Azure DevOps Services is a cloud service for collaborating on code development. It provides an integrated set of features that you access through your web browser or IDE client. The features are included, as follows:
Git repositories for source control of your code
Build and release services to support continuous integration and delivery of your apps
Agile tools to support planning and tracking your work, code defects, and issues using Kanban and Scrum methods
Many tools to test your apps, including manual/exploratory testing, load testing, and continuous testing
Highly customizable dashboards for sharing progress and trends
Built-in wiki for sharing information with your team
The Azure DevOps ecosystem also provides support for adding extensions and integrating with other popular services, such as: Campfire, Slack, Trello, UserVoice, and more, and developing your own custom extensions.
Azure Monitor for containers is a feature designed to monitor the performance of container workloads deployed to either Azure Container Instances or managed Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Monitoring your containers is critical, especially when you’re running a production cluster, at scale, with multiple applications.
Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. Container logs are also collected. After you enable monitoring from Kubernetes clusters, these metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux and stored in your Log Analytics workspace.
What I really like is that you now can see the Container Live logs from the Azure portal and see what is going on in the background of a Container 🙂
Activate Azure Kubernetes Container Live Logs
Here you see the Container Live logs
This feature provides a real-time view into your Azure Kubernetes Service (AKS) container logs (stdout/stderr) without having to run kubectl commands. When you select this option, new pane appears below the containers performance data table on the Containers view, and it shows live logging generated by the container engine to further assist in troubleshooting issues in real time.
Live logs supports three different methods to control access to the logs:
AKS without Kubernetes RBAC authorization enabled
AKS enabled with Kubernetes RBAC authorization
AKS enabled with Azure Active Directory (AD) SAML based single-sign on
You even can search in the Container Live Logs for Troubleshooting and history :
Search on ssh
Azure Monitor for containers uses a containerized version of the Log Analytics agent for Linux. After initial deployment, there are routine or optional tasks you may need to perform during its lifecycle.
Because of this agent you can work with Log Analytics in Azure Monitor :
When you have your production workload running on Azure Kubernetes Clusters, It’s important to monitor to keep you in Control of the solution in Microsoft Azure and watch for improvements like performance for the business. With Container Live logs you can see what is going on in the Containers when you have issues and that’s great for troubleshooting to get your problem solved fast. Get your workload into Azure Containers and make your Azure DevOps CI/CD Pipelines in the Cloud.
The velocity of change for the Microsoft SQL Server DBA has increased this decade. The span
between the releases of SQL Server 2016 and 2017 was only 16 months, the fastest new release
ever. Gone are the days when DBAs had between three to five years to soak in and adjust to new
features in the engine and surrounding technologies.
This book is written and edited by SQL Server experts with two goals in mind: to deliver a solid
foundational skillset for all of the topics covered in SQL Server configuration and administration,
and also to deliver awareness and functional, practical knowledge for the dramatic number
of new features introduced in SQL Server 2016 and 2017. We haven’t avoided new content—
even content that stretched the boundaries of writing deadlines with late-breaking new releases.
You will be presented with not only the “how” of new features, but also the “why” and the
“when” for their use.
Go Deep Dive with this Awesome SQL Server 2017 Ebook 😉
With container support, customers can use Azure’s intelligent Cognitive Services capabilities, wherever the data resides. This means customers can perform facial recognition, OCR, or text analytics operations without sending their content to the cloud. Their intelligent apps are portable and scale with greater consistency whether they run on the edge or in Azure.
Get started with these Azure Cognitive Services Containers
Building solutions with machine learning often requires a data scientist. Azure Cognitive Services enable organizations to take advantage of AI with developers, without requiring a data scientist. We do this by taking the machine learning models and the pipelines and the infrastructure needed to build a model and packaging it up into a Cognitive Service for vision, speech, search, text processing, language understanding, and more. This makes it possible for anyone who can write a program, to now use machine learning to improve an application. However, many enterprises still face challenges building large-scale AI systems. Today Microsoft announced container support for Cognitive Services, making it significantly easier for developers to build ML-driven solutions.
You must first complete and submit the Cognitive Services Vision Containers Request form to request access to the Face container. The form requests information about you, your company, and the user scenario for which you’ll use the container. Once submitted, the Azure Cognitive Services team reviews the form to ensure that you meet the criteria for access to the private container registry.
Important !
You must use an email address associated with either a Microsoft Account (MSA) or Azure Active Directory (Azure AD) account in the form. If your request is approved, you then receive an email with instructions describing how to obtain your credentials and access the private container registry.
The Face container uses a common configuration framework, so that you can easily configure and manage storage, logging and telemetry, and security settings for your containers.
Configuration settings
Configuration settings in the Face container are hierarchical, and all containers use a shared hierarchy, based on the following top-level structure:
End-to-end Pipelines for Automating Microsoft Azure Deployments
Overview :
Imagine a fully automated, end-to-end pipeline for your cloud deployments—one that encompasses and automates everything:
• Source code repos.
• The build and release iterations.
• Agile processes supported by continuous integration and continuous deployment (CI/CD)
• Security and governance.
• Business unit chargebacks.
• Support and maintenance.
Azure services and infrastructure-as-code (IaC) make control plane automation very achievable. Many enterprise IT groups dream of creating or unifying their disparate automation processes and supporting a common, enterprise-wide datacenter control plane in the cloud that is integrated with their existing or new DevOps workflows. Their development environments may use Jenkins, Azure DevOps Services (formerly Visual Studio Team Services), Visual Studio Team Foundation Server (TFS), Atlassian, or other services. The challenge is to automate beyond the CI/CD pipeline to the management and policy layers. From a planning and architecture standpoint, it can seem like an overwhelming program of interdependent systems and processes. This guide outlines a planning process that you can use for automated support of your cloud deployments and DevOps workflows beyond the CI/CD pipeline. The Azure platform provides services you can use, or you can choose to work with third-party or open source options. The process is based on real-world examples that we have deployed with enterprise customers on Azure.
This whitepaper was authored by Tim Ehlen. It was edited by Nanette Ray. It was reviewed by AzureCAT.
Damian speaks to Edward Thomson about how to get started with Azure Pipelines – right from GitHub. The deep integration and GitHub Marketplace app for Azure Pipelines makes it incredibly easy to build your projects no matter what language you’re using. You can even use the builds as part of your PR checks!
Edward shows us the incredible (free!) offers for open and closed source projects, and walks through creating and running a new Azure Pipelines build from scratch in only a few minutes.
Azure Service Fabric Mesh is a fully managed service that enables developers to deploy microservices applications without managing virtual machines, storage, or networking. Applications hosted on Service Fabric Mesh run and scale without you worrying about the infrastructure powering it. Service Fabric Mesh consists of clusters of thousands of machines. All cluster operations are hidden from the developer. Simply upload your code and specify resources you need, availability requirements, and resource limits. Service Fabric Mesh automatically allocates the infrastructure and handles infrastructure failures, making sure your applications are highly available. You only need to care about the health and responsiveness of your application-not the infrastructure.
With Service Fabric Mesh you can:
“Lift and shift” existing applications into containers to modernize and run your current applications at scale.
Build and deploy new microservices applications at scale in Azure. Integrate with other Azure services or existing applications running in containers. Each microservice is part of a secure, network isolated application with resource governance policies defined for CPU cores, memory, disk space, and more.
Integrate with and extend existing applications without making changes to those applications. Use your own virtual network to connect existing application to the new application.
Modernize your existing Cloud Services applications by migrating to Service Fabric Mesh.
Build high-availability into your application architecture by co-locating your compute, storage, networking, and data resources within a zone and replicating in other zones. Azure services that support Availability Zones fall into two categories:
Zonal services – you pin the resource to a specific zone (for example, virtual machines, managed disks, IP addresses)
Zone-redundant services – platform replicates automatically across zones (for example, zone-redundant storage, SQL Database).
To achieve comprehensive business continuity on Azure, build your application architecture using the combination of Availability Zones with Azure region pairs. You can synchronously replicate your applications and data using Availability Zones within an Azure region for high-availability and asynchronously replicate across Azure regions for disaster recovery protection.
The Service Fabric team will be hosting an Ask Me Anything (AMA) (more like “ask us anything”!) session for Service Fabric Mesh on Twitter on Tuesday, October 30thfrom 9am to 10:30am PST. Tweet to@servicefabricor @AzureSupport using #SFMeshAMA with your questions on Mesh and Service Fabric. More information here
More information about Azure Service Fabric Mesh :
Azure Pipelines is a cloud service that you can use to automatically build and test your code project and make it available to other users. It works with just about any language or project type.
Pipelines combines both Continuous Integration (CI) and Continuous Deployment (CD) to constantly and consistently test and build your code and ship it to any target.
Microsoft made it really easy to make your first Azure DevOps Pipeline in the Cloud.
Here you find a step-by-step guide to make your first Azure pipeline :
When you already made your Cloud application, you can choose option Bring your Own Code 😉
But in this step-by-step guide, I choose for a HTML5 Azure Web App template which is available in Azure.
Static Azure Website => Next.
When you create your Azure DevOps project you can see the Flow steps for Creation.
For the Service of the Web App, there are two options in this deployment template :
Web App for Containers
Web App as a Service.
Azure Web Apps enables you to build and host web applications in the programming language of your choice without managing infrastructure. It offers auto-scaling and high availability, supports both Windows and Linux, and enables automated deployments from GitHub, Azure DevOps, or any Git repo
Web App for Containers provides built-in Docker images on Linux with support for specific versions, such as PHP 7.0 and Node.js 4.5. Web App for Containers uses the Docker container technology to host both built-in images and custom images as a platform as a service. In this tutorial, you learn how to build a custom Docker image and deploy it to Web App for Containers. This pattern is useful when the built-in images don’t include your language of choice, or when your application requires a specific configuration that isn’t provided within the built-in images.
The last step needs information about :
Organization: for the site name.
Projectname
Subscription ID
Web App Name
Azure Location.
And then click on Done
Deployment overview.
Your Azure DevOps Pipeline is Running as easy like that 🙂
But most important your Azure Web App is running.
Running in your Container in Azure Cloud Services.
Azure DevOps Container Web App Pipeline is running.
From here you can build your Project and Share it with your Developer Team.
More information you can find on Azure DevOps Docs
Here you see some snapshots on the latest Releases of Azure DevOps release features when I made this blogpost :
When you want to keep up-to-date on Microsoft Azure DevOps, here are some links :
• Big Data Clusters o Deploy a Big Data cluster with SQL and Spark Linux containers on Kubernetes
o Access your big data from HDFS
o Run Advanced analytics and machine learning with Spark
o Use Spark streaming to data to SQL data pools
o Use Azure Data Studio to run Query books that provide a notebook experience
• Database engine
o UTF-8 support
o Resumable online index create allows index create to resume after interruption
o Clustered columnstore online index build and rebuild
o Always Encrypted with secure enclaves
o Intelligent query processing
o Java language programmability extension
o SQL Graph features
o Database scoped configuration setting for online and resumable DDL operations
o Always On Availability Groups – secondary replica connection redirection
o Data discovery and classification – natively built into SQL Server
o Expanded support for persistent memory devices
o Support for columnstore statistics in DBCC CLONEDATABASE
o New options added to sp_estimate_data_compression_savings
o SQL Server Machine Learning Services failover clusters
o Lightweight query profiling infrastructure enabled by default
o New Polybase connectors
o New sys.dm_db_page_info system function returns page information
• SQL Server on Linux
o Replication support
o Support for the Microsoft Distributed Transaction Coordinator (MSDTC)
o Always On Availability Group on Docker containers with Kubernetes
o OpenLDAP support for third-party AD providers
o Machine Learning on Linux
o New container registry
o New RHEL-based container images
o Memory pressure notification
• Master Data Services o Silverlight controls replaced
• Security
o Certificate management in SQL Server Configuration Manager
• Tools
o SQL Server Management Studio (SSMS) 18.0 (preview)
o Azure Data Studio
Introducing Microsoft SQL Server 2019 Big Data Clusters
SQL Server 2019 big data clusters make it easier for big data sets to be joined to the dimensional data typically stored in the enterprise relational database, enabling people and apps that use SQL Server to query big data more easily. The value of the big data greatly increases when it is not just in the hands of the data scientists and big data engineers but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use big data ecosystem tools while also utilizing easy, real-time access to the high-value data in SQL Server because it is all part of one integrated, complete system.
Starting in SQL Server 2017 with support for Linux and containers, Microsoft has been on a journey of platform and operating system choice. With SQL Server 2019 preview, we are making it easier to adopt SQL Server in containers by enabling new HA scenarios and adding supported Red Hat Enterprise Linux container images. Today we are happy to announce the availability of SQL Server 2019 preview Linux-based container images on Microsoft Container Registry, Red Hat-Certified Container Images, and the SQL Server operator for Kubernetes, which makes it easy to deploy an Availability Group.
Azure Data Studio is a new cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. Previously released under the preview name SQL Operations Studio, Azure Data Studio offers a modern editor experience with lightning fast IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built-in charting of query resultsets and customizable dashboards.
Don’t miss the Live Stream of Microsoft Ignite 2018
Get the latest insights and skills from technology leaders and practitioners shaping the future of cloud, data, business intelligence, teamwork, and productivity. Immerse yourself with the latest tools, tech, and experiences that matter, and hear the latest updates and ideas directly from the experts.
Watch live https://www.microsoft.com/en-us/ignite as Microsoft CEO Satya Nadella lays out his vision for the future of tech, then watch other Microsoft leaders explore the most important tools and technologies coming in the next year. After the keynotes, select Microsoft Ignite sessions will stream live—take a deep dive into the future of your profession.
Cloud and Datacenter Management Blog 