What is Azure?

Azure is launched by Microsoft in February 2010.

It is a cloud computing platform including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) and also these can be used for services such as analytics, virtual computing, storage, networking, etc.

The Azure cloud platform has more than 200 products and cloud services designed to help the users and to solve today’s challenges and create the future.

What is the use of Azure?

There are 10 uses of Azures –

• Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) capabilities:
• Security Offerings:
• Scalability and Ductility:
• Unmatched Hybrid Capabilities:
• Integrated Environment with Other Microsoft Tools:
• Analytics and Intelligence capabilities:
• Easy learning curve:
• Cost Efficient Platform:
• Interoperability:
• Identity & Access Management (IAM):

Compare Google Cloud Certification Vs Azure Certification Vs AWS

Amazon Web Services (AWS) –

AWS is the most widely used cloud platform, having a very vast experience with an expansive range of services tailored for different use cases. AWS is currently at the top, holding the world’s largest public cloud market share. It has been introduced in 2006 and was the first to offer infrastructure services through the Internet.

AWS incorporates many different cloud computing products and services comprising compute, storage, analytics, databases, networking, mobile, developer tools, management tools, and IoT.

Key Features of AWS

• Reliable, scalable, and low-cost infrastructure platform
• Over 200 fully-featured services
• A flexible and secure cloud computing environment
• 81 availability zones in 25 geographic regions across the world

Microsoft Azure–

Azure is the second-largest Cloud Service Provider after AWS. Microsoft Azure is Microsoft’s public cloud computing platform that was introduced in October 2008 and became available in public in February 2010. It is the biggest competitor of AWS. The platform provides a broad range of products and cloud services to allow businesses to find today’s challenges and create the future. Azure’s product portfolio comprises compute, AI & Machine Learning, analytics, databases, DevOps, IoT, networking, storage, web, and security.

Key Features of Azure

• More than 200 products and cloud services
• 90+ compliance offerings
• 95 percent of the Fortune 500 companies rely on Azure
• Datacentres in 60+ global regions

Google Cloud Platform–

GCP is new to cloud computing offered by Google. Many leading companies over industries keep faith on Google Cloud to solve their business problems. Some of its popular cloud services comprise computing, data analytics, data storage, networking, IoT, and machine learning.

Between all three cloud platforms, GCP provides the low-cost pricing model and has flexible cost-control, enable you to try the different services and features.

Key Features of GCP

• Open source, multi-cloud, and hybrid cloud
• Over 100 products
• A global network spanning across 200+ countries & territories, 85 zones
• Trust and security

Certification

The certification confirms your basic knowledge of cloud services and how those services are offered with Azure. Candidates should be competent to demonstrate a fundamental knowledge of cloud concepts, alongside with Azure services, workloads, security, privacy, pricing, and support.

A Microsoft azure certification allows you to chase a wide range of career options. You can become a cloud architect, a developer or solution architect. Additionally, the certification allows you to work in different industries at different locations.

List of Azure Certification

• Microsoft Certified: Azure – Fundamentals Exam AZ-900
• Microsoft Certified: Azure AI – Fundamentals Exam AI-900
• Microsoft Certified: Azure Data – Fundamentals Exam DP-900
• Microsoft Certified: Azure Administrator – Associate Exam AZ-104
• Microsoft Certified: Azure Developer – Associate Exam AZ-204
• Microsoft Certified: Azure Security Engineer – Associate Exam AZ-500
• Microsoft Certified: Azure AI Engineer – Associate Exam AI-100
• Microsoft Certified: Azure Data Scientist – Associate Exam DP-100
• Microsoft Certified: Azure Data Engineer – Associate Exams DP-200 and DP-201
• Microsoft Certified: Azure Database Administrator – Associate Exam DP-300
• Microsoft Certified Solutions Architect – Expert Exams AZ-303 and AZ-304
• Microsoft Certified: Azure DevOps Engineer – Expert Exam AZ-400

Azure Certification Path

Fundamentals-Level Certifications

• Microsoft Certified: Azure Fundamentals AZ-900
• Microsoft Certified: Azure Data Fundamentals DP-900
• Microsoft Certified: Azure AI Fundamentals AI-900
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900

Associate-Level Certifications

• Microsoft Certified: Azure Administrator Associate AZ-104
• Microsoft Certified: Azure Developer Associate AZ-204
• Microsoft Certified: Azure Database Administrator Associate DP-300
• Microsoft Certified: Azure Security Engineer Associate AZ-500
• Microsoft Certified: Azure Data Engineer Associate DP-203
• Microsoft Certified: Azure AI Engineer Associate AI-102
• Microsoft Certified: Azure Stack Hub Operator Associate AZ-600

Expert-Level Certifications

• Microsoft Certified: Azure Solutions Architect Expert AZ-303
• Microsoft Certified: DevOps Engineer Expert AZ-400

Specialty Certifications

• Microsoft Certified: Azure IoT Developer Specialty AZ-220
• Microsoft Certified: Azure for SAP Workloads Specialty AZ-120

Azure Certification Cost

• Azure Fundamentals AZ-900 – ( $99 USD* )
• Azure Data Fundamentals DP-900 – ( $99 USD* )
• Azure AI Fundamentals AI-900 – ( $99 USD* )
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900 – ( $99 USD* )
• Azure Administrator Associate AZ-104 – ( $165 USD* )
• Azure Developer Associate AZ-204 – ( $165 USD* )
• Azure Database Administrator Associate DP-300 – ( $165 USD* )
• Azure Security Engineer Associate AZ-500 – ( USD 165.00* )
• Azure Data Engineer Associate DP-203 – ( USD 165* )
• Azure AI Engineer Associate AI-102 – ( $165 USD* )
• Azure Stack Hub Operator Associate AZ-600 – ( $165 USD* )
• Azure Solutions Architect Expert AZ-303 ( USD 165.00* )
• DevOps Engineer Expert AZ-400 ( USD 165.00* )
• Azure IoT Developer Specialty AZ-220 ( USD 165.00* )
• Azure for SAP Workloads Specialty AZ-120 ( USD 165.00* )

Best salary for Azure Certified Professional

The average salary of Azure Solutions Architects in the U.S. $152,142 and Rs 1,575,000 in India.
The Azure Fundamentals certification average salary is $126,653 and Rs. 9,00,000 in India.
Azure Administrator Associate average salary is $125,993 and 3.0 Lakhs to Rs 16.9 Lakhs in India.

Best Azure Certification Tutorials

https://www.tutorialspoint.com/microsoft_azure/cloud_computing_overview.htm

Best Azure Certification Video Tutorials

Best Azure certification excercise dumps

https://www.examtopics.com/exams/microsoft/az-900/view/

https://www.prepaway.com/az-104-exam.html

Best Azure certification Ebooks

https://azure.microsoft.com/en-in/blog/free-ebook-the-developer-s-guide-to-microsoft-azure-now-available/

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Microsoft claims its new PyTorch Enterprise on Microsoft Azure is the first offering from a cloud platform to provide enterprise support for PyTorch, the popular open source deep learning framework.

Along with that enterprise support, it comes with prioritized troubleshooting and also integrates with other Azure solutions, such as Azure Machine Learning.

PyTorch is an open source machine learning/deep learning framework based on the Torch library, used for applications such as computer vision and natural language processing, driven by Facebook’s AI Research lab, according to Wikipedia.

Regular Visual Studio Magazine readers will know that it’s also a favorite tool of our own data science guru, Dr. James McCaffrey of Microsoft Research, who authors regular hands-on PyTorch-based tutorials for our Data Science Lab.

The new offering comes after Microsoft teamed up with Facebook to become a founding member of the PyTorch Enterprise Support Program, which helps service providers develop and offer tailored enterprise-grade support to their customers.

The enterprise support initiative was reportedly sparked by feedback from customers, who found it easy to get started with PyTorch but not so easy to implement complicated real-world enterprise production initiatives. Thus Microsoft will provide commercial support for the public PyTorch codebase. “Each release will be supported for as long as it is current,” Microsoft said in a recent blog post. “In addition, one PyTorch release will be selected for LTS every year. Such releases will be supported for two years, enabling a stable production experience without frequent major upgrade investment.”

Supported configurations include:

• PyTorch: version 1.8.1 and up.
• Libraries: torch, torchaudio, torchvision, torchtext, onnxruntime, and torch-tb-profiler.
• Python: version 3.6 and up.
• NVIDIA CUDA: versions 10.2 and 11.1.
• Operating systems: Windows 10, Debian 9, Debian 10, Ubuntu 16.04.7 LTS, and Ubuntu 18.04.5 LTS (x86_64 architecture only).

It does not, however, support C++ or Java interfaces, or PyTorch libraries and features marked “experimental and subject to change” including TorchServe and Pipeline Parallelism.

To be eligible at no additional cost, enterprise customers must join Microsoft’s Premier or Unified support programs. For such organizations, as part of the aforementioned prioritized troubleshooting, “The dedicated PyTorch team in Azure will prioritize, develop, and deliver hotfixes to customers as needed. These hotfixes will get tested and will be included in future PyTorch releases. In addition, Microsoft will extensively test PyTorch releases for performance regressions with continuous integration and realistic, demanding workloads from internal Microsoft applications.”

Microsoft is heavily invested in the PyTorch ecosystem, noting that the company’s data scientists like Dr. McCaffrey use PyTorch as the primary framework to develop models to enhance Office 365, Bing, Xbox and other offerings. That investment includes projects such as PyTorch Profiler, ONNX Runtime on PyTorch, PyTorch on Windows, DeepSpeed and more.

For now, PyTorch Enterprise is available on Azure Machine Learning and Data Science Virtual Machines (DSVM), coming soon to Azure Synapse Analytics.

The post Microsoft Offers Deep Learning Support with PyTorch Enterprise on Microsoft Azure appeared first on Artificial Intelligence.

New Delhi: SoftBank-owned UK chipmaker Arm has partnered with Microsoft to create Azure-based tools to enable developers transfer data from Arm-based Internet of Things (IoT) devices to Azure Cloud. The partnership will enable developers to easily target a broad range of Arm-based devices like intelligent computer-vision-enabled cameras, connected vehicles, AI gateways and intelligent appliances.

With over 30 billion active IoT devices in the world with 400 per cent growth over the past three years, the industry is moving incredibly fast. “Arm’s leading silicon architecture combined with their broad ecosystem and commitment to optimising developer experiences will enable this partnership to accelerate AI innovation for IoT devices, delivering better solutions and a better future for end-users,” said Moe Tanabian, Vice President, General Manager-Azure Edge Devices.

In one of the biggest tech deals, graphics giant NVIDIA in September announced it is acquiring Arm for $40 billion with an aim to create a premier computing company for the age of artificial intelligence (AI). Microsoft is working closely with the silicon ecosystem to launch an Azure-Ready Silicon programme.

“We are committed to helping foster innovation and diversity within the broader silicon and device ecosystems, and making it easy, fast, and cost-effective for our customers,” Microsoft said in a blog post on Thursday. “Our collaboration with Arm to facilitate an end-to-end AI toolchain to simplify development is an important part of this strategy”.

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In June 2019, the announcement surprised . Oracle and Microsoft forged an unexpected alliance in the cloud, creating interconnections between their respective data centers. First in the United States, then in the United Kingdom in September .

Everything else ? If from a strategic point of view the alliance made sense, from a practical point of view it seemed less clear. Because even in the case of a classic architecture (on-site development with Microsoft technologies on an Oracle on-site base that we migrate to the cloud), the issues of multicloud , latency, version upgrades and Updates on different cycles on two different platforms remained pending.

Of course, Microsoft and Oracle have a lot of common customers. Certainly both are present in large accounts. And of course they have AWS as their common “enemy”. But the real use cases that led to this merger therefore deserved to be specified.

This is what Applications & Data (LeMagIT) did during the visit to Europe (more precisely in London) of Clive D’Souza , the very courteous “Head of Product Management & Business Strategy, VMware & Azure Interconnect at Oracle Cloud Infrastructure ”from Oracle.

Three use cases for a wedding

For Clive D’Souza three main use cases emerged from common customers.

Use case 1 – This is the most obvious, mentioned above. “One of the most popular use cases is an application developed in .NET on top of an Oracle database that a company wants to migrate to the cloud. It can now modernize it and port it while keeping the code on Azure and the data on Oracle Cloud, ”explains Clive D’Souza (see below).

Use case 2 – Companies may want to make new developments using Azure services (Cognitive or others) on data in Oracle Cloud DB.

As opposed to the previous one, “this case is essentially ‘cloud native’, with customers wanting to leverage what Azure really does well – like Power BI , analytics, and the dashboard. They want to develop on top of that. But if there is critical data – corporate data – they can separate it and put it in an Oracle database ”. A configuration that confirms the analysis of Holger Mueller from Constellation Research.

Why, in this configuration, not instead choose the analytical or cognitive services of Oracle Cloud  – which Oracle also extensively highlighted in London in February? “The choice to put such or such part of the application stack in such or such cloud depends a lot on the application and the proximity of the customer [with Oracle and Microsoft]”, answers Clive D’Souza.

Use case 3 – Companies would also have requested the possibility of having an Oracle development stack (Full Stack Oracle Enterprise Apps Development) – like JDE, Hyperion, EBS, etc. – on OCI, but with middleware triggering triggers and alerts, from and to a SQL Server database on Azure.

3 questions to Clive D’Souza (Oracle) and a “Cloud Center of Excellence”

These three use cases do not, however, make us forget that multicloud – even with direct interconnection between infrastructures – is not trivial. Clive D’Souza does not hide it in any way.

On the contrary, he speaks of a long-term transformation project, throughout the company, around a “Cloud Center of Excellence”.

A&D / LeMagIT: For use case # 1, a big problem is that the code of an application is particularly sensitive to the version of the underlying database. An ERP is clearly very sensitive to this. So when you change the base version, there is a good chance that you will also have to rework the code.

If I have my code on Azure and the database updates itself regularly – because that’s what your standalone database does on OCI – how do I handle this?

Clive D’Souza  : Between the Autonomous Database and the application, there is a management and orchestration layer (a “control plan” or a “data plan”). And in this layer, there are “triggers”. Whenever a change occurs – whether on the code side or the base side, with a patch for example – an alert is sent from both sides [Editor’s note: to developers and DBAs ].

But what you’re talking about is a broader discussion of how you come to a form of excellence – a “Cloud Center of Excellence” – and how you sustain that.

A&D / LeMagIT: But in conclusion, will I have to regularly touch the code?

Clive D’Souza  : Yes… but again: it’s not just a matter of database or application. We are entering a more modern cloud operating model (“modern cloud operating model”). This is the “Cloud Center of Excellence”.

When you are there, with this new cloud operating model, you step into the lands of DevOps and CI / CD . It is obligatory.

And when you reach these levels of deployments and this very extensive management of operations, it’s not to do a single application. It flows through the entire organization, in the form of microservices in a DevOps model (you can think of this as very fine piping).

A&D / LeMagIT: Are there many companies in Europe that have reached this level of maturity or are we still only at the very beginning?

Clive D’Souza  : Those on the move are some of the biggest names in the business. Unfortunately, I am not yet allowed to give them to you. But Suzanne Holliday [Editor’s note: present during interview] oversees my team’s efforts for Europe and you should soon have public examples.

But yes, there are different maturities of PoC and deployments.

That said, obviously, the interconnection in Amsterdam [Editor’s note: which Oracle announced at the start of the year] was a “must have” [to get the hybrid cloud between Azure and OCI off the ground] in Europe. And the more regions and interconnections there are, the more customers we will see doing this.

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Teradata is extending support for data science with an update to its flagship data management for analytics platform, Teradata Vantage. Teradata made the announcement via a press release. The Teradata Vantage data science update is headlined by expanded native support for R and Python, automatic generation of SQL from R and Python code, and added support for JupyterHub for Python, R and SQL. New features are free to all Teradata Vantage customers.

Teradata offers a broad spectrum of data management solutions that include database management, cloud data warehousing, and data warehouse appliances. The company’s product portfolio is available on its own managed cloud and on Amazon Web Services and Microsoft Azure. Teradata provides organizations the ability to run diverse queries, in-database analytics, and complex workload management.

The Teradata Vantage data science platform enhancement includes an expanded set of analytic functions through R and Python that help facilitate native algorithms. In addition, there is also a new suite of open source R and Python analytic algorithms which can be used with Vantage. The new Analytics Library includes a set of data preparation and modeling functions as well.

A new automatic SQL statement generation feature helps facilitate communication between multiple user groups. SQL generated from Vantage Analyst allows business users to discover fundamental insights from Vantage Analyst’s graphical interface and also generate the underlying SQL script automatically without having to write code. While R, Python and SQL scripts can be executed by an analyst’s choice of Integrated Development Environment, Teradata’s support for JupyterHub helps to facilitate better code collaboration and best practice sharing.

According to the vendor, these new capabilities “further support Teradata’s advocated Analytics 1-2-3 strategy which holds that organizations can only successfully scale their Machine Learning (ML) and Artificial Intelligence (AI) initiatives by engaging in rapid experimentation and agile model building by using curated features with high predictive value.”

The Teradata Vantage data science update is automatically available now to current customers while Analytics Library will be available in Q4 2020.

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the intelligent data virtualization provider for advanced analytics, today announced an enhanced platform integration with Azure Databricks. The integration extends AtScale’s Cloud Online Analytical Processing (COLAP) to analytics workloads wherever they reside: on-premises, public cloud, multi-cloud, or hybrid environments. AtScale’s latest platform release unlocks new artificial intelligence (AI) and business intelligence (BI) capabilities while alleviating complex and time-consuming data engineering tasks and maximizing analytics ROI.

With enterprises rapidly moving to the cloud, it is critical that OLAP capabilities are readily available for mission-critical AI and BI workloads. With AtScale’s Universal Semantic Layer™, business users and data scientists alike can analyze semi-structured and structured data types wherever the data exists, without requiring manual data engineering or changing the user’s analytics tool of choice. Further, AtScale’s Autonomous Data Engineering™ accelerates the user experience by automating the creation and life cycle of acceleration structures, which improves ROI by avoiding expensive query operations.

“We are proud of our long collaboration with Microsoft,” said Christopher Lynch, Executive Chairman and CEO of AtScale. “Microsoft Excel and Microsoft Power BI are two of the primary BI tools that our customers use to analyze their data, many with data on Microsoft platforms. With our AtScale 2020.3 release, they’ll now be able to add Microsoft Azure Databricks to their Cloud OLAP environments.”

“Microsoft Azure Databricks provides a fast, easy, and collaborative analytics and AI platform to accelerate and simplify the process of building big data and AI solutions backed by industry leading SLAs,” said Bharat Sandhu, Director, Product Marketing, Azure Data + AI, at Microsoft Corp. “We are pleased to see AtScale supporting Azure Databricks because it means that our customers can perform self-service analytics and collaborate effectively on shared BI, AI, and machine learning projects.”

The AtScale 2020.3 platform release includes:

Enhanced Preferred Aggregate Storage — Building on the AtScale 2020.2 release that introduced Preferred Aggregate Storage for AtScale’s Autonomous Data Engineering™, AtScale’s latest platform extends where these data structures can be located and how best to use them by leveraging enhanced AI.
Orchestrator Phase Two — Enhancements include providing a physical and logical view of AtScale nodes, an improved interface, and new logging service.
AtScale core platform improvements, including:
Expanded enterprise data platform support, and
Additional enterprise security features and enhancements.
Additional Microsoft Power BI support — The AtScale connector is an MDX-based connector that publishes the full semantic layer of AtScale virtual cubes, including hierarchies, drill-down paths and measures.
For more information:

Read the Accelerate Azure Synapse Analytics with Cloud OLAP solution sheet.
Watch the OLAP on the Cloud with Azure Databricks and Azure Synapse presentation from the 2020 Global Summer Azure DataFest.
Download AtScale’s Cloud Warehouse Benchmark reports.
Visit AtScale in the Azure Marketplace.

AtScale

The Global 2000 relies on AtScale to provide a single, secured, and governed workspace for distributed data. The combination of the company’s Cloud OLAP Engine, Autonomous Data Engineering™, and Universal Semantic Layer™ powers business intelligence and machine learning, resulting in faster, more accurate business decisions at scale.

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Machine learning, a sub-component of artificial intelligence, is not new to the enterprise. But with techniques like deep learning, emulating human brain actions, increasingly gaining traction, businesses are identifying new and potentially transformative deployments of digitally disruptive technologies.

According to Algorithmia’s 2020 report, the main use cases for machine learning translate to customer service (i.e. chatbots) and internal cost reduction. But machine learning has applications far and wide. Dynamic pricing or surge pricing is essentially ML models that learn from corresponding factors that include customer interest, demand and history to adjust prices and entice purchases. Churn modelling is another application in telecom analytics where Machine Learning is deployed to predict which customers are likely to be lost and allowing corrective measures to be undertaken to mitigate the churn.

Currently, to Ensure Business Continuity in the Covid-19 era, more and more businesses are moving to the cloud, and the cloud is making AI and Machine Learning more accessible to the enterprise. Here are a few cloud deployments that find enterprise adaptability-

• AWS

Amazon’s cloud service, AWS offers a wide range of machine learning solutions on the cloud, with Amazon claiming that more machine learning happens on its platform than anywhere else. Of particular note is Amazon SageMaker, which is focused on simplifying the process of building, training and deploying machine learning models. It does this in part through a web-based visual interface allowing for the uploading of data, the tuning of models and comparisons of performance.

AWS has also developed specific hardware for machine learning, with an inference chip known as Inferentia, which is intended for sophisticated applications such as search recommendations, dynamic pricing and automated customer support, and is accessible through the cloud.

• Google Cloud

Google is perhaps the company most associated with machine learning, thanks to its development of the open-source TensorFlow platform, as well as its association with one of the most advanced machine learning companies – DeepMind and its programs such as AlphaGo.

Intended for enterprise use, Google Cloud’s AI Platform combines and integrates different aspects of the machine learning pipeline, from data storage and labelling to training to deployment.

• Microsoft Azure

Microsoft’s Azure cloud platform has built-in machine learning services for enterprises looking to bring machine learning models to bear. With a stated focus on MLOps, the subset of DevOps dealing with correct machine learning development practices, it includes both code-based and drag-and-drop environments to accommodate users of all skill levels.

Azure also has a focus on the potential perils of machine learning, building in so-called ‘responsible machine learning’ solutions to mitigate bias in models.

Summing up, with the proliferation of machine learning services on the cloud critically becoming indispensable to push down operational costs and opening up possibilities, expect enterprises to leverage the technology going forwards. ML will open up new methods of customer interaction, as chatbots are proving, and highlighting areas in need of efficiency, are enterprises ready for this massive change?

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As time-series database provider InfluxData adds the last major piece in its multi-cloud strategy, adding Microsoft Azure, it has entered an OEM agreement to embed its data platform into PTC’s ThingWorx IoT platform. PTC has not announced when the embedded database will be formally available, but we expect it will be sometime during the remainder of this year.

Under the agreement with PTC, InfluxDB Cloud will be sold, delivered, and supported by PTC as part of its ThingWorx service. In turn, PTC has had a long relationship with Microsoft dating back to its origins as a product lifecycle management (PLM)/CADCAM firm provider (a business that it’s stll very much in). And, while Microsoft Azure was already on InfluxData’s roadmap for its new cloud service (the service was already available on AWS and GCP), the PTC agreement hastened events.

Both companies are products of transition. InfluxData’s original sweet spot was with IT log data, a market that has been dominated by search-based analytics engines such as Splunk and Elasticsearch. But with the growth of IoT, most of its newer customers were looking to time series databases for use cases, especially in manufacturing operations. If the company had to embrace a second-generation serverless cloud-native architecture, the shift to IoT use cases provided a useful breakpoint as, arguably, most IoT data tends to live in the cloud anyway.

For its part, PTC pivoted about five years ago from the PLM market to add Industrial IoT (IIoT) — in essence, broadening its audience from design to add manufacturing engineers. ThingWorx is a framework for IoT services, including a modeling environment for defining digital “things;” a development studio for creating augmented reality experiences that can be used for interacting with IoT devices in the real world; a connectivity tier for linking, monitoring, and managing devices; and analytics for performing real-time pattern detection and automated predictive analytics. While also available on AWS, PTC designed Azure as the preferred cloud for ThingWorx. For ThingWorx, InfluxData provides the missing piece — a time-series database for persisting device data.

As noted, InfluxDB Cloud was originally designed as a cloud-neutral service based on a cloud-native architecture using microservices and running as a stateful application on Kubernetes. Initially released on AWS, the service went live on Google Cloud about three months ago as part of GCP’s open-source database program, which involves joint marketing and support of open source databases running on its platform.  There was little question that InfluxDB Cloud would eventually come to Azure, but with the OEM deal with PTC, Azure support was going to come sooner rather than later.

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Ectron Corporation announced it has joined Microsoft Azure Certified for Internet of Things (IoT), ensuring customers get IoT solutions up and running quickly with hardware and software that has been pre-tested and verified to work with Microsoft Azure IoT services. Microsoft Azure Certified for IoT allows businesses to reach customers where they are, working with an ecosystem of devices and platforms, allowing for faster time to production.

Ectron Corporation, based in San Diego, California supplies ruggedized signal conditioners, amplifiers, and thermocouple calibrators. Ectron’s Azure IoT certified family of ECT-ECI edge computers enable our customers to deploy Ectron’s plug and play solution for IIoT (Industrial Internet of Things) and provide edge and cloud analytics working with Microsoft Azure.

“Microsoft Azure Certified for IoT validates our ability to jumpstart customers’ IoT projects with pre-tested device and operating system combinations,” said Gautam Kavipurapu, Ectron’s COO and VP of engineering. “Decreasing the usual customization and work required for compatibility ensures Ectron helps customers get started quickly on their IoT solution.”

“Microsoft Azure Certified for IoT extends our promise to bring IoT to business scale, starting with interoperable solutions from leading technology companies around the world,” said Jerry Lee, Director of Marketing for Azure Internet of Things, Microsoft Corp. “With trusted offerings and verified partners, Microsoft Azure Certified for IoT accelerates the deployment of IoT even further.”

IoT projects are complex and take a long time to implement. Customers find that choosing and connecting the right set of devices, assets or sensors to the cloud can be time-consuming. To jumpstart their IoT projects with confidence, customers are looking for certified devices and platforms that are tested for readiness, compatibility and usability with the Microsoft Azure IoT Suite. By choosing a partner from the Microsoft Azure Certified for IoT program, customers can save time and effort on project specs and RFP processes by knowing in advance what devices and offerings will work with the Azure IoT Suite.

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The Internet of Things (IoT) is a broad term with multiple definitions, likely stemming from the different sectors of technology that IoT impacts. According to Microsoft Azure, a cloud computing service created by Microsoft, IoT is defined as “equipment, machines, products, and devices that are connected to the cloud and outfitted to collect and securely transmit data.” The company breaks the overall concept into five common uses for IoT technologies: remote monitoring, predictive maintenance, facilities management, manufacturing efficiency, and connected products.

“Connected products have smart, connective components that allow data to be exchanged between the product and its user, manufacturer, or environment,” stated Microsoft Azure’s definition. “Organizations that invest in the Internet of Things are able to deliver more value, including more personalized customer engagement, less waste of materials and labor, and increased operational efficiency. As data is collected and analyzed, new trends may even reveal new revenue opportunities.”

This is the foundation of IoT. Stepping from that foundation, the HVAC industry is venturing further into the realm of connected devices. Combined with new customer interface demand, the industry finds itself in a prime position to embrace the next evolution of communicating technology.

“Over the last 10 years, we’ve seen connected solutions move from single-point remote controls to entire platform ecosystems that interoperate and automate on behalf of homeowners, simplifying ownership,” said Dave Quam, global connected services director at Resideo. “Homeowners also are becoming more informed on what is causing home discomforts.”

Comfort Experience and Smart Solutions

No longer happy with being warm or cool depending on the thermostat setting, homeowners are seeking what Resideo is calling a comfort experience. This further designation not only includes air temperature, humidity, and IAQ, but it also includes IoT.

“With IoT, data from Resideo’s comfort controls can discover the home’s pulse — its data and energy — and help demystify how the home operates, which ultimately simplifies what it means to own a home through predictive and automated actions,” said Quam. “An example of this is Resideo’s AirCycle service that uses an air quality monitor to generate a personalized report of a home’s IAQ, which helps facilitate a richer consultation between the homeowner and the service professional.”

Comfort often comes at a price, but driven by the principle of value addition, Emerson introduced the Sensi smart thermostat in 2014, which allows homeowners to control their comfort while saving on energy costs.

“IoT technology in smart thermostats also offers environmental sustainability and allows homeowners to participate in demand response programs with utility companies to receive financial incentives on managing their energy demand,” said Kashif Rasheed, director of marketing, White-Rodgers for Emerson. “Many utility companies have a fixed energy supply and are increasingly seeking innovative ways to accommodate peaks in demand without having to resort to carbon intensive and expensive energy from peak plant suppliers. Through demand response programs, customers are offered incentives to provide utility companies with access to smart thermostats to adjust temperature remotely and in turn manage the peak periods of demand and under-supply.”

According to Emerson, energy is one of the largest bills for households and HVAC accounts for approximately half of the total energy bill. The company has seen that using Sensi smart thermostats allows customers to save about 23 percent of their HVAC energy consumption, by adjusting the temperature using flexible scheduling, remote access, and geofencing features.

“With household penetration of smart thermostats at only 12 to 13 percent, we expect to see more widespread adoption of smart thermostats in the coming years,” said Rasheed. “Additionally, we are seeing an increase in customers’ interest to be able to control comfort. IoT technology allows customers to make adjustments that affect comfort remotely, particularly when traveling or away from home for an extended period of time.”

Contractor Experience With Internet of Things

The comfort experience achieved through connected products is also facilitated by remote monitoring and predictive maintenance. These two branches of IoT provide a variety of benefits that improve not only the contractor experience but the customer experience as well.

“HVAC systems don’t break without warning; control logic inside these systems orchestrates retries and recovery when errors occur,” said Quam. “The challenge is that prior to the IoT, these warning signs remained hidden inside the system and only became evident to homeowners after a failure, and usually after the home became uncomfortable. Using IoT data, we can surface these early warning signs in efficient ways to homeowners and contractors alike, making what was once a reactive issue now a proactive conversation, eliminating the stress of sudden breakages.”

Last year, Resideo acquired what it said was a critical appliance monitoring solution that helps professionals see these issues before they become a costly repair.

“Contractors and homeowners using the Resideo app and upcoming monitoring equipment will have access to data that can help diagnose a problem before it gets more serious and prevent premature failure in costly appliances such as water heaters and furnaces,” said Quam.

Emerson’s Sensi Predict Smart HVAC Maintenance solution utilizes IoT technology for predictive and preventive HVAC maintenance and diagnostics. Contractors using the technology are equipped with a “know before you go” solution which enables contractors to address any issue efficiently with a single truck roll. The product also gives contractors real-time data and insights on HVAC systems that predict and diagnose problems before they become bigger issues.

“Diagnostic IoT, such as Sensi Predict, is one trend we’re expecting to see become more and more commonplace,” said Rasheed. “This is still a relatively new technology but has benefits that can revolutionize the way we approach modern HVAC service calls and maintenance agreements. Similar to how car diagnostics are now an expectation, diagnostic IoT technology for HVAC may very well become the new norm.”

Another key element to the contractor experience with IoT is validating installation. According to the Environmental Protection Agency (EPA), 50 percent of HVAC systems in the U.S. are not operating efficiently.

“During installation, contractors are able to monitor system performance in real time, leaving the jobsite in confidence and reducing the number of call backs,” said Rasheed. “Homeowners and contractors can then analyze and stay ahead of problems before they occur, which prevents system failures and extends equipment life.”

Future Plans for IoT

IoT has ushered in a new era for customers and contractors. Where the technology ends is not a consideration as technologies continue to advance and artificial intelligence further adds to the mix.

According to Rasheed, IoT technology will soon be able to support programming replacement parts remotely so contractors can reduce the amount of time spent in cramped areas where installation may be required, such as attics, basements, and crowded utility rooms.

“As platforms mature and data transforms the comfort and efficiencies over time, the HVAC IoT experience will feel dynamic — a constantly curated, evolving, and personalized experience,” said Quam. “Additionally, HVAC service professionals are increasingly integrating IoT and better data into their daily operations, helping to simplify operations, offer value-added differentiated services, and ultimately enhancing the consultative dialogue with their customers. Over time, we’ll see companies focusing more and more on the post-sale services and experiences their products provide the home. Creating a personalized and engaging experience ongoing is a focus of Resideo’s now and into the future.

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