into your applications by getting hands-on with the Cortana Intelligence Suite. On December 6, Microsoft Virtual Academy will host Microsoft Architects Todd Kitta and Jin Cho as they lead a workshop where you will create an end-to-end solution and finish the session with a working web app. Todd and Jin will pause their instruction to give you time to work through the project and to ask questions.

Learn how to architect solutions in Cortana Intelligence Suite

Bring your interest in data science and advanced analytics to learn what’s possible as we take a step-by-step look at the platform. We’ll explore key components, including Data Factory, HDInsight Spark, Azure Machine Learning, and Power BI, as we build the app. The instructors will talk about open source capabilities, walk through a machine learning model, show you how to set up a Data Factory pipeline, and much more.

Note: To best follow along with the instructors, be sure to set up a free trial subscription to Microsoft Azure before the event.

Course outline

Analytics State of the Union + Cortana Intelligence Suite Keynote
Building a Machine Learning Model and Operationalizing
Setting up Azure Data Factory
Developing a Data Factory Pipeline for Data Movement
Operationalizing Machine Learning Scoring with Azure Machine Learning and Data Factory
Summarizing Data Using HDInsight Spark
Visualizing Spark Data in Power BI
Deploying an Intelligent Web App
Wrap-up and Cleanup of Azure Resources

Register Now

Cortana Intelligence Suite End to End

Date: December 6, 2015
Time: 9am‒4pm PST
Where: Live, online virtual classroom
Cost: Free!

Register now
Quelle: Azure

da Agency

Help us shape Azure Storage Client Libraries and Tools

Azure Storage team is looking for feedback that will help with the planning of the Storage Client Libraries and Tools. If you currently use any of the client libraries and/or tools for Azure Storage, please take our short survey that shouldn't take more than 3 minutes to complete.

Once you complete the survey you will also have the opportunity to learn more about the upcoming preview programs as well as helping us shape the product roadmap.
Quelle: Azure

da Agency

Integrated Vulnerability Assessment with Azure Security Center

Vulnerability management is a critical part of an organization’s security and compliance strategy. Security flaws are constantly being discovered and fixed by vendors, making it hard for organizations to keep up with security patches. Meanwhile, missing security updates are easy targets for attackers and can compromise the security of the entire network.

Traditional network based scanners are available in the Azure Marketplace and successfully used by customers for vulnerability assessment. Nevertheless, many Azure customers are looking for continuous agent based solutions for the following reasons:

Cloud environments tend to be more dynamic. Virtual Machines (VMs) are being spun up and down frequently, making it more difficult for a scheduled scan to cover all assets.
Network based scanners require a user account on target virtual machines in order to provide full insight. In many cases, however, customers lack the ability to centrally manage such accounts in the cloud.
When resources are spread across different virtual networks, multiple network based scanners are required to get access to all virtual machines.

As announced at the end of September, Azure Security Center now offers integrated vulnerability assessment with Qualys cloud agents (preview) as part of the Virtual Machine recommendations. If a Virtual Machine does not have an integrated vulnerability assessment solution already deployed, Security Center recommends that it be installed. The solution can be deployed to multiple VMs at one time, and the ability to automatically deploy on new VMs as they are created, will be added soon. Once deployed, the Qualys agent will start reporting vulnerability data to the Qualys management platform, which in turn provides vulnerability and health monitoring data back to Security Center. Users can quickly identify vulnerable VMs from the Security Center dashboard. Additional reports and information are available in the Qualys management console, which is linked directly from Security Center.

To get started, simply follow the “Add a vulnerability assessment solution” recommendation in Azure Security Center as shown in this article. You can also watch this short video on channel 9. If you don’t have yet a Qualys subscription, you can enable a free trial, in just a few clicks. Once the trial is over, the Qualys agent would stop reporting vulnerabilities and can be easily removed from the Security Center dashboard without any impact to the VM.

Interested in learning more on Azure Security Center and its partner ecosystem integration?

Top 4 reasons for using Azure Security Center for partner security solutions
Managing security recommendations in Azure Security Center
Monitoring partner solutions with Azure Security Center
Integrating Security Center alerts with Azure log integration (Preview)
Security Resource Provider REST API Reference

Quelle: Azure

da Agency

Announcing StorSimple Data Transformation preview

There has been a popular and persistent ask from customers to do more with the data that moves to the cloud via StorSimple. For example, use an Azure service such as Azure Media Services, Azure HDInsight, or Azure Machine Learning to process this data. To date, there was no scalable way to do this. That changes today!

We are excited to announce the private preview of StorSimple Data Transformation- a managed service – that allows you to convert your data of interest from StorSimple format to Azure blobs. This in turn opens the data to the rich ecosystem of various Azure services.

Using StorSimple Data Transformation, files that you put into your StorSimple on-premises volumes will be converted to Azure blobs. Each file will reside as a blob in your storage account. Given that StorSimple is often used for storing media content, we have also provided the option of converting your files directly into media assets in your Media Services account. Now, with a few clicks, you can set up jobs that can move your data in a transparent and seamless way. You can initiate these jobs from the Azure Portal, or you can even integrate the APIs in your code (for example, web jobs or worker roles)

Here is a block diagram of how StorSimple Data Transformation enables this solution:

 

Our customers are planning on using this service in interesting ways:

A call center customer uses StorSimple to store the audio recording of all calls. Using StorSimple Data Transformation, they are using Azure Media services to convert call to text, and analyze these calls for silence duration, sentiment and operator performance.
A customer who uses StorSimple for storing video surveillance feeds plans to use this service for face recognition and redaction using Azure Media services.
A customer who stores log files on StorSimple wants to use compute in the cloud to analyze these logs

If you are a StorSimple customer who wants to put their data to work in Azure, sign up for the preview. If you are a Media services or HDInsight customer looking to fulfil your on-premises storage needs while trying to use these solutions in Azure, enroll today!

 
Quelle: Azure

da Agency

Introducing: Time picker in Application Insights Analytics

A time picker has been added to Analytics in Azure Application Insights so that you can easily set a time range for your queries. The default range is Last 24 hours, but you can easily change that: Just select a different preset range, or simply add your own “where timestamp…” clause to the query (so if your queries already include a time filter, they’ll work exactly as before).
 

Why do we need a time picker?

With mountains of data at hand, Analytics users often want to focus on a specific time frame. The new time picker allows you to zoom in and explore interesting events without manually editing your query. With the coming upgrade of the data retention period to a full 90 days, applying a time filter to narrow down the number of records returned will help identify events’ root causes, as well as investigate their consequences. Time filters are also a good way to make your queries more efficient and get faster results. This is why we’ve decided to introduce the new time picker, bringing time filtering to the front and making it as easy as possible.

What does the time picker do?

The time picker, located next to the familiar Go button, allows you to select a time range to apply to your query, instead of manually typing it again and again. The selected time range will apply to all the queries in the current tab. The default range is Last 24 hours, but you can easily use a different range –  simply select any of the other presets – last 10 minutes, last hour, 2 or 7 days. After executing the query, you can see the applied time range on the results bar.

 
If none of the presets matches your needs, select custom time range to learn how to add your own time filter to queries.

But wait, what about queries that already include a time filter?

No worries. Specifying an explicit time filter in queries remains a very powerful feature. For these cases we added the Set in query mode, selected automatically for queries that include a time filter.

 

What’s next?

This is of course just the beginning. As more users get acquainted with the time picker, it will evolve to support many more use cases, such as selecting the previous month (the whole calendar month preceding the current one) and specific start and end date and time.

As always, feel free to send us your questions or feedback by using one of the following channels:
•  Suggest ideas and vote on our UserVoice page
•  Join the conversation in the Application Insights Community forum
•  Try Application Insights Analytics
Quelle: Azure

da Agency

Announcing general availability of preview features and new APIs in Azure Search

Today we are announcing the general availability (GA) of several features as well as a new REST API version and .NET SDK that support the new GA features. All GA features and APIs are covered by the standard Azure service level agreement (SLA) and can be used in production workloads.

New GA APIs

Blob Indexer allows you to parse and index text from common file formats such as Office documents, PDF, and HTML. NOTE: CSV and JSON support is still in preview.
Table Indexer enables you to ingest data from an Azure Table store.
Custom Analyzers enable you to take control over the process of lexical analysis that’s performed over the content of your documents and query terms. For example, custom analyzers enable support for Phonetic search. For more information, read more on custom analyzers. 
Analyze API allows you to test built-in and custom analyzers to see how the analyzer breaks text into tokens.
ETag Support allows you to safely update index definitions, indexers, and data sources concurrently.

New REST API Version (2016-09-01) and .NET SDK

The following REST API and .NET SDKs are GA:

Service REST API version (2016-09-01), which includes all of the GA features noted in the previous section.
A .NET SDK equivalent of the Service REST API (2016-09-01).
A .NET SDK for management operations (Microsoft.Azure.Management.Search), which is the first .NET SDK for Search service and api-key management.

The existing preview API (2015-02-28-Preview) is still active and continues to support existing preview features such as moreLikeThis, and CSV and JSON support in Blob indexer. We look forward to your continued feedback on these and other new features as we work to add more GA features.

Learn More

Learn more about the new REST API version, including steps to upgrade from an older version of the REST API to the GA version. Get the download to GA .NET SDK.
Quelle: Azure