Microsoft named a Leader in The Forrester Wave: Enterprise iPaaS, 2021

We are thrilled to share that Microsoft has been named a Leader in the Forrester Wave for Enterprise Integration-Platform-as-a-Service (iPaaS), 2021. Azure Integration Services consisting of Logic Apps, API Management, Event Grid, and Service Bus helps customers connect applications, data, and services on-premises and in the cloud, helping enterprises create new revenue opportunities with an API-driven partner and developer ecosystem and boost productivity with secure and automated workflows. Forrester credits Microsoft for providing a high-end developer experience to every kind of organization, from large enterprises to smaller ones, supporting varying integration requirements with flexible pricing options.

Our vision is to empower all kinds of organizations and users, from citizens to professional developers, to use Azure Integration Services to enable a composable enterprise. We believe that integration is an essential part of modern applications and should be available and accessible to all kinds of users; citizens to professional developers. We reach out to developers at the platform of their choice with Visual Studio and Visual Studio Code extensions, support for Azure DevOps, GitHub Actions, Application Insights, and Azure Monitor, offering an intuitive user experience and increasing developer productivity. With Power Automate and its Windows-integrated robotic process automation (RPA) capabilities, we enable citizen developers to leverage integration without writing code. In addition to that, more than 450 out-of-the-box connectors such as Microsoft 365, Dynamics 365, Salesforce, SAP, and Twitter, help customers connect data, events, and resources across applications, and systems, fast and easy, without writing any code.

With the ability to run Azure’s application services on Kubernetes or any cloud through Azure Arc, Azure Integration Services enables customers to create, deploy, and manage integrations wherever they make the most sense to run, in on-premises data centers, or Azure and multicloud, or in the edge, or any combination of the above. Azure Arc enabled integration services offer customers better control to develop and deploy their integration solutions while providing the benefits of our iPaaS and the flexibility of hosting it anywhere.

Azure Integration Services has a vast and global customer base with more than 43,000 customers using our integration services to connect applications, systems, and services. Learn how customers use Azure Logic Apps and Azure API Management to connect business applications, data, and processes to digitally transform their businesses. We also have a vast partner network across all geographies to help enterprises modernize existing on-premises integration solutions, such as Microsoft BizTalk Server, or build modern integration platforms spanning cloud and on-premises.

Source: microsoft.com

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Updates to Azure Files: NFS v4.1, higher performance limits, and reserved instance pricing

Azure Files offers fully managed, simple, secure, and serverless enterprise-grade cloud file shares. On the Azure Files team, our mission is to expand Azure Files to more platforms and workloads. We recently took a huge step in workload expansion by announcing the general availability of our NFS v4.1 shares. This greatly expands the workloads you can run on Azure file shares by providing POSIX compatible file systems for Linux virtual machines and container-based workloads. This blog provides information on the general availability of NFS v4.1 shares, increased performance for all premium file shares, and reserved instance pricing for premium file shares to lower your costs.

NFS v4.1 shares are now generally available

In November we announced the general availability of Azure Files support for NFS v4.1. Now you can deploy these fully POSIX compliant, distributed NFS file shares in your production environments for a wide variety of Linux and container-based workloads.

We saw strong interest in the preview with participation from companies of all sizes, ranging from emerging startups to Fortune 100s, running a plethora of workloads. Some examples of workloads include SAP application layer, enterprise messaging, user home directories, custom line-of-business applications, database backups, database replication, AI and machine learning user directories, DevOps pipelines, and many more industry-specific workloads such as the solution from EDF Energy below.

EDF Energy uses Azure file shares as part of its asset management solution:

“At EDF Energy, Nuclear Safety is our overriding priority. As part of our Asset Management solution used to control work, maintenance, and defect resolution to support site license conditions, we needed a performant shared file system between multiple Linux Application Servers—such as an NFS share. We used NFS v4.1 on Azure Files in the preview and have now taken full dependency on it. The NFS system is working very well for us, persisting our files and keeping our IaaS requirements to a minimum.”

—Cathy Handley, AMS Upgrade Programme Manager, EDF Energy—Helping Britain Achieve Net Zero

Customers running critical systems such as SAP have told us that synchronous zonal redundancy is a game-changer for them to achieve high availability for their application layer. With Azure premium file shares you can choose between Locally redundant storage (LRS) or Zonal Redundant Storage (ZRS) redundancy. With ZRS, data is synchronously replicated to three different availability zones within an Azure region. This means your applications’ access to the data will not be disrupted, even in the unlikely event of an entire zone failure. SAP storage administrators gave us great feedback including: “easy to use”, “good performance”, and “better cost optimization.”

Unlike the lower versions of NFS, locking is inbuilt into NFS v4.1. Hence, software like IBM MQ relies on locking support from NFS v4.1 to keep data consistent within a distributed system. While in preview, we enhanced our locking support and implemented locking upgrades and downgrades.

The Azure Files CSI driver (now generally available) makes it easy to access your Azure File shares from Azure Kubernetes Service (AKS). The fast attach-detach times of Azure Files have been appealing to applications that require rapid scale up and scale down.

NFS v4.1 is available in all regions where the premium tier of Azure Files exists. For the full list, see the Azure service availability page. You can now get started using NFS by following these simple step-by-step instructions.

Improved performance

Today, we are announcing more IOPS and throughput for all premium file shares (SMB and NFS).

All shares now provide a minimum of 3000 IOPS, up from the previous 400 IOPS baseline. We are also increasing the minimum burst IOPS such that even the smallest shares can burst up to 10,000 IOPS. Just as before, you will continue to linearly scale IOPS up to 100,000 as the share size increases.

You can now use 100 percent of the provisioned throughput towards either reads or writes. This means you can get up to 10GB/s of read or write traffic. Previously, premium shares used allocated throughput with a 40:60 write:read ratio resulting in max write of 4GB/s and max read of 6GB/s.

These performance enhancements will apply to all existing and new shares across all regions including public and sovereign cloud at no extra cost.

Lower cost with Reserved Instances

All premium file shares (SMB and NFS) now support capacity reservations which provide up to 36 percent discount, by pre-committing to storage utilization.

Reserved instances are also supported for the hot and cool Azure file shares (SMB only).

Get involved

You can put all the updates mentioned in this blog together for NFS v4.1 shares with higher throughput and more IOPS at a lower price. Of course, the performance improvements and reserved instances apply to both NFS and SMB shares. We continue to increase investments in Azure Files and look forward to getting the next wave of updates released.

Source: microsoft.com

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New satellite connectivity and geospatial capabilities with Azure Space

Figure 1: Azure Space ecosystem showing multi-orbits and geospatial capabilities through Machine Learning.

Last year, Microsoft announced Azure Space, bringing together the possibilities of Space with the power of the cloud to help people and organizations achieve more on and off the planet.

Today we are announcing new partnerships and capabilities for Azure Space including:

◉ Azure Orbital reaches preview—now anyone can communicate and control satellites, from our owned and partnered ground-stations around the world—with no backhaul costs into Azure.

◉ Innovations built on Azure are “seeing” through the clouds with SpaceEye, and enhancing imagery with Project Turing.

◉ Our new partnership with Airbus is bringing the world’s leading high-resolution satellite imagery and elevation data into Azure, to further transform our understanding of the world.

◉ A virtualization partnership with iDirect, one of the largest satellite modem providers, is creating modern and flexible solutions for customers.

◉ New geospatial and data analytics partnerships with Esri, Blackshark.ai, and Orbital Insight on Azure are enabling new insights for our customers.

The power of extracting and leveraging data collected from space can transform entire industries and create new paradigms. Azure Space, through partnerships, space data, our collaboration tools, and Microsoft services and capabilities, unlock powerful possibilities for customers.

Manage Satellite Data at cloud scale with Azure Orbital

The need for low latency, high capacity ground infrastructure is becoming more critical as the public and private sectors launch large numbers of satellites and new constellations into space. Microsoft is bringing its world-leading ground infrastructure alongside partners to support this industry.

Microsoft is announcing the next milestone for Azure Orbital with the preview of a service that enables satellite operators to eliminate the technical, scheduling, and cost challenges of building a dedicated ground station network. The scale and scope of global ground stations through Azure Orbital and our partner network makes coverage more accessible for satellite providers at a lower cost, enabling high reliability and resiliency around the world ensuring support for any mission profile.

The Azure Orbital preview includes support for Microsoft and KSATlite ground stations. Starting this month, customers can use Azure Orbital APIs or the Azure Portal to communicate with their satellites using Microsoft and KSAT antennas. This network will continue to expand early next year with support for ground station partners ViaSat and USEI.

This significant step in the Microsoft and KSAT partnership allows customers to benefit from expansive global coverage free of the integration and data-delivery costs associated with typical multi-network solutions facilitating access to the power of the Microsoft Cloud to process, store, and extract insights from spaceborne data.

“Over the past year, we have worked hard here at KSAT to continue our collaboration with Azure Orbital integrating our worldwide satellite ground network with Microsoft to provide seamless, global support for transporting, processing, and storing space-based data. Being able to deliver satellite data and run resource-intensive computing such as machine learning techniques and other applications using cloud-based solutions will not only change the way we deliver our services but also how our customers will be able to utilize this information in the future. Through this partnership, our goal is to continue to provide our customers with the most technically advanced solutions for their missions”—Rolf Skatteboe, CEO KSAT

New innovations enhance satellite images through Azure

SpaceEye—“seeing” through the Clouds

67 percent of the world is covered in clouds—a major challenge for Earth observation from space is that much of the Earth is covered by opaque clouds. Built on Azure by Microsoft Research, SpaceEye is an AI-based system that generates daily cloud-free optical and multispectral imagery for the planet.

SpaceEye uses the Synthetic Aperture Radar (SAR) instrument from the Sentinel-1 mission as a baseline data source—as radar data is not affected by cloud cover. Space Eye then combines this radar data with historical optical imagery to generate an AI image prediction of what it looks under the clouds. This can unlock significant use cases in agriculture, land-use monitoring and disaster response among others.

Project Turing—increase human perception of overhead imagery

Microsoft Azure is also being used to prepare and enhance geospatial data for better human interoperability. Using Microsoft’s Turing research, we have brought the fictional “enhance image” feature from Hollywood to reality. Turing’s semantic super-resolution allows us to use satellite imagery and increase the resolution to be comparable to aerial, greatly aiding human perception of overhead imagery. Today, this technology is running on Azure to enhance Bing Maps worldwide, covering over 50 percent of all user requests.

View high-resolution satellite imagery of anywhere on the planet in partnership with Airbus

https://www.microsoft.com/en-us/videoplayer/embed/RWQcuG

Microsoft is expanding on its mission to make Azure Space the platform and ecosystem of choice for the space community through a new partnership with Airbus and the general availability of their premium satellite imagery and elevation data in Microsoft Azure Maps. Through this partnership, Airbus will feed Azure Maps with its SPOT 1.5m, Pléiades 50cm, and Pléiades Neo 30cm resolution satellite imagery and WorldDEM4Ortho elevation data.

“We are thrilled to be a part of the Microsoft Azure community,” said François Lombard, director of the intelligence business at Airbus Defense and space. “Azure Maps users, eager for accurate and top-quality imagery, will be able to rely on Airbus’ premium data services to develop new applications and turn their innovative ideas into reality.”

Virtualization brings a new era to Space connectivity

Traditionally, connectivity from a satellite to a ground station has depended on expensive and inflexible radio hardware. Azure Orbital takes advantage of virtualization, which moves functionality from proprietary hardware into software that can be deployed on general-purpose hardware to deliver a more scalable and cost-efficient solution for customers.

Another significant challenge for the space community is the lack of standardization. We are working with the industry through the Digital IF Interoperability (DIFI) consortium for which Microsoft is a founding board member to create standards to support the space ecosystem, such as the new IEEE-ISTO Std 4900-2021: Digital IF Interoperability Standard for streaming data between digitizers and virtualized modems. Together, virtualization and open standards enable customers to harness the power of the cloud to usher in a new era in the space industry.

Leveraging our Azure software radio tools for virtualized space and satellite communications (which are available on GitHub), Microsoft is excited to add ST Engineering iDirect, one of the most widely deployed and trusted satellite platforms, to our list of Azure Orbital ground segment partners. Through our development partnership, ST Engineering iDirect and Microsoft will collaborate to bring ST Engineering iDirect’s satcom solutions to Azure as virtualized modems that can be readily deployed and used by Azure Orbital customers. iDirect and Microsoft are transforming satellite ground stations into a fully virtualized digital platform enabling Satellite operators to achieve the economies of scale and software efficiencies of cloud scale operation.

Geospatial partnerships enable seamless analysis of space data on Azure

Today, we are also announcing new geospatial and data analytics partnerships with Esri, Blackshark.ai, and Orbital Insight on Azure enabling new insights for our customers.

Esri

Microsoft is teaming up with Esri—the global market leader in geographic information system software (GIS), location intelligence and mapping—to provide geospatial analytic workflows on spaceborne data natively to the Azure cloud.

“Microsoft continues to provide cloud innovation in support of partner opportunity enablement. Azure Orbital and Esri will streamline spaceborne data workflows to empower Azure customers with access to near-real time satellite data combined with Esri’s ArcGIS geospatial analytics software.”—Richard Cooke, VP, Esri, Imagery and Remote Sensing

Esri will bring ArcGIS Image, a dedicated single tenant SaaS offering, to Azure customers enabling end to end geospatial data management and analytics for near-real-time spaceborne data. ArcGIS Image for ArcGIS Online is a multi-tenant SaaS offering designed to help customers manage, interpret, analyze, and share imagery and derived insights from customers’ existing imagery data. Customers will be able to easily host imagery in the Azure cloud, eliminating the need to manage their own infrastructure, and perform advanced analytics on that data at scale.

Blackshark.ai

Microsoft and Blackshark.ai are partnering to bring Blackshark.ai's advanced geospatial intelligence and 3D synthetic environments at scale to empower commercial and government customers with data, insights, and a digital twin of our planet on Microsoft Azure.

Orbital Insight

Additionally, Microsoft is partnering with Orbital Insight, a world-leading geospatial analytics software company that helps organizations understand what's happening on and to the Earth, to make Orbital Insight GO platform available on Microsoft Azure. With GO on Azure, enterprises and governments can unlock insights on patterns of life, make supply chains visible, find anomalies, monitor facilities, and detect military movements, empowering decision-makers to act confidently.

Source: microsoft.com

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New resources and tools to enable product leaders to implement AI responsibly

As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. A principled approach to responsible AI will be essential for every organization as this technology matures. As technical and product leaders look to adopt responsible AI practices and tools, there are several challenges including identifying the approach that is best suited to their organizations, products and market.

Today, at our Azure event, Put Responsible AI into Practice, we are pleased to share new resources and tools to support customers on this journey, including guidelines for product leaders co-developed by Microsoft and Boston Consulting Group (BCG). While these guidelines are separate from Microsoft’s own Responsible AI principles and processes, they are intended to provide guidance for responsible AI development through the product lifecycle. We are also introducing a new Responsible AI dashboard for data scientists and developers and offering a view into how customers like Novartis are putting responsible AI into action.

Introducing Ten Guidelines for Product Leaders to Implement AI Responsibly

Though the vast majority of people believe in the importance of responsible AI, many companies aren’t sure how to cross what is commonly referred to as the “Responsible AI Gap” between principles and tangible actions. In fact, many companies actually overestimate their responsible AI maturity, in part because they lack clarity on how to make their principles operational.

To help address this need, we partnered with BCG to develop “Ten Guidelines for Product Leaders to Implement AI Responsibly”—a new resource to help provide clear, actionable guidance for technical leaders to guide product teams as they assess, design, and validate responsible AI systems within their organizations.

“Ethical AI principles are necessary but not sufficient. Companies need to go further to create tangible changes in how AI products are designed and built,” says Steve Mills, Chief AI Ethics Officer, BCG GAMMA. “The asset we partnered with Microsoft to create will empower product leaders to guide their teams towards responsible development, proactively identifying and mitigating risks and threats.”

The ten guidelines are grouped into three phases:

1. Assess and prepare: Evaluate the product’s benefits, the technology, the potential risks, and the team.

2. Design, build, and document: Review the impacts, unique considerations, and the documentation practice.

3. Validate and support: Select the testing procedures and the support to ensure products work as intended.

With this new resource, we look forward to seeing more companies across industries embrace responsible AI within their own organizations.

Launching a new Responsible AI dashboard for data scientists and developers

Operationalizing ethical principles such as fairness and transparency within AI systems is one of the biggest hurdles to scaling AI, which is why our engineering teams have infused responsible AI capabilities into Azure AI services, like Azure Machine Learning. These capabilities are designed to help companies build their AI systems with fairness, privacy, security, and other responsible AI priorities.

Today, we’re excited to introduce the Responsible AI (RAI) dashboard to help data scientists and developers more easily understand, protect, and control AI data and models. This dashboard includes a collection of responsible AI capabilities such as interpretability, error analysis, counterfactual, and casual inferencing. Now generally available in open source and running on Azure Machine Learning, the RAI dashboard brings together the most used responsible AI tools into a single workflow and visual canvas that makes it easy to identify, diagnose, and mitigate errors.

Figure 1: The Responsible AI dashboard

Putting responsible AI into action

Organizations across industries are already working with Azure’s AI capabilities, including many of the responsible AI tools that are part of the Responsible AI dashboard.

One example is Novartis, a leading, focused medicines company, which earlier this year announced its eight principles for ethical use of AI. Novartis is already embedding AI into the workflow of their associates and have many instances across the value-chain in which AI is used in day-to-day operations. With AI playing such a critical role in enabling their digital strategy, Microsoft’s responsible AI is an integral piece to ensure AI models are built and used responsibly.

“This AI dashboard enables our teams to assess AI systems’ accuracy and reliability, aligned with our framework for ethical use of AI, to ensure they’re appropriate for the intended context and purpose, as well as how to best integrate them with our human intelligence.”—Nimit Jain, Head of Data Science, Novartis

Another example is Philips, a leading health technology company, which uses Azure and the Fairlearn toolkit to improve their machine learning models’ overall fairness and mitigate biases, leading to better management of patient wellbeing and care. And Scandinavian Airlines, an Azure Machine Learning customer, relies on interpretability in their fraud detection unit to understand model predictions and improve how they identify patterns of suspicious behavior.

Missed the digital event? Download the guidelines and tool

While we are all still navigating this journey, we believe that these new resources will help us take a thoughtful step toward implementing responsible AI. If you missed the event, make sure to watch the recording and download the resources available. Together with Microsoft, let’s put responsible AI into practice.

Source: microsoft.com

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Azure HBv3 virtual machines for HPC, now up to 80 percent faster with AMD Milan-X CPUs

Preview live today, available globally soon

We are announcing that a preview is now live for Azure HBv3 virtual machines enhanced by 3rd Gen AMD EPYC™ processors with AMD 3D V-cache, codenamed “Milan-X”. These processors significantly improve the performance, scaling efficiency, and cost-effectiveness of a variety of memory performance-bound workloads such as CFD, explicit finite element analysis, computational geoscience, weather simulation, and silicon design RTL workflows.

Compared to the current HBv3-series with 3rd Gen AMD EPYC processors, already the highest performance VM for HPC workloads on the public cloud, customers will experience up to:

◉ 80 percent higher performance for CFD

◉ 60 percent higher performance for EDA RTL

◉ 50 percent higher performance for explicit FEA

◉ 19 percent higher performance for weather simulation

In addition, all HBv3-series VMs globally will soon be upgraded with Milan-X processors. This upgrade will be provided at no additional cost beyond existing pricing for HBv3-series VMs, and with no changes required of customer workloads. No other changes are being made to the HBv3-series VM sizes customers already know and rely on for their critical research and business workloads.

Turbocharging memory performance-bound HPC workloads

Many HPC workloads are driven foremost by memory performance. For some, such as computational fluid dynamics, performance is directly driven by memory bandwidth. For others, such as RTL simulation that is the workhorse application for silicon design firms, this means memory latency. The forthcoming upgrade to HBv3-series VMs will address both needs by growing L3 cache memory to an unprecedented 1.5 gigabytes per virtual machine. This is three times larger than what is found in the standard 3rd Gen EPYC processors currently in HBv3-series VMs, and more than 25 times larger than the total L3 cache found in most HPC servers in customer datacenters today.

For memory bandwidth-bound workloads to run at an appropriate scale, the net effect of the larger L3 cache is an up to 1.8x increase in effective memory bandwidth. This means an HBv3 VM that today offers 350 GB/s (as measured by STREAM-TRIAD) will soon perform more like a VM with greater than 600 GB/s of memory bandwidth.

For memory latency-bound workloads, the net effect of the larger L3 cache in Milan-X processors is an up to 50 percent increase in cache hit rate and an overall cache latency range (latency from one core to nearest and farthest neighbor cores) that is, conversely, 50 percent lower than standard 3rd Gen EPYC processors.

Below is a sample of at-scale performance results with Azure HBv3 VMs enhanced with Milan-X processors as compared to the existing HBv3 series with standard 3rd Gen EPYC processors. Tests were conducted across a range of MPI scale scenarios, from 2 to 64 VMs (240 to 7,680 CPU cores).

Figure 1: Benchmarks were taken with WRF v. 4.15, OpenFOAM v. 1912, and Ansys Fluent 2021 R1

The more you scale, the less you pay … no, really

Azure’s use of NVIDIA Quantum InfiniBand Networking from our partners at NVIDIA already enables customers to scale MPI workloads to supercomputer heights. Milan-X processors in HBv3-series VMs raise the bar yet again by delivering above linear scaling efficiency across a range of MPI workloads and models. This means customers actually save on Azure compute costs as a result of realizing dramatically faster time-to-solution. Below is an example of this capability in action with Ansys Fluent and the canonical F1_racerar_140m simulation. HBv3 VMs with Milan-X processors deliver nearly 200 percent scaling efficiency by yielding 127 times higher performance for only 64 VMs worth of compute.

Figure 2: Measured scaling efficiency from 1 to 64 VMs using ANSYS Fluent 2021 R1

Continuous improvement for Azure HPC customers

Microsoft and AMD share a vision for a new era of high-performance computing in the cloud: one defined by continuous improvements to the critical research and business workloads that matter most to our customers. Azure has partnered with AMD to make this vision a reality by raising the bar on the performance, scalability, and value we deliver with every release of Azure HB-series virtual machines.

Figure 3: Azure HPC performance 2019 through 2021

We look forward to bringing Milan-X processors to all Azure HPC customers soon in the HBv3-series of virtual machines and look forward to seeing everyone in the preview.

Source: microsoft.com

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5 reasons Azure Databricks is best for Hadoop workloads

Due to the complexity, high cost of operations, and unscalable infrastructure, on-premises Hadoop platforms have often not delivered on their initial promises to impact business value. As a result, many enterprises are now seeking to modernize their Hadoop platforms to cloud data platforms. Catalysts include:

◉ High cost of ownership: On-premises hardware is costly and potential is never realized.

◉ End-of-life and expiring licenses: Do you renew or migrate?

◉ End of support: Customers are forced to upgrade or buy new hardware.

Customers are now turning to Azure Databricks. Azure Databricks is a unified data analytics platform for accelerating innovation across data science, data engineering, and business analytics. Azure Databricks brings a cost-effective and scalable solution to managing Hadoop workloads in the cloud—one that is easy to manage, highly reliable for diverse data types, and enables predictive and real-time insights to drive innovation.

Azure Databricks is the best place to migrate your Hadoop workloads

Migrating your Hadoop workloads to Azure Databricks brings cost management, scalability, reliability for all data types, and the ability to apply advanced analytics for deeper insights. Microsoft Azure provides a fully managed cloud platform that reliably handles all types of data with Delta Lake within Azure Databricks. The Databricks runtime engine is a highly optimized, highly performant-tuned Spark version deployed on Azure as a managed service. Databricks offers elastic auto-scalability powered by Azure. Customers can scale up or down based on workload to deliver the most cost-effective scale and performance in the cloud. With Azure Databricks, AI frameworks, including TensorFlow, Keras, and PyTorch, are available in one place. Access them using Python or Scala notebooks, all in an accessible, shared notebook. These capabilities are not possible in an on-premises environment.

Azure Databricks isn’t just the best destination for Hadoop migrations, it is also the best destination for all Databricks workloads. Azure Databricks is the only first-party service providing customers with benefits not offered in any other cloud. First-party integration and our unique strategic alliance save customers time and effort and significantly accelerate time to value. As Forrester notes, “the competitive advantage is no longer about ‘first to market’, it’s (now about) ‘first to value.’”1

Azure Databricks empowers customers to be first to value for these five reasons:

1. Unique engineering partnership

The Azure and Databricks engineering teams deepen the integration of Databricks within Azure to enable rapid customer success. Both engineering teams have spent hundreds of thousands of hours optimizing Databricks for Azure. This collaboration drives a highly performant level of cloud-scale operations that would not be possible otherwise. Since Azure Databricks is a first-party service, the Azure Databricks engineering team can optimize the offering across storage, networking, and compute to Azure customers’ benefit. Customers also get access to new innovations, like the exclusive preview of the new Photon engine, before they are available elsewhere.

2. Mission-critical support and ease for commerce

Azure Databricks customers receive enterprise-level support from a single place instead of the bifurcated model they would experience elsewhere. This is important for customers running mission-critical workloads with Databricks. With Azure Databricks, customers also benefit from a streamlined licensing process. Azure customers can start using Azure Databricks immediately, with no additional licenses to sign or procure. This is in addition to receiving a single bill, greatly simplifying the customer experience, and providing the level of support and predictability customers expect from their cloud providers.

3. Azure ecosystem

Azure Databricks is fully integrated with the vast portfolio of products and services in the Microsoft Azure ecosystem, accelerating customers’ time to value. The joint engineering effort ensures seamless integration of Azure Databricks with services such as Azure Event Hubs, Azure Data Lake Storage, Azure Synapse Analytics, and Azure IoT Hub. As showcased in our Ingestion, ETL, and stream processing pipelines architecture, Azure Databricks ingests data in a simple, open, and collaborative way. By building a data streaming solution with Azure Databricks, Providence Health Care unlocked real-time analytics capabilities to ease hospital overcrowding. Additionally, the highly optimized Azure Synapse connector is the most popular service connector across all of Databricks. The combination of these services operating seamlessly together reinforces Azure as the preferred destination for running mission-critical analytics workloads with Databricks.

4. Native security, identity, and compliance

Azure Databricks provides enterprise-grade Azure security, including Azure Active Directory (Azure AD) integration, role-based access controls, and service-level agreements (SLAs) that protect your data and your business. Native integration with Azure AD enables a customer to run complete Azure-based solutions using Azure Databricks from the moment an Azure Databricks workspace is deployed. This is a zero-touch experience compared to stitching together a separate, stand-alone authentication solution after deploying a workspace. The identity and access management propagates throughout all other Azure services in the solution and flows between other Azure services in your solution, which means less work and less time to get a solution up and running.

Azure Databricks is the only Databricks environment with the FedRAMP High Authorization along with 12 other security certifications. This authorization provides customers assurance that Azure Databricks is designed to meet U.S. Government security and compliance requirements to support their sensitive analytics and data science workloads. You can use Azure Databricks with confidence in regulated industries such as healthcare, life sciences, and financial services.

5. Rapid onboarding

Azure Databricks makes it easy to get started. With a few clicks, data teams can set up an Azure Databricks workspace. They can collaborate across teams and access other needed services immediately through the Azure portal. Azure Databricks provides an easy path to get started and is available to customers around the world in 35 Azure regions.

Source: microsoft.com

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Introducing Azure Load Testing: Optimize app performance at scale

Today, we are announcing the preview of Azure Load Testing. Azure Load Testing is a fully managed Azure service that enables developers and testers to generate high-scale load with custom Apache JMeter scripts and gain actionable insights to catch and fix performance bottlenecks at scale.

Application performance and resiliency are more important than ever before. As more businesses and services move toward digitization, the impact of outages and downtimes in lost revenue and customer dissatisfaction grows. Azure Load Testing is a service that can help testers and developers who are taking on a larger role in validating app quality, performance, and scalability.

Creating and managing the infrastructure required for load testing at scale can be time-consuming and complex. With Azure Load Testing, you can generate high-scale load without the need to manage complex infrastructure, and we have included Azure-specific integrations and insights so you can optimize your Azure application at scale.

You can get started with Azure Load Testing with this Quickstart that walks you through how to create and run your first load test.

Figure 1: Azure Load Testing architecture overview.

Learn how your application behaves under stress

Performance problems often remain undetected until an application is under load. We want to help development teams gain insight into how their application behaves under stress. You can start a high-scale load test in the Azure portal and see live updates of the client and server-side metrics while the test is running.

Azure-specific insights can help you understand how different load scenarios impact all the parts of your application, and you can compare test results across different load tests to understand behavior changes over time.

Azure Load Testing integrates with Azure Monitor, including Application insights and Container insights, to capture details from the Azure services. Depending on the type of service, different metrics are available. For example, the number of database reads, the type of HTTP responses, or container resource consumption. Both client-side and server-side metrics are available in the Azure Load Testing dashboard.

Figure 2: Get your performance insights across client and server with the Load Testing dashboard.

Build load testing into your CI/CD pipeline

Azure Load Testing is designed to help you integrate load testing into your continuous integration and continuous delivery (CI/CD) workflows so you can find performance issues before they happen in production. Using the integrations with GitHub Actions and Azure Pipelines you can establish a performance baseline and set clear pass or fail criteria to catch performance regressions on every build.

Azure Load Testing will automatically abort an automated load test in response to specific error conditions. This behavior helps protect against failing tests further incurring costs, for example, because of an incorrectly configured endpoint URL, and is especially important for enabling automated testing.

Source: microsoft.com

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