The DISM tool can be available from the Command Line or the Windows PowerShell. DISM tool can be used to update files and fix problems when a Windows image becomes unserviceable. However, when running the DISM tool, you may receive the error code 87 or some other codes like DISM error 50, DISM error 2, DISM error 3, DISM error 1639 and so on, as shown in the following picture

How to Fix Dism Error 87?

1. Use Correct DISM Command.
2. Run this command using an elevated command prompt.
3. Run Windows update.
4. Run System File Checker.
5. Use correct DISM version.
6. Reinstall Windows

Solution 1. Use Correct DISM Command

• DISM /Online /Cleanup-image /Scanhealth
• DISM /Online /Cleanup-image /Restorehealth

Solution 2. Run This Command Using an Elevated Command Prompt( as administrator )

Solution 3. Run Windows Update

The DISM restorehealth error 87 may be caused by the pending Windows update. So in order to solve this error 87 DISM, you need to install all pending updates.

Step 1: Open Command Prompt

1. Type command prompt in the search box of Windows 10 and choose the best matched one.
2. Right-click it to choose Run as administrator to continue.

Step 2: Type the command to continue

1. In the pop-up window, type the command sfc /scannow and hit Enter to continue.
2. Please do not close the command line windows until you see the message verification 100% completed

That’s it hope it help.

What Is SNMP and Why Does Monitoring It Matter?

Simple Network Management Protocol (SNMP) is one of the most widely deployed protocols for monitoring and managing network infrastructure. Defined in a series of IETF RFCs, SNMP allows management systems to collect performance data, receive alerts, and in some cases reconfigure devices — all over a standardised UDP-based interface (port 161 for queries, port 162 for traps).

SNMP is supported by virtually every enterprise-grade router, switch, firewall, server, UPS, and storage device, making it a foundational layer of network observability. Even in environments that have adopted modern telemetry protocols such as gNMI or OpenTelemetry, SNMP often remains the only option for older or embedded hardware.

Core SNMP Architecture Components

Understanding what SNMP monitoring tools actually manage requires familiarity with the four main architectural components that any tool in this space must handle:

• SNMP Manager — The central management station (or software process) that sends queries (GET, GETNEXT, GETBULK) to devices and receives trap notifications. This is the engine of any SNMP monitoring tool.
• SNMP Agent — Software running on the managed device (router, server, printer, etc.) that listens for manager requests, reads the relevant counters or state variables, and returns responses. Agents also proactively send traps when threshold events occur.
• Managed Devices — The physical or virtual infrastructure being monitored: network switches, routers, firewalls, servers, storage arrays, environmental sensors, and any other SNMP-capable endpoint.
• Management Information Base (MIB) — A hierarchical, tree-structured schema that defines every object an agent can expose. Each object has a unique Object Identifier (OID). Vendors publish proprietary MIBs alongside the standard MIBs defined by the IETF (e.g., MIB-II, IF-MIB).

A capable SNMP monitoring tool must be able to parse and walk MIBs, poll agents on configurable intervals, correlate traps to known thresholds, and visualise or alert on the resulting data.

SNMP Versions: v1, v2c, and v3

Most tools on the market support all three SNMP versions, but the choice matters for security:

• SNMPv1 — The original 1988 specification. Community-string authentication only; data sent in plain text. Avoid in production where possible.
• SNMPv2c — Adds GETBULK for efficient bulk retrieval and 64-bit counters (critical for high-speed interfaces). Still uses plain-text community strings.
• SNMPv3 — Introduces authentication (MD5 or SHA) and optional encryption (DES or AES). The only version suitable for environments with security or compliance requirements. Many organisations still run v2c internally due to device or tooling limitations.

Key Vendors in the SNMP Monitoring Tool Space

The market research report cited Cisco Systems, Nagios Enterprises, Microsoft, OpenNMS Group, HCL, and Zoho as notable players. Here is a brief, factual description of each vendor’s known position in this space:

Cisco Systems

Cisco is both a major producer of SNMP-capable devices and a vendor of network management platforms. Products such as Cisco Prime Infrastructure and Cisco DNA Center include built-in SNMP polling and MIB management for Cisco hardware.

Nagios Enterprises

Nagios is one of the most widely known open-source-rooted monitoring platforms. The core Nagios engine, along with Nagios XI (the commercial product), supports SNMP polling and trap handling through a rich ecosystem of community and commercial plugins. Nagios XI ships with built-in MIB management and trap translation.

Microsoft

Microsoft’s System Center Operations Manager (SCOM) includes network device discovery and SNMP-based monitoring. Windows Server also ships with an optional SNMP service that can expose Windows performance counters to external managers. Microsoft’s position in pure-play SNMP tooling is secondary to its role as a managed-device platform.

OpenNMS Group

OpenNMS is an open-source enterprise network management platform with strong SNMP capabilities including auto-discovery, SNMP data collection, MIB compiler, and trap daemon. The OpenNMS Group offers commercial support and the Horizon and Meridian distributions.

HCL Technologies

HCL has network and infrastructure management offerings, including tools that incorporate SNMP monitoring as part of broader IT operations management suites.

Zoho (ManageEngine)

Zoho’s ManageEngine division produces OpManager, a widely deployed network monitoring platform that supports SNMP v1/v2c/v3 polling, MIB browsing, and trap processing. ManageEngine also offers dedicated tools such as MibBrowser Free Tool and OpUtils.

Other Widely Used SNMP Monitoring Tools

The vendor list above is not exhaustive. Other tools with strong SNMP support that DevOps and network teams commonly deploy include:

• PRTG Network Monitor (Paessler) — Sensor-based model; dedicated SNMP sensors for standard and custom OIDs.
• Zabbix — Open-source; supports SNMP polling and traps natively with flexible MIB-based item discovery.
• LibreNMS — Open-source auto-discovering network monitoring system built on SNMP, with a large community MIB library.
• SolarWinds Network Performance Monitor — Commercial platform with deep SNMP integration and automated MIB compilation.
• Checkmk — Combines agent-based and SNMP-based monitoring with automated device discovery.

Typical Application Areas

SNMP monitoring tools are used across a range of operational scenarios:

• Network performance monitoring — Interface utilisation, error rates, packet loss, and latency on routers and switches.
• Hardware health monitoring — CPU and memory usage, fan speeds, temperature sensors, and power supply status on servers and appliances.
• Availability monitoring — Up/down detection via SNMP reachability and trap-based alerts.
• Capacity planning — Long-term trending of bandwidth, storage, and resource utilisation data collected via SNMP.
• Compliance and auditing — Verifying device configurations and software versions across a managed fleet.

Geographic Market Distribution

SNMP monitoring tool adoption is global, reflecting the ubiquity of enterprise networking. The market research report identified the following major regions as relevant to the analysis:

• North America (United States, Canada, Mexico)
• Europe (Germany, UK, France, Russia, Italy, Turkey, and others)
• Asia-Pacific (China, Japan, India, Australia, South Korea, Vietnam, Malaysia, Thailand, Philippines, Indonesia)
• South America (Brazil and others)
• Middle East and Africa (GCC countries and Egypt)

North America and Europe have historically represented the largest markets for enterprise network monitoring software, driven by large financial services, healthcare, and telecommunications sectors with complex infrastructure requirements.

Evaluating an SNMP Monitoring Tool: Key Criteria

When assessing tools for your environment, consider the following criteria:

• MIB support and compiler — Can the tool import vendor-specific MIBs and translate OIDs into human-readable names?
• SNMPv3 support — Is SNMPv3 with AES encryption fully supported, not just documented?
• Trap handling — Can the tool receive, parse, deduplicate, and alert on SNMP traps in addition to active polling?
• Scalability — How many devices and polling intervals can the system handle before performance degrades? Distributed pollers matter at scale.
• Discovery — Does the tool auto-discover devices using SNMP, CDP, LLDP, or network range scanning?
• Integration — Does it integrate with alerting systems (PagerDuty, Opsgenie), ITSM platforms (ServiceNow, Jira), or observability stacks (Grafana, Prometheus)?
• Licensing model — Per-device, per-interface, per-sensor, or subscription-based pricing has significant budget implications at enterprise scale.

SNMP in the Context of Modern Observability

SNMP coexists with — rather than being replaced by — modern observability tooling. Streaming telemetry protocols (gNMI/gRPC) offer lower latency and better scalability for high-end platforms, but SNMP remains the only supported management interface for a large proportion of installed network equipment. Most mature monitoring platforms support both, allowing teams to use streaming telemetry where available and fall back to SNMP elsewhere.

The practical approach for most organisations is to treat SNMP as a required baseline capability, layer modern telemetry on top for supported devices, and centralise all data into a unified observability platform.

Overview: What Changed and Why It Matters

In August 2020, Microsoft executed a notable internal reorganization that reshuffled responsibilities across its Windows + Devices and Azure business units. Unlike the smaller round of layoffs the company made earlier that summer, this restructuring was not primarily a headcount reduction—it was a strategic realignment of teams, leadership roles, and product ownership.

The headline move: Microsoft brought parts of its Azure organization, specifically the COSINE team, into the Experiences and Devices (E+D) organization led by Panos Panay. The goal was to help Windows and Surface teams build more cohesive, end-to-end user experiences and to give the Surface hardware team stronger software and services alignment.

Key Leadership Changes in Windows + Devices

The most significant individual change was Michael Fortin joining Panos Panay’s Experiences and Devices organization. His expanded role combines services teams from COSINE and 365 Experiences and Services into a single new team.

Several COSINE team leads also moved into Panay’s org, including:

• Kevin Gallo
• Mark Alcazar
• Kristian Andaker

This move is particularly notable for developers: because Kevin Gallo was closely associated with Project Reunion (now known as the Windows App SDK), his transfer to the Windows org likely means Project Reunion moved under Windows leadership—widely considered a more natural home for the initiative than Azure.

Additional Windows + Devices org changes included:

• Jeff Johnson leading a newly formed Windows, Developer, and Experiences engineering team.
• Linda Averett joining Windows + Devices to lead a new Windows, Developers, and Experiences PM team.

Leadership positions that remained stable:

• Ralf Groene continuing to lead the Design Team.
• Stevie Bathiche continuing to lead the Applied Sciences Group.
• Robin Seiler now leading the Devices Portfolio and Operations (DPO) team.

Key Changes on the Azure Side

The reorganization was not limited to Windows. Azure also saw structural changes designed to consolidate infrastructure services and prepare for emerging markets.

New Azure Core Team

A new Azure Core team was formed by consolidating Compute, Networking, and Storage services under a single umbrella, spanning both PM and engineering. Girish Bablani was tapped to lead this team.

Azure Edge + Platform

The legacy COSINE team was reorganized by merging services from ASME, PIE, and the remaining COSINE components to create a new team: Azure Edge + Platform. Henry Sanders was appointed to a newly created role of CTO of Azure Edge + Platform.

Azure for Operators: 5G and Virtual Networking

A new Azure for Operators organization was established to pursue opportunities in 5G and virtual networking. This team brought together leadership from two recent Microsoft acquisitions:

• Anand Krishnamurthy, CEO of Affirmed Networks.
• Martin Lund, CEO of Metaswitch.

The formation of Azure for Operators signals Microsoft’s intention to compete in the carrier and telecommunications infrastructure market, an area where cloud vendors see significant growth potential.

Strategic Pillars for Windows + Devices

To understand the thinking behind the Windows + Devices reorganization, Microsoft articulated three strategic pillars guiding the org’s product decisions:

1. Deliver iconic experiences across Microsoft, enabling both modern work and personal use cases.
2. Grow the Windows and Surface business by delivering the best of Microsoft and creating end-user demand.
3. Lead the industry through innovation and technology, enabling the partner ecosystem to thrive.

Context: Surface Business Momentum

The restructuring came on the heels of strong Surface hardware sales in Microsoft’s fiscal year 2020. One significant shift noted at the time: while Microsoft had historically maintained roughly a 50/50 split between consumer and commercial Surface sales, the end of FY20 saw that balance tilt heavily toward commercial customers—likely driven by enterprise remote-work purchasing during the COVID-19 pandemic.

The new organizational structure was designed to build on that commercial momentum by better integrating cloud services with the hardware and software experience.

What This Means for DevOps and Developer Teams

For engineering and DevOps teams that build on Microsoft’s platform, a few implications are worth watching:

• Project Reunion / Windows App SDK ownership moving to Windows leadership could accelerate delivery of a unified Windows development model, reducing fragmentation between Win32 and UWP application models.
• Azure Core consolidation of Compute, Networking, and Storage under a single leadership team may eventually improve cross-service consistency and integration for infrastructure teams.
• Azure for Operators signals a serious push into 5G and edge networking infrastructure, which is relevant to teams building latency-sensitive or distributed applications.
• New organizational boundaries always create transition periods—expect some turbulence in roadmap communication and team contacts while the new structure stabilizes.

All changes described above were reported as effective immediately as of the original publication date in August 2020. Given how quickly Microsoft’s organizational structure evolves, current team names and leadership assignments should be verified against up-to-date Microsoft sources.

Microsoft Confirms CyberX Acquisition

Microsoft officially confirmed its acquisition of CyberX, an industrial IoT (IIoT) cybersecurity startup based in Waltham, Massachusetts. The announcement came more than a month after Israeli media outlets first reported that the two companies were in the final stages of a deal. While Microsoft did not publicly disclose the purchase price, multiple reports — including one from TechCrunch — placed the deal at approximately $165 million.

The acquisition was announced in a blog post co-authored by Microsoft executives Michal Braverman-Blumenstyk (CTO, Microsoft Security) and Sam George (VP, Azure IoT), who outlined how CyberX would be integrated into Microsoft’s existing Azure cloud security portfolio.

Why CyberX? Filling the Industrial IoT Security Gap

Microsoft’s Azure platform already offered multi-layer IoT security and monitoring capabilities prior to this acquisition. However, CyberX addresses a specific and growing challenge: securing devices in industrial IoT (IIoT), operational technology (OT), and critical infrastructure environments — settings where traditional IT security tools often fall short.

Many industrial environments run legacy equipment — programmable logic controllers (PLCs), SCADA systems, industrial sensors — that cannot run security agents. These unmanaged devices present significant blind spots for security teams. CyberX’s platform is designed specifically for this problem.

What CyberX Brings to the Table

Founded in 2013, CyberX had raised $48 million across five funding rounds and employed approximately 163 people at the time of the acquisition. Its most recent round — $18 million closed in April 2019 — was led by Qualcomm Ventures and Inven Capital.

CyberX’s core product is an agentless cybersecurity platform built for industrial environments. Key capabilities include:

• Passive asset discovery: Automatically identifies and inventories unmanaged IIoT and OT devices without requiring agents or active scanning that could disrupt sensitive industrial equipment.
• Continuous network monitoring: Monitors OT networks in real time for anomalous behavior, unauthorized changes, and indicators of compromise.
• Vulnerability management: Profiles each discovered device — including firmware version, open ports, and known CVEs — to help operators understand their attack surface.
• Threat intelligence: Applies threat intelligence specific to industrial control systems (ICS) to identify attack patterns relevant to OT environments.
• Digital asset mapping: Produces a visual map of thousands of devices across a factory floor or facility, showing asset relationships and communication paths.

The platform’s XSense product line focuses specifically on anomaly detection and threat intelligence for securing industrial operations and reducing unplanned downtime caused by cyber incidents.

Integration with Azure Sentinel and the Broader Azure Security Stack

One of the most significant planned integrations at the time of the announcement was with Azure Sentinel, Microsoft’s cloud-native Security Information and Event Management (SIEM) platform. The addition of CyberX’s OT visibility data was expected to give Sentinel the ability to surface cyber threats targeting operational technology assets — something most enterprise SIEMs are not equipped to do out of the box.

This matters because OT attacks — such as those targeting power grids, water treatment facilities, or manufacturing lines — can have physical consequences that go well beyond data theft. Correlating OT signals with IT telemetry inside a unified SIEM is a meaningful step toward converged IT/OT security operations.

Microsoft’s executives also emphasized that the acquisition did not signal a retreat from ecosystem partnerships. Third-party security vendors providing on-the-ground expertise and integration services for OT environments would remain an important part of Microsoft’s go-to-market approach.

CyberX’s Customer Base and Market Position

At the time of the acquisition, CyberX counted among its customers two of the top five U.S. energy providers, a top-five global pharmaceutical company, a top-five U.S. chemical company, and national electric and gas utility operators across Europe and Asia-Pacific. This roster reflects the company’s strength in regulated, high-consequence industries where OT security failures carry serious operational and safety risks.

Strategic Context: The Growing IIoT Security Market

The CyberX acquisition fits into a broader trend of major cloud and security vendors moving to address the OT/ICS security market. Industrial environments are increasingly connected to corporate IT networks and the internet — expanding the attack surface dramatically. High-profile incidents targeting industrial control systems (such as attacks on energy infrastructure) have heightened awareness of the risks.

For Microsoft, adding native OT visibility to Azure IoT and Azure Sentinel positions the company to compete more directly for security budgets in manufacturing, energy, utilities, and critical infrastructure — sectors where Azure was already making inroads through its broader cloud platform.

The acquisition also reflects the difficulty of building ICS/OT security expertise organically. CyberX’s founding team had backgrounds in Israeli military intelligence cyber units, giving it deep domain knowledge in adversarial ICS attack techniques — the kind of institutional knowledge that is hard to replicate quickly.

What This Means for DevOps and Security Teams

For engineering and security teams operating in or adjacent to industrial environments, the key takeaways from this acquisition are:

• OT asset visibility is now a first-class Azure capability. Teams managing hybrid IT/OT environments can expect tighter integration between IIoT device inventory and Azure security tooling.
• Azure Sentinel gains OT context. Security operations teams using Sentinel for IT incident detection can anticipate expanded coverage for OT threats in the same interface.
• Agentless coverage matters. The agentless approach is not optional in OT — it is often a hard requirement. Any security tooling for industrial environments must accommodate devices that cannot run software agents.
• IT/OT convergence security is accelerating. As digital transformation pushes more industrial systems online, the boundary between IT and OT security is dissolving. Architects designing connected factory or smart grid solutions should plan for security tooling that spans both domains.

Note: This article is based on reporting from CRN and TechCrunch from June 2020 and Microsoft’s original acquisition announcement blog post. CyberX technology has since been rebranded and integrated into Microsoft’s security portfolio as Microsoft Defender for IoT.

Udacity and Microsoft Launch Azure Machine Learning Scholarship Programme

US-based online learning platform Udacity announced a partnership with Microsoft in June 2020 to provide scholarships for a new Machine Learning Nanodegree programme focused on Microsoft Azure. The initiative was designed to expand the pool of data science professionals equipped to work with Azure cloud services, targeting both beginners and those looking to advance existing skills.

How the Two-Phase Programme Works

The scholarship programme is structured in two sequential phases, with the second phase reserved for top performers from the first.

Phase One: Foundation Course (10,000 Seats)

• Capacity: Up to 10,000 applicants
• Duration: Two months
• Course title: Introduction to Machine Learning on Azure
• Approach: Low-code experience, making it accessible to learners without deep programming backgrounds
• Community support: Participants are supported by a community channel moderated by Udacity
• Goal: Complete the prerequisites required for the full Nanodegree programme

A standout feature of the foundation course is Azure Labs, which lets students work on projects directly inside live Azure environments from within the Udacity classroom — removing the friction of setting up personal Azure accounts to complete hands-on exercises.

Phase Two: Full Nanodegree Scholarship (Top 300 Performers)

• Eligibility: Top 300 performers from Phase One
• Award: Full scholarship to the Microsoft Azure Machine Learning Engineer Nanodegree programme
• Focus: Advanced, in-depth machine learning engineering skills on the Azure platform

Application Window

Applications for the programme opened on June 10, 2020 and were scheduled to close on June 30, 2020. The programme was open to candidates seeking to build or deepen skills in Azure cloud services and machine learning.

What the Partnership Aims to Achieve

Julia White, Corporate Vice President of Azure Marketing at Microsoft, described the intent behind the collaboration:

“Through our collaboration with Udacity to offer low-code and advanced courses on Azure Machine Learning, we hope to expand data science expertise as experienced professionals will truly be invaluable resources to solving business problems.”

— Julia White, Corporate Vice President, Azure Marketing, Microsoft

Udacity CEO Gabriel Dalporto also highlighted the opportunity for professionals looking to grow their skill sets:

“Udacity’s training programme for Azure machine learning presents an amazing opportunity for those looking to further expand their skill set.”

— Gabriel Dalporto, CEO, Udacity

Why This Matters for DevOps and Cloud Engineers

Machine learning is an increasingly critical capability for DevOps and cloud engineering teams. Integrating ML pipelines into CI/CD workflows, automating infrastructure scaling based on predictive models, and working with data science teams on Azure-hosted workloads are all becoming standard expectations. Programmes like this lower the barrier of entry for engineers who want to understand or build ML solutions on Azure without requiring a deep mathematics background, thanks to the low-code emphasis in Phase One.

The Azure Labs feature is particularly relevant for practitioners: working in a real cloud environment — rather than a sandboxed simulation — means the skills and troubleshooting experience gained are directly transferable to production work.

Note: This programme was announced in June 2020. Application deadlines and scholarship availability will have passed. Check Udacity’s website and Microsoft Learn for current Azure Machine Learning training offerings and scholarship opportunities.

Why Run HPC Workloads in the Cloud?

All major public cloud providers support HPC workloads to some degree. Azure, in particular, offers purpose-built VM families, managed scheduling services, and high-speed interconnects designed for tightly coupled parallel workloads. Moving HPC to the cloud delivers several structural advantages over on-premises clusters:

• High availability: Cloud-native redundancy — including mirrored data centers and availability zones — reduces interruptions in HPC job execution and protects data.
• Elasticity: You can increase or decrease compute, memory, and storage on demand. Pay for what you use, and release resources when jobs finish.
• Reduced capital costs: Eliminating on-premises server purchases, rack space, power, and maintenance removes large upfront costs and reduces technical debt over time.
• Workload distribution: Containerized microservices and orchestration platforms let you distribute HPC jobs across many nodes and even migrate existing toolchains to the cloud with minimal rework.

Effective cloud HPC still depends on the right infrastructure primitives: low-latency networking, high-throughput storage, bare-metal or near-bare-metal compute, batch scheduling, and high-speed interconnects between nodes. Azure provides all of these, which is covered in detail below.

HPC Deployment Models in Azure

Azure supports several deployment patterns depending on how much of your existing infrastructure you want to retain:

• Fully cloud-based: All HPC nodes and storage run in Azure. This is the simplest model for net-new workloads.
• Hybrid (cloud-bursting): Your head node and baseline capacity stay on-premises, while Azure nodes absorb peak demand. Microsoft HPC Pack supports this model natively.
• Burst-only: Use Azure purely as overflow capacity for workloads that exceed on-premises headroom, keeping routine jobs local.

For inter-node communication, Azure supports Remote Direct Memory Access (RDMA) and InfiniBand networking on specific VM sizes (the HB, HC, and HX series, for example). These interconnects are critical for tightly coupled MPI workloads that require low-latency, high-bandwidth node-to-node communication. For GPU-accelerated workloads such as molecular dynamics or deep learning, Azure offers VM sizes with NVIDIA GPUs. The right choice depends on your workload profile and budget.

Core Azure HPC Components

HPC Head Node

The head node is a VM that acts as the central controller for your cluster. It handles job scheduling, dispatches tasks to worker nodes, and aggregates status information. In a hybrid deployment, the head node may run on-premises while worker nodes run in Azure.

Virtual Machine Scale Sets

VM Scale Sets let you provision and manage a group of identical VMs with built-in autoscaling, load balancing, and distribution across availability zones. For HPC, Scale Sets are commonly used for worker node pools. They integrate with schedulers and can scale in or out automatically based on queue depth or custom metrics.

Azure Virtual Network

A Virtual Network (VNet) connects your head node, compute nodes, and storage tier over a private IP space. VNets provide subnet-level traffic control and support secure connectivity back to on-premises environments via Azure ExpressRoute (dedicated private link) or IPsec VPN.

Storage Options

Azure provides multiple storage tiers relevant to HPC:

• Azure Managed Disks: Block storage for OS and local data.
• Azure Files: Fully managed SMB/NFS file shares accessible by multiple nodes simultaneously.
• Azure Blob Storage: Object storage suited for staging input data and storing results.
• Azure Data Lake Storage: Hierarchical namespace over Blob, optimized for analytics workloads.
• Avere vFXT for Azure: A distributed in-memory cache layer that reduces latency for file-based HPC workloads by caching hot data close to compute nodes.

Azure Resource Manager (ARM)

ARM is the deployment and management plane for all Azure resources. You define HPC infrastructure as code using ARM templates (JSON) or Bicep, and ARM handles provisioning order, dependency resolution, and idempotent redeployment. This is the foundation that tools like CycleCloud and Azure Batch build on.

Managing HPC Deployments in Azure

Azure offers three main services for managing HPC cluster lifecycles and job scheduling. Each targets a different operational model.

Azure Batch

Azure Batch is a fully managed job scheduling and compute management service. You define a VM pool, upload your application and data, and submit jobs. Batch handles VM provisioning, task assignment, retry logic, and progress monitoring. It supports autoscale formulas tied to pending task count, making it well suited for embarrassingly parallel workloads such as rendering, simulation parameter sweeps, or media transcoding. Batch integrates with Azure Storage for data staging and supports both Windows and Linux nodes.

Microsoft HPC Pack

HPC Pack is Microsoft’s on-premises-rooted cluster management suite that extends to Azure. It provides a familiar Windows-based interface for job scheduling, node provisioning, and cluster monitoring. HPC Pack is particularly useful for organizations migrating existing Windows HPC Server workloads to Azure, or for teams running hybrid deployments where the head node stays on-premises and Azure nodes act as burst capacity. It supports MPI job types and integrates with Active Directory environments.

Azure CycleCloud

Azure CycleCloud is an enterprise-grade cluster orchestration tool designed for cloud-native HPC. It supports popular open-source schedulers — including Slurm, PBS Pro, LSF, Grid Engine, and HTCondor — through a plug-in model. CycleCloud provisions infrastructure using ARM under the hood, manages autoscaling based on queue depth, and supports multiple file system types that can be mounted to compute nodes. It is the recommended tool for teams that need scheduler flexibility and want a managed control plane without writing raw ARM templates.

Best Practices for Large-Scale HPC on Azure

1. Split Large Deployments Across Multiple Services

Avoid placing hundreds of VMs in a single deployment unit. Keeping each service or scale set under roughly 500–700 VM instances reduces the risk of deployment timeouts, cascading failures, and virtual IP conflicts. Smaller deployment units also give you finer operational control:

• Stop or start specific node groups independently without affecting the entire cluster.
• Terminate idle node groups when no jobs are running to eliminate unnecessary costs.
• Span multiple Azure regions or availability zones for business continuity and disaster recovery.

2. Use Multiple Storage Accounts for I/O-Intensive Node Deployments

For I/O-bound workloads, a single Azure storage account can become a bottleneck when provisioning large numbers of nodes simultaneously. Spreading node images and OS disks across multiple storage accounts distributes the provisioning I/O load. Keep these accounts separate from the storage accounts used for job data, input datasets, and task outputs — mixing provisioning and workload traffic in the same account can degrade performance for both.

3. Right-Size the Number of Proxy Nodes

In hybrid deployments using HPC Pack, proxy nodes (Azure worker role instances) handle communication between Azure compute nodes and the on-premises head node. Proxy nodes are provisioned automatically, but the default count may be insufficient for large deployments under heavy load. Monitor proxy node CPU and network utilization and scale up as needed. As a general guideline, Microsoft recommends approximately ten proxy nodes per deployment, though your actual requirement depends on job count and node density.

4. Use HPC Client Utilities Instead of Remote Desktop for Head Node Access

Connecting many users to the head node via Remote Desktop Services (RDS) consumes head node CPU and memory, potentially degrading job scheduling performance. Instead, have users install HPC Pack Client Utilities on their local workstations. These utilities provide command-line and GUI access to cluster management and job submission without establishing full RDS sessions on the head node, keeping the head node focused on scheduling work rather than rendering desktop sessions.

Conclusion

Azure provides a mature, well-integrated stack for running HPC workloads — from genomics and computational fluid dynamics to financial risk modeling and AI training. The combination of InfiniBand-capable VM families, managed scheduling services (Azure Batch, CycleCloud, HPC Pack), flexible storage tiers, and cloud-native elasticity makes it possible to run production-grade HPC clusters without the capital burden of on-premises hardware.

To get the most out of your Azure HPC deployment: split large clusters into manageable service units, distribute provisioning I/O across multiple storage accounts, size your proxy node tier appropriately for hybrid setups, and route user access through client utilities rather than direct head node desktop sessions. Following these practices helps you avoid common pitfalls and keeps costs proportional to actual workload demand.

Bengaluru: DevOps Enabler & Co., the company behind an exclusive startup bundle for organizations that want to scale their operations, today announced it has attained a gold Microsoft DevOps And Cloud Platform competency, demonstrating a “best-in-class” ability and commitment to meet Microsoft Corp. customers’ evolving needs in today’s mobile-first, cloud-first world and distinguishing itself within Microsoft’s partner ecosystem.

DevOps Enabler & Co., the company behind an exclusive startup bundle for organizations that want to scale their operations, today announced it has attained a gold Microsoft DevOps And Cloud Platform competency, demonstrating a “best-in-class” ability and commitment to meet Microsoft Corp. customers’ evolving needs in today’s mobile-first, cloud-first world and distinguishing itself within Microsoft’s partner ecosystem.

To earn a Microsoft competency, partners must successfully complete exams (resulting in Microsoft Certified Professionals) to prove their level of technology expertise, and then designate these certified professionals uniquely to one Microsoft competency, ensuring a certain level of staffing capacity. They must also submit customer references that demonstrate successful projects, meet a performance (revenue and or consumption/usage) commitment (for most gold competencies), and pass technology and/or sales assessments.

The DevOps Enabler and Co. startup bundle provides customers with cloud migration and enablement, DevOps implementation, remote working security, and migration of their applications to containers.

DevOps Enabler & Co., an ISO 27001:2013 certified organization, provides software delivery automation to help companies grow their business. With expertise in cloud computing and DevOps, the organization helps its customers manage cloud platforms, provides DevOps automation, big data implementation, and management and staffing services. The Microsoft DevOps and Cloud Platform Gold Competency provides DevOps Enabler & Co. with access to Microsoft technical team members and advisors who can help it make the most of Microsoft technologies for its clients.

“This Microsoft Gold DevOps and Cloud Platform competency is a demonstration of our testament to providing nothing but the best to our customers, even in these uncertain times. It shows our commitment to the technology market and the skill and expertise of Microsoft products that we share,” said Ram Suresh, Vice President for DevOps Enabler & Co. “This gold competency shows how our teams have the knowledge and experience to accelerate our customers’ success. It is a major step in our journey, as it brings the power of DevOps and the cloud to our Windows community and especially our Microsoft-based enterprise customers.”

“By achieving a gold competency, partners have demonstrated the highest, most consistent capability and commitment to the latest Microsoft technology,” said Gavriella Schuster, corporate vice president, One Commercial Partner (OCP) at Microsoft Corp. “These partners have a deep expertise that puts them in the top of our partner ecosystem, and their proficiency will help customers drive innovative solutions.”

The Microsoft Partner Network helps partners strengthen their capabilities to showcase leadership in the marketplace on the latest technology, to better serve customers, and to easily connect with one of the most active, diverse networks in the world.

DevOps Enabler & Co. is focused on providing the best of DevOps services to help its customers in the best way possible. The startup bundle isn’t just a consultation; it’s a holistic growth solution designed for companies that want to scale quickly and tackle the challenges that come their way. DevOps Enabler & Co. provides software delivery automation, cloud enablement and management, and big data and staffing services to its customers.

Azure Quantum Opens to Select Developers

At its Build 2020 developer conference, Microsoft announced a limited preview of Azure Quantum, making its cloud-based quantum computing platform available to select developers and enterprises. The company positioned the release as the product of nearly two decades of internal quantum research, embedded across every layer of the Azure Quantum stack.

The limited preview gives participants access to an open ecosystem that spans quantum software, hardware, and solutions — sourced from both Microsoft and a set of launch partners.

Partner Ecosystem and Hardware Access

Azure Quantum launched with hardware and software contributions from four key partners:

• 1QBit — quantum software and optimization solutions
• Honeywell — trapped-ion quantum hardware
• IonQ — trapped-ion quantum processors
• QCI (Quantum Computing Inc.) — quantum-ready software solutions

By aggregating multiple hardware backends under a single cloud platform, Microsoft aims to give developers the flexibility to target different quantum architectures without having to manage separate vendor relationships or toolchains.

Quantum-Inspired Solutions for Classical Hardware

Alongside access to actual quantum processors, Azure Quantum also offers what Microsoft calls quantum-inspired solutions — optimization algorithms derived from quantum computing research that run on classical hardware. These pre-built solutions are designed to tackle complex optimization problems in logistics, finance, and scheduling without requiring a physical quantum machine, making them immediately practical for enterprise workloads.

The Road to a Scalable Quantum Computer

At the time of the announcement, Microsoft had not yet publicly demonstrated a general-purpose quantum supercomputer. Microsoft’s longer-term research program is focused on topological qubits — an approach the company believes will produce more stable and scalable quantum processors compared to competing qubit technologies. Researchers were described as actively developing the software infrastructure needed to support a future scalable quantum machine.

Microsoft expressed confidence that a scalable quantum machine could be achievable within a five-year horizon from the time of the announcement, though no firm timeline or performance milestones were committed to publicly.

Why Qubits Matter: A Quick Primer

Classical computers store and process information as bits — binary values that are either 0 or 1 at any given moment. This binary constraint limits throughput when processing combinatorially large problem spaces.

Quantum computers instead use qubits, which exploit quantum mechanical properties — specifically superposition and entanglement — to represent and process 0 and 1 simultaneously. This allows a quantum processor to evaluate many possible solutions in parallel, offering a theoretical advantage for specific problem classes such as:

• Cryptography and security
• Drug discovery and molecular simulation
• Climate modeling and clean energy research
• Materials design
• Large-scale optimization problems

It is important to note that quantum computers are not universally faster than classical machines — they offer advantages only for specific problem types where quantum algorithms apply.

Competitive Context

Microsoft’s announcement came in the context of a rapidly accelerating quantum computing race. Google had previously claimed to have demonstrated quantum supremacy with its Sycamore processor, reportedly completing a specific calculation in approximately 200 seconds that Google estimated would take classical supercomputers thousands of years — a claim that was subsequently disputed by IBM and others regarding the classical comparison baseline. IBM, Google, and Amazon (via Amazon Braket) were also offering quantum cloud services at the time, making Azure Quantum a direct competitive entry into an already active market.

What This Means for DevOps and Cloud Engineers

For DevOps practitioners and cloud engineers, the Azure Quantum preview represented a low-friction entry point into quantum development. Key practical takeaways from the announcement include:

• Unified access: Multiple quantum hardware backends accessible through the familiar Azure portal and SDK.
• Classical bridge: Quantum-inspired optimization tools that work today on classical infrastructure, without waiting for fault-tolerant quantum hardware.
• Open ecosystem: Microsoft’s stated goal of an open platform means developers are not locked into a single hardware vendor.
• Q# language: Microsoft’s domain-specific language for quantum programming, Q#, is the primary development interface for Azure Quantum workloads.

As quantum computing matures from research curiosity to cloud service, familiarity with hybrid classical-quantum workflows is increasingly relevant for engineers working in high-performance computing, AI infrastructure, and scientific computing domains.

Microsoft has launched its first industry-specific cloud offering aimed at healthcare, with the promise of more industry clouds to follow.

The announcement was made as part of the now-virtual Microsoft Build event. While the major headlines of the developer jamboree have focused around big investments in artificial intelligence, as sister publication AI News has reported, Azure has often featured peripherally underpinning the projects. The partnership with OpenAI – to whom Microsoft invested $1 billion last year – building a supercomputer is hosted on Azure.

This is therefore one of the few announcements with Azure front and centre – and health was an obvious first choice, as Microsoft explained. “Our commitment has always been to ensure the tools we provide are up to the task of supporting our customers in their time of need,” wrote Tom McGuinness, Microsoft CVP worldwide health and Gregory J. Moore, CVP Microsoft health, in a blog post.

“The offer brings together existing and future capabilities that deliver automation and efficiency on high-value workflows, as well as deep data analytics for both structured and unstructured data, that enable customers to turn insight into action,” they added. “A robust partner ecosystem extends the value of the platform with additional solutions to address the most pressing challenges the healthcare industry is facing today.”

The healthcare cloud, which is available in public preview and is available on free trial for the next six months, will connect products including Microsoft Dynamics 365 for marketing and customer service, as well as Azure IoT. Use cases include connected referral management for care teams, self-service portals for patients and providers, and the design of ‘interactive patient journeys [to] help promote better outcomes.’

Microsoft Teams, which is HIPAA-compliant and HITRUST certified, will also be part of this initiative, providing secure chat, voice and video meetings, alongside recording and note transcription.

Health remains one of the primary industries for the leading cloud providers. Google Cloud has its own HIPAA-approved healthcare APIs, which were made generally available a month ago having been in the works for two years, while Amazon Web Services (AWS) has an extensive healthcare and life sciences portfolio. Amid the Covid-19 pandemic, many providers extended services to those working in the industry; Microsoft made Teams available for free to NHS staff in the UK among other initiatives.

Finxact Completes Azure Re-Platforming for Core Banking

Core banking software provider Finxact has announced the completion of a re-platforming initiative that moves its Core-as-a-Service offering onto Microsoft Azure. The move is a direct response to growing demand from the company’s regional and super-regional bank clients and prospects who require Azure-based infrastructure to meet their own procurement, compliance, and operational standards.

What Is Finxact Core-as-a-Service?

Finxact provides a SaaS-delivered core banking platform designed to act as a modern System of Record (SOR) for financial institutions. Key capabilities include:

• Secure record-keeping and transaction processing with enforced system, business, and regulatory rules
• Elastic scalability — technically, functionally, and commercially — to support everything from core conversions to greenfield digital-only bank launches
• An open banking API designed to allow banks to deliver omnichannel and event-driven services without rebuilding their core

The platform is built on the premise that critical banking workloads — traditionally kept on-premises or on private infrastructure — can be securely and reliably operated in the public cloud, while still satisfying the operational continuity and compliance requirements regulators expect of core systems.

Why Microsoft Azure?

Finxact selected Azure based on Microsoft’s compliance coverage, security tooling, and its established position as a trusted cloud platform in the financial services sector. The partnership involved co-development of the datacenter infrastructure configuration to align with Finxact’s application design and deployment patterns.

“Our team worked side-by-side with a talented and attentive team from Microsoft to provision our datacenter infrastructure in a way that optimized our application design and deployment. It was an excellent outcome, and we’re looking forward to future co-development efforts with the Microsoft team to position our joint customers well for enhanced analytics, AI, and other Microsoft Azure solutions.”

— Jim Joyce, Chief Technical Officer, Finxact

“Many banks are looking for efficiency and agility gains from technology advancements. By adopting Microsoft Azure, Finxact will be able to help its clients accelerate their digital banking presence through greater computing scale, unlocking new capabilities.”

— Luke Thomas, Head of Banking, U.S. Financial Services, Microsoft

Broader Context: Cloud Adoption in Core Banking

When Finxact first launched Core-as-a-Service, public cloud adoption in banking was largely limited to front-office workloads — productivity suites and customer-facing channel applications. Running a core banking System of Record on public cloud infrastructure was, and remains, a significant architectural and regulatory decision for any bank.

Finxact’s Azure availability represents an argument that the security controls, geographic redundancy, and compliance certifications available on hyperscale cloud platforms can meet — and in some respects exceed — what banks can achieve in their own data centers for core processing workloads. The pitch is particularly targeted at regional and super-regional banks that may lack the capital and engineering resources to maintain differentiated on-premises infrastructure.

Co-Development Roadmap: Analytics and AI

Beyond infrastructure, Finxact and Microsoft are also pursuing co-development in areas where core transaction data can feed downstream analytics, AI, and decisioning tooling. Microsoft Power BI is cited as one example of a tool that could ingest and surface insights from core transaction data streams, though the specific integrations are described as being in active development rather than generally available.

Early Customer Adoption

At the time of this announcement, middle-class-focused digital bank One had selected Finxact’s Core-as-a-Service to power its banking operations, representing one of the early production use cases for the platform.

About Finxact

Founded in 2016, Finxact describes its mission as freeing banks from the constraints of legacy systems so they can build the bank they envision rather than the bank their technology permits. The company has raised funding from a range of strategic investors in the banking and fintech ecosystem, including the American Bankers Association (ABA), First Data, Accenture Ventures, SunTrust Bank, Live Oak Ventures, and Woodforest National Bank, among others.

The company positions its platform as enterprise-class, public cloud-hosted, private-data architecture with a completely open banking API — a combination it argues differentiates it from both legacy core vendors and newer but less capable cloud-native alternatives.

Application Release Automation (ARA) refers to the tools and practices that orchestrate, standardize, and accelerate the deployment of software releases across environments — from development and testing through to production. As organizations adopt DevOps and continuous delivery, ARA platforms have become a critical part of the software delivery toolchain, reducing manual effort, minimizing deployment errors, and shortening release cycles.

This article provides an independent technical overview of the ARA market landscape, including market segments, notable vendors, deployment models, and the industry factors driving adoption.

What Is Application Release Automation?

ARA platforms automate the steps involved in releasing software, including environment provisioning, artifact promotion, configuration management, approval workflows, and rollback procedures. They sit at the intersection of CI/CD pipelines and IT operations, giving teams a governed, repeatable process for moving code from commit to production.

Key capabilities typically found in ARA tools include:

• Pipeline orchestration: Coordinating multi-stage deployment workflows across teams and environments.
• Environment modeling: Representing the topology of target environments so deployments are consistent and auditable.
• Approval gates: Enforcing change-management controls and compliance checkpoints before promotion.
• Rollback and remediation: Automating recovery when a release fails validation.
• Audit trails: Logging every deployment action for traceability and compliance reporting.

Market Segments

The ARA market can be segmented along several dimensions that reflect how organizations procure and use these solutions.

By Component

• Tools/Platforms: The core ARA software — standalone products or modules within broader DevOps platforms.
• Professional Services: Consulting, implementation, and integration services that help organizations deploy and customize ARA tools.
• Training and Education: Enablement programs for engineering and operations teams.
• Support and Maintenance: Ongoing vendor support contracts.
• Managed Services: Fully outsourced ARA operations delivered by a service provider.

By Deployment Model

• On-premises: Self-hosted within the organization’s own data centers, preferred by enterprises with strict data sovereignty or compliance requirements.
• Cloud (SaaS/hosted): Vendor-managed instances delivered over the internet, offering faster onboarding and reduced infrastructure overhead.

By Organization Size

• Small and Medium-sized Enterprises (SMEs): Often favor lightweight, cloud-native tools with low operational overhead.
• Large Enterprises: Typically require enterprise-grade governance, integration with ITSM platforms, and support for complex multi-tier environments.

By Industry Vertical

• IT-Enabled Services (ITES) and Telecommunications
• Banking, Financial Services, and Insurance (BFSI)
• Manufacturing
• Retail and Consumer Goods
• Healthcare
• Media and Entertainment
• Government and Public Sector
• Travel and Hospitality

BFSI and healthcare verticals are notable for their stringent regulatory environments, where ARA’s audit and approval capabilities are especially valuable. Telecommunications and ITES organizations, which operate large-scale, frequently updated service platforms, are also strong adopters.

Key Vendors in the ARA Space

The ARA landscape includes established enterprise software vendors alongside specialist DevOps tool providers. Notable players include:

• BMC Software — enterprise IT automation with strong ITSM integration
• Broadcom (formerly CA Technologies) — release automation as part of a broad DevOps portfolio
• IBM — UrbanCode Deploy and related tooling for enterprise release management
• Micro Focus — release management and testing automation for large enterprises
• Microsoft — Azure DevOps Pipelines for cloud-native and hybrid release workflows
• Red Hat — Ansible Automation Platform for infrastructure and application deployment
• VMware — deployment automation integrated with virtualization and cloud infrastructure
• CloudBees — enterprise CI/CD and release orchestration built on Jenkins
• CollabNet VersionOne — DevOps and agile toolchain integration
• Electric Cloud (now CloudBees) — pipeline and release orchestration focused on enterprise scale
• Octopus Deploy — deployment automation with strong support for .NET and multi-cloud targets
• Puppet — infrastructure-as-code and continuous delivery for complex environments
• Chef Software (now Progress) — infrastructure automation and compliance
• XebiaLabs (now Digital.ai) — release orchestration with value stream management features
• Plutora — release management and value stream analytics
• Datical (now Liquibase) — database change automation as part of the release pipeline
• Inedo — ProGet and BuildMaster for artifact management and deployment
• Fujitsu, NIIT Technologies, Rocket Software, MidVision, Flexagon, Clarive, Arcad Software, Attunity — additional specialized vendors serving niche or regional markets

Note: The DevOps tooling market consolidates quickly. Several vendors listed above have since been acquired or rebranded. Always verify current vendor status before making procurement decisions.

Regional Outlook

ARA adoption is global, but regional maturity and growth rates vary:

• North America: The most mature market, driven by large enterprises with established DevOps programs, a strong cloud-native ecosystem, and high technology investment.
• Europe: Strong adoption, particularly in BFSI and manufacturing. GDPR compliance requirements increase demand for auditable, policy-driven release pipelines.
• Asia Pacific: A high-growth region, driven by digital transformation initiatives in financial services, e-commerce, and telecommunications across markets such as India, China, Australia, and Japan.
• Latin America, Middle East, and Africa: Emerging markets with growing cloud adoption and increasing investment in DevOps practices, though starting from a lower base.

Key Market Drivers

Several converging forces are expanding demand for ARA platforms:

• DevOps and CI/CD adoption: As organizations invest in continuous integration and delivery, automated release pipelines become a prerequisite rather than a luxury.
• Cloud migration: Moving workloads to cloud and hybrid environments increases deployment complexity, making automation essential for consistency.
• Regulatory compliance: Industries such as finance and healthcare require documented, repeatable release processes with clear approval trails.
• Release frequency pressure: Competitive markets demand faster software delivery, which is unsustainable with manual release processes at scale.
• Microservices and containerization: Architectures built on containers and Kubernetes introduce coordination challenges that ARA tools help manage.

Key Challenges

• Integration complexity: ARA tools must integrate with a wide variety of CI tools, artifact repositories, ITSM platforms, and target environments. Integration effort can be significant.
• Cultural resistance: Automating release gates can conflict with established change-management cultures, requiring organizational change alongside tooling.
• Skill gaps: Building and maintaining sophisticated release pipelines requires expertise that many teams are still developing.
• Vendor consolidation: Rapid M&A activity in the DevOps space creates uncertainty around product roadmaps and long-term vendor viability.
• Security in the pipeline: Automated pipelines that have broad environment access become high-value targets and must be secured accordingly.

Impact of Broader Technology Trends

The ARA market does not exist in isolation. Its trajectory is shaped by adjacent trends in software delivery:

• Value Stream Management (VSM): ARA vendors are increasingly incorporating VSM capabilities to give engineering and business leaders visibility into end-to-end software delivery performance.
• GitOps: For Kubernetes-native environments, GitOps patterns (using Git as the source of truth for deployments) are influencing how ARA capabilities are implemented.
• AI and ML in pipelines: Emerging capabilities include anomaly detection in deployment telemetry and predictive analysis of release risk.
• Platform engineering: Internal developer platforms are absorbing ARA capabilities, with teams building self-service deployment tooling on top of Kubernetes and cloud-native infrastructure.

Questions to Ask When Evaluating ARA Tools

When assessing ARA platforms for your organization, consider the following:

• Does the tool integrate with your existing CI system, artifact repository, and ITSM platform?
• Does it support your target environments (on-premises, AWS, Azure, GCP, Kubernetes, mainframe)?
• What approval and compliance workflow capabilities does it provide?
• How does it handle secrets and credential management in deployment pipelines?
• What visibility does it offer into deployment history, drift, and failure analysis?
• What is the vendor’s roadmap for cloud-native and container-based deployments?
• What is the total cost of ownership, including integration and ongoing maintenance?

Summary

Application Release Automation has moved from a niche enterprise capability to a mainstream component of modern software delivery. As organizations increase release frequency, adopt cloud-native architectures, and face growing compliance requirements, the business case for ARA tooling strengthens. The vendor landscape is broad and evolving, ranging from heavyweight enterprise platforms to lightweight, developer-friendly tools — giving teams meaningful choices depending on their scale, stack, and maturity.

Evaluating ARA platforms should be driven by your specific pipeline architecture, compliance requirements, and team capabilities rather than analyst rankings alone. A proof-of-concept with your actual workloads and environments will reveal integration friction and usability issues that no market report can surface.

Redis Labs and Microsoft Form Strategic Partnership

Redis Labs announced a strategic agreement with Microsoft to deliver Redis Enterprise as new, fully integrated tiers of Azure Cache for Redis. The offering, initially available in Private Preview at the time of announcement, extends the standard Azure Cache for Redis service with enterprise-grade capabilities designed for mission-critical, large-scale workloads.

The partnership positions Redis Enterprise not as a separate product customers must manage independently, but as first-class tiers embedded directly within the Azure Cache for Redis experience — meaning procurement, billing, and management all flow through the familiar Azure portal.

What the Enterprise Tiers Add to Azure Cache for Redis

Standard Azure Cache for Redis is built on open-source Redis. The new enterprise tiers layer Redis Enterprise on top, unlocking capabilities that are not available in the open-source distribution:

• Redis on Flash: Extends the Redis dataset beyond DRAM by tiering data across NVMe SSDs, enabling terabyte-scale datasets at costs comparable to disk-based datastores.
• Redis Modules: Native support for purpose-built data models including probabilistic data structures, time-series, full-text search, and streams.
• Active geo-replication: Planned support for active-active, geo-redundant cache configurations suited to hybrid-cloud and multi-region architectures.
• Up-to-date Redis versions: Access to the latest Redis releases, including native data structures not yet available in older managed tiers.

Key Benefits for Azure Customers

Improved Developer Productivity

Because the enterprise tiers integrate directly into the Azure Cache for Redis interface, developers can provision a Redis Enterprise cluster within minutes using tools and workflows they already know. Native integration with other Azure services means applications can consume Redis Enterprise without requiring significant architectural changes.

Greater Resiliency, Security, and Compliance

The enterprise tiers come with stronger SLAs backed by Redis Enterprise’s active geo-replication technology, which maintains multiple active dataset replicas across regions. Network isolation support helps organizations meet security and compliance requirements for sensitive workloads.

Simplified Procurement and Lower Total Cost of Ownership

Redis Enterprise consumption is billed as part of the customer’s standard monthly Azure invoice, removing the need for a separate vendor relationship. Consumption-based pricing allows teams to scale their Redis footprint in real time, potentially reducing over-provisioning costs compared to fixed-capacity deployments.

Why This Matters: Moving Mission-Critical Workloads to Azure

For organizations migrating on-premises databases to the public cloud, a fully managed, enterprise-grade caching layer with strong SLAs and geo-redundancy addresses one of the most common concerns: data resilience and latency at scale. Redis on Flash in particular is relevant for teams whose datasets are too large to hold entirely in DRAM but still require low-latency access patterns that disk-based databases cannot match.

The active geo-replication roadmap item is especially significant for hybrid-cloud architectures, where a single cache cluster must serve both cloud-native and on-premises consumers with consistent, low-latency reads and writes.

What the Companies Said

“The new enterprise tiers of Azure Cache for Redis fulfills the desire we hear from customers for a fully managed cloud data store that provides significant cost savings for large datasets and the greatest simplicity to consume Redis Enterprise with the confidence and resilience they need from their data layer as they move to Azure.”

— Ofer Bengal, CEO and Co-founder, Redis Labs

“The integration with Redis Labs underscores Microsoft’s vision for a successful cloud and open source collaboration that serves the needs of developers and supports the open source ecosystem for continued innovation. We believe the new enterprise tiers of Azure Cache for Redis will help companies deliver their most critical applications with scale, high availability, and low latency.”

— Julia Liuson, Corporate Vice President, Developer Division, Microsoft

Availability

At the time of announcement in May 2020, the enterprise tiers of Azure Cache for Redis were available in Private Preview. Organizations interested in early access were directed to contact Redis Labs or Microsoft through their respective Azure and enterprise sales channels. General availability timelines were not disclosed in the original announcement.

Check the Azure Cache for Redis product page for current tier availability and pricing, as the service has continued to evolve since this announcement.

Why Split Workloads Across Environments?

Not every workload belongs in the same environment. Organizations typically split workloads for two broad reasons:

• Cost optimization: Dynamic, bursty, or temporary workloads—such as software testing, peak-demand traffic, or batch analytics—can be run on a public cloud where you pay only for what you consume, rather than over-provisioning on-premises hardware.
• Data sensitivity and compliance: Regulated or sensitive data (financial records, healthcare data, personal identifiable information) may need to stay on-premises or in a private cloud to meet compliance requirements and data sovereignty obligations.

Understanding the Three Deployment Models

Public Cloud

A public cloud is operated by a third-party provider—such as Microsoft Azure, AWS, or Google Cloud—who charges customers based on resource consumption. The provider owns, manages, and maintains the underlying infrastructure. Public clouds are well suited for:

• Absorbing peaks in workload demand
• Developing and testing applications
• Running big data analytics jobs
• Rapid prototyping and proof-of-concept work

Private Cloud

A private cloud is dedicated to a single organization, hosted either on-premises in the organization’s own data center or through a managed private hosting provider. Because the organization retains greater control over the infrastructure, private clouds are commonly used for compliance-sensitive workloads where security and governance cannot be compromised.

On-Premises Resources

On-premises resources are deployed and managed entirely within the organization’s own facilities. The organization is responsible for hardware procurement, maintenance, patching, and all related operational processes. While this model offers maximum control, it requires significant capital investment and operational overhead.

A hybrid cloud strategy combines all three models, giving an organization the flexibility to place each workload in the environment best suited to its requirements—balancing scalability, cost, security, and regulatory compliance.

A Framework for Splitting Workloads

Whether you are migrating to Azure Stack or planning a broader hybrid strategy, a structured four-phase approach helps avoid costly mistakes:

1. Qualification

Use tooling to analyze existing workloads and infrastructure. The goal is to characterize each workload by:

• Utilization patterns (CPU, memory, storage, network)
• Data classification (public, internal, confidential, regulated)
• Technical dependencies (operating systems, middleware, databases)
• Performance and latency requirements

This analysis produces a workload profile for each application or service, which informs subsequent decisions about where each workload should live.

2. Assessment

With workload profiles in hand, evaluate which hosting environment best fits each profile. Consider:

• Compliance and data residency requirements
• Total cost of ownership across public, private, and on-premises models
• Service-level requirements (availability, latency, throughput)
• Application architecture compatibility with IaaS or PaaS models

Group workloads that share characteristics so they can be migrated together, reducing complexity and coordination overhead during execution.

3. Execution

Migrate workload groups to their target environments according to the plan. Prioritize validation at each step: verify that migrated workloads are functioning correctly, that data integrity is preserved, and that performance meets expectations before decommissioning source environments.

4. Optimization

Migration is not a one-time event. Continuously monitor resource utilization, cost, and performance after migration. Use this data to right-size allocations, identify further optimization opportunities, and adjust workload placement as business requirements evolve.

How Azure Stack Supports Workload Splitting

Microsoft Azure Stack is a hyper-converged infrastructure platform that extends Azure’s public cloud capabilities into your own data center. Its core value proposition is architectural consistency: Azure Stack uses the same APIs, tooling, and management interfaces as Azure’s public cloud, eliminating the integration and compatibility friction that typically makes hybrid deployments complex.

Key Capabilities

• Unified management: Manage public cloud and on-premises workloads from a single interface, reducing operational overhead.
• IaaS and PaaS support: Import and run virtual machines under an IaaS model, or deploy applications on managed platform services under a PaaS model—without changing your toolchain.
• Resource aggregation: Azure Stack aggregates and classifies underlying hardware resources, then allocates capacity dynamically to workloads based on demand.
• Consistent developer experience: Development teams use the same Azure SDKs, CLI tools, and ARM templates regardless of whether they are targeting the public cloud or an Azure Stack deployment, which accelerates application development and simplifies deployment pipelines.
• Disconnected operations: Applications can run on Azure Stack in environments with limited or no connectivity to the public internet, then sync data once connectivity is restored—useful for edge or remote scenarios.

Common Enterprise Use Cases

Who Benefits Most from Azure Stack?

Azure Stack is particularly well suited for organizations that:

• Operate in heavily regulated industries (finance, healthcare, government) where data cannot leave a specific jurisdiction
• Are already Azure customers and want to extend the same operational model to on-premises workloads
• Need low-latency processing that a public cloud cannot provide due to geographic distance or connectivity constraints
• Want to modernize IT infrastructure gradually without a full lift-and-shift to public cloud
• Run edge or remote sites that require local compute but benefit from centralized management

Summary

Splitting workloads across public, private, and on-premises environments is a practical strategy for balancing cost, performance, and compliance. A disciplined approach—qualification, assessment, execution, and ongoing optimization—helps ensure each workload lands in the environment that best meets its requirements.

Microsoft Azure Stack simplifies hybrid workload management by bringing Azure’s public cloud capabilities on-premises with full architectural consistency. For organizations that need the agility of a public cloud but cannot move all workloads off-premises, Azure Stack offers a credible path to a functioning hybrid architecture without the integration complexity that typically accompanies mixed-vendor environments.

Background: VMware Workloads in Azure Before This Announcement

Enterprise data centers have increasingly adopted multi-cloud strategies, running workloads across on-premises infrastructure and one or more public clouds on an as-needed basis. For organizations heavily invested in VMware, a key requirement is the ability to extend VMware virtual machine environments into a public cloud without re-platforming workloads.

Before May 2020, Microsoft offered exactly one path for this: Azure VMware Solution, a service built in partnership with CloudSimple, a cloud services firm specializing in VMware-as-a-service. That partnership hit a complication in late 2019 when Google acquired CloudSimple, redirecting the company’s web presence toward Google Cloud Marketplace.

What Microsoft Announced: A Next-Generation, First-Party Azure VMware Solution

In May 2020, Microsoft announced what it described as the “next generation” of Azure VMware Solution. Confusingly, the new offering carries essentially the same name as its predecessor, which led to immediate questions about whether it was a replacement or an evolution of the existing service.

The answer, confirmed by a Microsoft spokesperson to Data Center Knowledge, is that the two services coexist:

“Azure VMware Solution by CloudSimple is still a Microsoft first-party offering (sold and supported by Microsoft). The solution announced [Monday] is an additive Azure service, not a replacement. Customers using the Azure VMware Solution by CloudSimple are not impacted.”

Microsoft spokesperson, via Data Center Knowledge

In practice this means enterprises running the original CloudSimple-backed solution do not need to migrate immediately and will continue to receive Microsoft support and sales coverage.

Key Technical Differences in the New Solution

VMware NSX-T as the Default Network Plane

The most significant architectural change in the new offering is its use of VMware NSX-T as the default network virtualization layer. NSX-T is the portable, multi-hypervisor version of VMware’s network virtualization platform, designed to run across heterogeneous environments including public clouds.

What this means in practice for IT administrators:

• NSX Manager remains the familiar administrative interface — no new tooling required for network operations.
• At general availability, Azure Portal will surface NSX configuration options for private cloud setup, offering a simplified alternative entry point alongside native VMware tooling.
• Each private cloud gets its own vCenter instance, meaning administrators can use native VMware interfaces as they always have.

Bare-Metal Managed Service, Not IaaS

Like its predecessor, the new Azure VMware Solution is not an Infrastructure-as-a-Service (IaaS) offering. Instead, it is a managed service that extends Azure capabilities to bare-metal servers running VMware Cloud Foundation. Workloads are deployed through Azure Portal but managed through standard VMware tools such as vCenter and NSX Manager.

According to Microsoft, running VMware Cloud Foundation in Azure differentiates this service from pure IaaS on Azure or competing clouds.

VMware HCX Enterprise for Live Migration

The new solution also includes an option to adopt VMware HCX Enterprise. HCX is VMware’s workload mobility platform, and the Enterprise tier enables organizations to live-migrate non-VMware workloads into a VMware environment on Azure — without downtime and while keeping those workloads within Azure infrastructure.

Licensing and Cost Considerations

Microsoft highlighted two potential cost advantages for Windows Server and SQL Server workloads running on the new solution:

• Azure Hybrid Benefit: Customers with existing on-premises Windows Server or SQL Server licenses covered by Software Assurance can apply those licenses to Azure VMware Solution workloads, reducing per-unit costs.
• Free Extended Security Updates (ESUs): Workloads migrated to Azure — including those running on Azure VMware Solution — can qualify for free ESUs for end-of-support products such as Windows Server 2008 and SQL Server 2008, which would otherwise require paid ESU subscriptions on-premises.

Microsoft indicated that specific pricing for the new solution would be announced closer to general availability, which was expected in the second half of 2020.

How Close Was the Microsoft–VMware Collaboration?

A natural comparison point is the well-publicized VMware Cloud on AWS, which VMware and Amazon Web Services built through a notably deep engineering collaboration. Data Center Knowledge asked both companies directly whether a similar level of joint development occurred here.

VMware’s response was notably reserved:

“VMware is confident in the performance of the VMware stack running in all of its partners’ clouds. Customers will make their own evaluation plans for various cloud options based on their unique business requirements.”

VMware spokesperson, via Data Center Knowledge

VMware spokespersons declined to answer additional questions on the topic. Microsoft, for its part, described the collaboration in more positive terms:

“Microsoft developed the Azure VMware Solution in close partnership with VMware. In addition, the Azure VMware Solution continues to integrate tightly with the Azure native services over time, in collaboration with VMware engineering.”

Microsoft spokesperson, via Data Center Knowledge

The divergence in tone between the two statements suggests the Azure VMware Solution does not enjoy the same depth of co-engineering as VMware Cloud on AWS — though the degree of technical integration may still be significant.

Summary: What Changed and What Stayed the Same

Key Takeaways for VMware Administrators Evaluating Azure

• Existing CloudSimple-based Azure VMware Solution customers are not required to migrate and will continue to receive Microsoft support.
• The new first-party solution centers on NSX-T, which is better suited to multi-cloud and heterogeneous environments than the older NSX-V architecture.
• Azure Hybrid Benefit and free ESUs make the economics of migrating Windows Server and SQL Server workloads potentially attractive.
• VMware HCX Enterprise inclusion simplifies migration of non-VMware workloads into a VMware-managed environment on Azure.
• Organizations evaluating this against VMware Cloud on AWS should assess the depth of integration and support SLAs independently, as the level of VMware co-engineering appears to differ between the two offerings.

This article was updated for clarity and technical accuracy based on original reporting by Data Center Knowledge, May 2020.

What Is the Azure Sphere Research Challenge?

Microsoft launched the Azure Sphere Research Challenge, a time-limited bug bounty program offering approved security researchers individual rewards of up to $100,000 for discovering and demonstrating dangerous exploits against Azure Sphere — its Linux-based platform designed for internet-connected (IoT) devices.

The challenge ran from June 1, 2020 through August 31, 2020 — a three-month window during which vetted researchers could probe the OS for critical vulnerabilities. It represented a significant expansion of Microsoft’s existing Azure Security Lab, which was announced at Black Hat in August 2019 with a top reward of $40,000.

What Is Azure Sphere?

Azure Sphere is Microsoft’s integrated security platform for IoT devices. It consists of three primary components:

• A custom Linux kernel and OS — a purpose-built operating system hardened for constrained IoT hardware.
• A connected microcontroller unit (MCU) — the Azure Sphere-certified chip that runs the OS and application code.
• A cloud-based security service — Microsoft’s backend infrastructure that handles remote device management, software updates, and protection against denial-of-service attacks and unauthorized firmware changes.

Typical Azure Sphere use cases include consumer appliances such as refrigerators and washing machines, as well as industrial IoT equipment that needs secure, remotely managed firmware. Azure Sphere reached general availability in February 2020.

Architecture: Normal World vs. Secure World

Understanding the challenge scope requires familiarity with how Azure Sphere partitions trust levels:

• Normal World — the Linux equivalent of user mode, where third-party applications run. This is comparable to user space in a standard Linux system.
• Secure World — sits below Microsoft’s custom Linux kernel. This is where the Security Monitor executes. Only Microsoft-supplied code is permitted to run in supervisor mode or in Secure World.
• Azure Pluton — Microsoft’s security subsystem and hardware root of trust embedded in the Azure Sphere chip, responsible for cryptographic operations and secure boot.

The highest reward tier — $100,000 — was reserved for researchers who could achieve code execution on Azure Pluton or within Azure Secure World, representing the most severe possible compromise of the platform’s security guarantees.

Reward Tiers and Scope

The challenge offered tiered rewards based on severity and target:

• Up to $100,000 — Code execution on Azure Pluton or Azure Secure World.
• Lower tiers — Other significant vulnerabilities within the defined challenge scope (specific lower amounts were not detailed in the original announcement summary).

Security vulnerabilities discovered outside the challenge’s defined scope — for example, bugs in the cloud-side Azure Sphere infrastructure — could still be submitted for consideration under the broader Azure Bounty Program. However, physical attacks were explicitly excluded from both the challenge and the general Azure Bounty Program.

What Resources Did Microsoft Provide to Researchers?

Accepted participants received a supported research environment, including:

• An Azure Sphere development kit (hardware).
• Access to Microsoft products and services for research purposes.
• Full Azure Sphere product documentation.
• Direct communication channels with the Microsoft engineering and security team.

This level of supported access was designed to give researchers the practical tooling needed to investigate deep vulnerabilities, rather than conducting purely black-box research.

Industry Partners Involved

Microsoft also engaged a number of established security firms with IoT research expertise to participate in the challenge. Named partners included:

• Avira
• Baidu International Technology
• Bitdefender
• Bugcrowd
• Cisco Talos
• ESET
• FireEye
• F-Secure
• HackerOne
• K7 Computing
• McAfee
• Palo Alto Networks
• Zscaler

Why This Program Matters for IoT Security

IoT devices have historically been among the least-secured endpoints in enterprise and consumer networks. Devices with long operational lifetimes, infrequent patching cycles, and direct access to physical infrastructure represent a significant attack surface. A platform like Azure Sphere — which embeds a root of trust at the hardware level and enforces strict code signing — addresses many of these concerns architecturally.

Inviting external researchers to attempt to break these protections under controlled conditions is a sound approach to validating security claims before adversarial actors find the same weaknesses in production deployments. The expanded reward pool (from $40,000 to $100,000) also signals that Microsoft considers the Secure World and Pluton boundaries to be genuinely high-value targets worth significant investment to protect.

As Microsoft noted at the time: “By expanding the Azure Security Lab, we’re providing more content and resources to better arm security researchers with the tools needed to research high-impact vulnerabilities in the cloud.”

Application Timeline (Historical Reference)

For historical reference, the program’s key dates were:

• Application deadline: May 15, 2020
• Challenge period: June 1, 2020 – August 31, 2020
• Microsoft reviewed applications weekly and notified accepted researchers by email.

Note: This challenge has concluded. For current Microsoft bug bounty programs, refer to the official Microsoft Security Response Center Bounty Programs page.

What Is Container Registry Software?

A container registry is a repository — or collection of repositories — used to store and distribute container images. Container registry software refers to the platforms and services that provide this capability, either as cloud-hosted services or as self-hosted solutions. Teams use registries to version, tag, scan, and pull container images as part of CI/CD pipelines and production deployments.

Registries vary in scope: some are simple image stores, while others integrate security scanning, access controls, replication across regions, and deep integration with orchestration platforms such as Kubernetes.

Leading Container Registry Software Vendors

The market includes both hyperscaler-backed managed services and independent, self-hostable products. Prominent vendors and projects in this space include:

• Amazon Web Services (AWS) – Elastic Container Registry (ECR), tightly integrated with ECS, EKS, and IAM.
• Microsoft – Azure Container Registry (ACR), with geo-replication and integration with Azure DevOps and AKS.
• Docker – Docker Hub, the most widely used public registry, along with Docker Trusted Registry for private deployments.
• JFrog Artifactory – A universal artifact manager that supports Docker images alongside other package formats; popular in enterprise environments.
• Google – Artifact Registry (successor to Container Registry / GCR), integrated with GKE and Cloud Build.
• Oracle – Oracle Cloud Infrastructure Registry (OCIR).
• Red Hat – Quay.io (also available as the self-hosted Red Hat Quay), with built-in image scanning via Clair.
• IBM – IBM Cloud Container Registry.
• Alibaba Cloud – Alibaba Container Registry (ACR), serving the Asia-Pacific market.
• UrbanCode – IBM UrbanCode, focused on enterprise release automation pipelines.

Open-source self-hosted options — such as Harbor (CNCF graduated project) and the Docker Distribution project — also compete in this space, particularly for organisations that require on-premises or air-gapped deployments.

Market Segmentation

By Deployment Model

• Cloud-based: Fully managed registries hosted by a cloud provider. Reduces operational overhead; suits teams already invested in a cloud platform.
• Web-based / self-hosted: Software deployed on-premises or in a private cloud environment. Preferred by organisations with strict data-residency, security, or compliance requirements.

By Organisation Size

• Large enterprises: Typically require private registries, role-based access control, image signing, vulnerability scanning, and integration with existing identity providers and CI/CD toolchains.
• SMEs: Often start with free tiers of public cloud registries or Docker Hub, scaling to paid tiers as image volume and security needs grow.

Key Growth Drivers

Several technical and organisational trends are driving adoption of container registry software:

• Accelerating container adoption: As Kubernetes becomes the de facto container orchestration standard, every organisation running containers needs a reliable place to store images.
• DevSecOps integration: Security teams increasingly demand that registries scan images for known CVEs before deployment, making security-integrated registries a requirement rather than a nice-to-have.
• Multi-cloud and hybrid strategies: Organisations running workloads across multiple clouds benefit from registries that offer geo-replication and cross-cloud access.
• CI/CD pipeline maturity: Automated pipelines that build, test, and deploy container images depend on fast, reliable registries with programmatic APIs.
• Edge and IoT deployments: Pulling images to edge nodes requires lightweight, low-latency registries or local mirroring capabilities.

Challenges and Considerations

Despite strong growth tailwinds, teams evaluating container registry software should account for:

• Vendor lock-in: Cloud-native registries are tightly coupled to their respective platforms. Switching registries later requires updating image references across all manifests, Helm charts, and pipeline configurations.
• Image bloat and storage costs: Without lifecycle policies that automatically delete old or untagged images, storage costs can grow unexpectedly.
• Rate limiting: Docker Hub introduced pull rate limits in 2020, prompting many teams to migrate to alternative registries or set up pull-through caches.
• Security scanning coverage: Not all scanning tools cover the same CVE databases or application-layer vulnerabilities. Teams should validate scanning depth before relying on a registry’s built-in scanner.
• Authentication complexity: Managing registry credentials across clusters, namespaces, and CI pipelines requires a coherent secrets-management strategy.

Evaluating a Container Registry: What to Look For

When selecting a container registry for your organisation, consider the following criteria:

1. Access control: Does it support RBAC, LDAP/OIDC integration, and fine-grained repository-level permissions?
2. Image scanning: Is vulnerability scanning built in, and how frequently are CVE databases updated?
3. Replication: Can images be replicated across regions or to other registries for disaster recovery and low-latency pulls?
4. API and CLI support: Does it implement the OCI Distribution Specification so standard tooling works without modification?
5. Audit logging: Are push, pull, and delete events logged for compliance purposes?
6. Pricing model: Is pricing based on storage, data transfer, or seats? Model your expected usage to avoid surprises.
7. Self-hosting option: If data residency matters, is there a self-hosted version with the same feature set?

Market Outlook

Container adoption continues to grow across enterprises of all sizes, driven by cloud-native application development, microservices architectures, and the maturation of Kubernetes tooling. This directly sustains demand for robust, secure container registries. Competitive pressure from cloud providers has also driven improvements in features and reductions in per-GB storage costs across the board.

The competitive landscape is consolidating around a handful of major platforms, but open-source projects such as Harbor continue to give organisations a self-sovereign alternative. Teams that invest in a well-integrated registry early — with lifecycle policies, scanning, and access control in place — reduce technical debt as their container footprint grows.

Microsoft Moves Deeper Into 5G Infrastructure

Microsoft announced an agreement to acquire Affirmed Networks, a specialist in virtualized, cloud-native mobile network solutions. The deal signals a clear strategic intent: to position Azure as a premier platform for telecommunications operators navigating the transition to 5G.

Affirmed Networks provides a portfolio of 5G applications and services designed to simplify network operations and accelerate the launch of new network-based services. At the time of the announcement, Affirmed was already serving major global telcos including SoftBank, Vodafone, and Orange.

What Affirmed Networks Brings to Azure

Affirmed Networks’ core product is a fully virtualized Evolved Packet Core (vEPC) and 5G core — essentially the software-defined heart of a mobile network. Running these functions in a cloud-native environment, rather than on proprietary hardware, enables operators to:

• Scale network capacity dynamically without adding physical infrastructure
• Deploy and update network functions as containerized microservices
• Run network workloads across hybrid environments that span public cloud and on-premises edge hardware
• Reduce operational costs associated with traditional, hardware-bound telco architectures

Microsoft stated that the acquisition would allow it to extend its cloud offering to operators globally as they increasingly look to operate their networks in hybrid environments — combining private on-premises infrastructure with public cloud capacity.

Microsoft’s Broader Telco Ambitions

The Affirmed acquisition is not an isolated move. Microsoft has been systematically building its telco credentials over many years, even if some earlier efforts (such as ventures into mobile operating systems) did not succeed. The purchase of Skype demonstrated that Microsoft was willing to cross traditional ICT industry boundaries when it saw long-term strategic value.

The larger question the industry is watching is whether Microsoft’s end game extends beyond selling cloud services to telcos, toward building or assembling the components of a global, virtualized network infrastructure of its own — one that could bind data centers to telco edge and access networks on a worldwide scale. Such a capability would make Azure a uniquely powerful platform for delivering end-to-end cloud services to enterprise customers.

Whether the strategy is to empower existing operators or to enable new market entrants — or both — the Affirmed deal puts Azure squarely in the telco infrastructure conversation.

A Crowded and High-Stakes Competitive Landscape

Microsoft is entering territory already contested by several well-resourced rivals:

• Amazon Web Services (AWS) had already established a presence in hosting telecom functions via AWS Wavelength, which embeds compute and storage capacity at the 5G network edge to minimize latency for end applications.
• Google Cloud launched Anthos for Telecom, extending its Anthos hybrid and multi-cloud platform to the network edge, with tools to help operators build, secure, and manage cloud-native and hybrid network environments.
• HPE released its 5G Core Stack, offering software building blocks for operators constructing 5G core networks.
• IBM acquired Red Hat, bringing open-source, cloud-native expertise — including Red Hat’s significant footprint in the telecom software space — under its umbrella.
• Dell Technologies combined Dell’s hardware scale with VMware’s virtualization and cloud software, targeting the infrastructure layer of the telco modernization stack.

Why This Matters for DevOps and Platform Engineers

The convergence of hybrid cloud, telco edge computing, and 5G represents one of the defining infrastructure shifts of the decade. For DevOps and platform engineering teams, this means:

• New edge deployment targets: 5G network edges hosted on cloud platforms like Azure will become viable deployment environments for low-latency applications, expanding the operational scope for engineering teams.
• Cloud-native telco patterns: As network functions move to Kubernetes-based, containerized deployments, the tooling and practices familiar to cloud engineers — Helm charts, GitOps pipelines, observability stacks — become relevant inside telecom networks themselves.
• Vendor consolidation risk and opportunity: Major acquisitions like this reshape platform ecosystems. Teams building on Azure should monitor how Affirmed’s capabilities are integrated into Azure services over time.

Summary

Microsoft’s acquisition of Affirmed Networks is a deliberate step in a longer-term strategy to make Azure the cloud platform of choice for telecommunications operators worldwide. With strong existing competitors already in the space, the outcome is far from certain — but the deal gives Microsoft cloud-native 5G core technology and established operator relationships that meaningfully improve its starting position. The hybrid cloud and telco edge space is set to be fiercely competitive through the remainder of the decade.

Azure’s Rise: A Genuine Competitive Threat to AWS

For most of cloud computing’s history, Amazon Web Services has held a commanding lead in market share. But Microsoft Azure has been steadily closing the gap. According to figures cited around early 2020, Azure grew from roughly 14 percent of the cloud infrastructure market in 2018 to approximately 18 percent by 2019 — a meaningful shift that prompted AWS to reportedly double the size of its sales force to respond to the competitive pressure.

For DevOps engineers and cloud architects, this trend carries a practical message: fluency in Azure is fast becoming as important as AWS expertise, and organizations are increasingly running multi-cloud or Azure-first environments.

Why DevOps Engineers Should Take Azure Seriously

Azure’s growth is not happening in a vacuum. Several factors have driven enterprise adoption:

• Microsoft ecosystem integration: Organizations already running Windows Server, Active Directory, Microsoft 365, or SQL Server often find Azure integration straightforward, reducing migration friction.
• Hybrid cloud strength: Azure Arc and Azure Stack give enterprises mature options for extending cloud services to on-premises infrastructure — an area where Azure has historically led.
• Enterprise agreements: Many large enterprises already have Microsoft licensing agreements that make Azure commercially attractive.
• Developer tooling: Deep integration with GitHub, Azure DevOps, and Visual Studio makes Azure a natural fit for development pipelines already built on Microsoft tooling.

Core Azure Skills Every Cloud Engineer Should Build

Whether you are expanding from AWS or starting fresh, the following capability areas are foundational to productive Azure work:

Compute and App Hosting

Azure Virtual Machines, Azure App Service, Azure Functions (serverless), and Azure Kubernetes Service (AKS) are the primary compute primitives. Understanding when to use each — and how to size and scale them — is essential for any Azure developer or architect role.

Storage and Data Services

Azure offers Blob Storage, Table Storage, Queue Storage, and Azure Files as core storage options, alongside managed database services including Azure SQL Database, Cosmos DB, and Azure Database for PostgreSQL and MySQL. Knowing the right tool for structured, unstructured, and NoSQL workloads is critical.

Identity and Security

Azure Active Directory (AAD) is central to authentication and authorization across Azure services. Engineers should understand role-based access control (RBAC), managed identities, Key Vault for secrets management, and Azure Policy for governance enforcement.

Monitoring and Observability

Azure Monitor, Application Insights, and Log Analytics form the observability stack. Setting up meaningful alerts, dashboards, and diagnostic logging is an operational requirement, not an afterthought.

Networking

Virtual Networks (VNets), subnets, Network Security Groups (NSGs), Azure Load Balancer, Application Gateway, and VPN Gateway are the building blocks of secure, well-architected Azure network topologies.

Azure Developer Certification: AZ-204 (Formerly AZ-203)

The original article references the AZ-203 exam, which was the Microsoft Certified: Azure Developer Associate certification exam available at the time of writing (early 2020). Microsoft has since retired AZ-203 and replaced it with AZ-204: Developing Solutions for Microsoft Azure, which covers similar developer-focused content updated for current Azure capabilities.

The AZ-204 exam targets developers who design, build, test, and maintain cloud applications and services on Azure. Key domains include:

• Developing Azure compute solutions (VMs, containers, App Service, Azure Functions)
• Developing for Azure storage
• Implementing Azure security
• Monitoring, troubleshooting, and optimizing Azure solutions
• Connecting to and consuming Azure services and third-party services

If you are planning to sit an Azure developer exam, verify the current exam code and objectives directly on the Microsoft Learn credentials page, as Microsoft updates exam content on a regular cadence.

How to Structure Your Azure Learning Path

If you are building Azure skills from scratch or transitioning from another cloud platform, a practical learning sequence looks like this:

1. Platform fundamentals: Get comfortable with the Azure Portal, Azure CLI, and Azure Resource Manager (ARM) concepts including resource groups, subscriptions, and management groups.
2. Core services hands-on: Provision and configure VMs, storage accounts, and a basic App Service web app. Use the free tier where available.
3. Identity and access: Set up AAD users, groups, and RBAC assignments. Practice creating service principals and managed identities.
4. Developer services: Deploy a containerized application to AKS or Azure Container Instances. Build a simple Azure Function triggered by a queue message.
5. Observability: Enable Application Insights on a web app and set up a Log Analytics workspace. Write a basic Kusto (KQL) query.
6. Certification preparation: Use Microsoft Learn’s free learning paths and practice assessments aligned to AZ-204 objectives before booking the exam.

Free Resources to Get Started

Microsoft provides substantial free learning material that should be your first stop before purchasing any third-party course:

• Microsoft Learn (learn.microsoft.com): Structured, hands-on learning paths for every Azure role and certification, available at no cost.
• Azure Free Account: Includes a 12-month free tier for select services and $200 in credit for the first 30 days — enough to work through most lab exercises.
• Azure Architecture Center: Reference architectures, best practices, and design guides for common workload patterns.
• Azure documentation: Comprehensive service documentation with quickstarts, tutorials, and how-to guides.

The Multi-Cloud Reality for DevOps Teams

The competitive pressure between AWS and Azure ultimately benefits engineers: skills in both platforms are genuinely valued in the job market, and most large enterprises are running workloads across multiple clouds. Infrastructure-as-code tools like Terraform and Pulumi, and container orchestration via Kubernetes, provide portability across providers. Building a solid foundation in Azure — including its native DevOps toolchain — makes you a stronger cloud practitioner regardless of which provider your employer favors.

Microsoft’s Long History in Automotive Software

Microsoft’s involvement in the automotive industry stretches back decades. Windows Embedded Automotive — first launched in 1998 — powered early connected-car platforms including Ford’s original SYNC, Toyota’s Entune, and Fiat’s Blue & Me. However, active development on Windows Embedded Automotive ceased around 2010, with end-of-support following shortly after.

With that platform era concluded, Microsoft shifted its automotive ambitions toward cloud and DevOps infrastructure. Rather than owning the in-vehicle operating system, Microsoft now positions Azure as the backbone for AV development toolchains, data storage, AI model training, and simulation pipelines.

The Data Problem: 20–100 TB Per Vehicle Per Day

The central engineering challenge for autonomous vehicle development is data scale. According to the Microsoft and Frost & Sullivan whitepaper, AVs operating at higher levels of autonomy are expected to generate between 20 and 100 terabytes of data per day during on-road development and testing.

This data comes from the sensor stack required for autonomous perception: LiDAR, radar, cameras, ultrasonic sensors, GPS, and inertial measurement units all running simultaneously. Every object detection event, lane-keep maneuver, and edge-case driving scenario must be logged, labeled, and fed back into AI training pipelines.

At that scale, on-premises data centers face serious limitations in cost, throughput, and elasticity. The whitepaper argues that cloud infrastructure becomes a practical necessity — not merely a preference — as AV programs scale from prototype to fleet-level trials.

The Four Technology Pillars of Autonomous Vehicles

Autonomous vehicle technology can be broken into four functional layers, each with distinct engineering and infrastructure requirements:

• Perception: Collection and processing of sensor data — object recognition, localization, and environmental mapping. Perception systems generate the bulk of raw data that must be stored and analyzed.
• Planning: AI models that predict and plan safe paths, avoid obstacles, and manage motion at intersections, merges, and complex scenarios. Requires large-scale model training infrastructure.
• Actuation: Low-level control of braking, steering, throttle, and the human-machine interface (HMI). Actuation systems must meet strict real-time and safety certification requirements.
• Software: The connective tissue across all three layers — managing data ingestion, model training, simulation, validation (including software-in-the-loop and hardware-in-the-loop testing), and continuous deployment.

Software is identified as the central competency that determines how fast an AV program can iterate. This is where cloud-based DevOps tooling becomes directly relevant to engineering teams.

Key Engineering Challenges in AV Development

The whitepaper outlines several recurring challenges that AV engineering teams face:

• End-to-end workflow management: AV toolchains span data acquisition, labeling, training, simulation, and validation. Keeping these stages integrated with feedback loops and audit trails for automotive safety certifications (such as ISO 26262) is a non-trivial systems problem.
• Data curation at scale: Not all 20–100 TB of daily sensor data is equally valuable. Teams need pipelines to filter, tag, and selectively store edge cases and high-value driving scenarios without incurring unsustainable storage costs.
• Compute demand for training: Training deep learning models on large labeled datasets — especially for perception tasks like object detection and semantic segmentation — requires significant GPU or specialized AI accelerator capacity that fluctuates based on project phase.
• Simulation and validation: Real-world testing must be complemented by software-in-the-loop (SIL) and hardware-in-the-loop (HIL) simulations to achieve the safety validation mileage required for regulatory approval without proportionally increasing road-test costs.

Emerging Business Models Enabled by Autonomy

The Frost & Sullivan analysis also addresses how autonomous driving is reshaping automotive business models — context that is important for DevOps and platform engineering teams building the services layer on top of AV hardware:

• Mobility-as-a-Service (MaaS): The industry is projected to shift from vehicle ownership to usership models. Robotaxi and shared autonomy platforms require backend infrastructure for fleet management, routing, and payment — all software-intensive problems.
• Vehicle services differentiation: As sensor hardware standardizes across manufacturers, software services and in-vehicle experiences become the competitive differentiator. OEM brand value increasingly depends on software update cadence and service quality.
• Unlocked in-vehicle experiences: With drivers no longer required to focus on the road, new productivity and entertainment applications become viable in-cabin products — creating demand for connected application platforms.
• Usage-based insurance and predictive maintenance: Connected vehicle data enables peripheral services that monetize the telemetry stream, requiring secure data pipelines and analytics platforms.
• Autonomous logistics and long-haul delivery: Vehicles that can operate continuously without driver rest requirements represent a structural efficiency gain for freight. Autonomous last-mile delivery pilots are already underway at several logistics providers.

How Azure Maps to the AV Development Stack

Microsoft’s whitepaper positions Azure as an integrated platform for AV development rather than just a storage backend. The following table, drawn from the whitepaper’s product mapping, illustrates how Microsoft’s services correspond to AV engineering functions:

This mapping is not exhaustive and reflects Microsoft’s own positioning. Organizations building AV toolchains will typically evaluate these services alongside competing offerings from AWS (such as AWS RoboMaker and SageMaker) and Google Cloud (Vertex AI, Cloud Storage), depending on existing vendor relationships and specific workload requirements.

Why Cloud-Native DevOps Is Central to AV Programs

For DevOps engineers working in or adjacent to the automotive space, the key takeaway from this analysis is structural: AV development is fundamentally a software engineering and data engineering problem at scale. The vehicle is, in many respects, a data collection and compute platform that happens to move.

That framing has direct implications for how AV programs should be organized:

• CI/CD pipelines must extend from model training and simulation through to over-the-air (OTA) software deployment on the vehicle.
• Data versioning and lineage tracking are as important as code versioning — knowing which dataset version trained which model version is essential for safety audit trails.
• Infrastructure must be elastic enough to absorb spikes in compute demand during major training runs while remaining cost-controlled during quieter development phases.
• Security and access control must satisfy both automotive safety standards and cloud security best practices, given that vehicles are safety-critical systems connected to public networks.

The Broader Strategic Picture

Microsoft’s positioning in the AV space reflects a deliberate choice to compete as an infrastructure and toolchain provider rather than as a vehicle software platform owner. This mirrors how major cloud vendors have approached other capital-intensive industries — offering the picks-and-shovels rather than mining the ore directly.

For legacy automakers under pressure from both technology-native AV startups and platform companies, the appeal of a proven, scalable cloud toolchain is clear. The alternative — building and maintaining proprietary data infrastructure capable of handling petabyte-scale AV data — is a significant capital and engineering investment that does not directly contribute to vehicle differentiation.

Whether Azure, AWS, or Google Cloud emerges as the dominant AV cloud platform will depend on which ecosystem best addresses the unique constraints of automotive engineering: functional safety certification support, low-latency edge integration, and long-term platform stability commitments that match automotive product lifecycles.

Nadella Arrives in India for Three-Day Strategic Visit

Microsoft CEO Satya Nadella began a three-day visit to India in late February 2020, starting in Mumbai before moving on to meetings with Indian industry leaders, technology partners, and government officials. The visit was widely interpreted as a signal of Microsoft’s deepening commitment to the Indian market, particularly as the company accelerated growth of its Azure cloud platform across the Asia-Pacific region.

India ranks as one of Microsoft’s top-performing markets globally, with more than half of the company’s India revenue reported to be coming from cloud and cloud-based services at the time of the visit — a figure that underscores the strategic importance of the region as Microsoft competes with Amazon Web Services (AWS) and Google Cloud.

New Engineering Hub in Noida Anchors Microsoft’s India Expansion

Just prior to Nadella’s visit, Microsoft opened a new engineering hub in Noida, Uttar Pradesh — a facility intended to develop products and services that support digital innovation at a global scale. Analysts described the hub as a strategically important investment in Microsoft India’s long-term growth story.

“This is a strategic investment for Microsoft and will play an important role in Microsoft India’s expansion and growth story. It will also create thousands of jobs for Indian engineers.”

Sanchit Vir Gogia, Founder and CEO, Greyhound Research

Government Engagement: Digital Transformation of State Ecosystems

A central element of Nadella’s India itinerary was a planned meeting with Uttar Pradesh Chief Minister Yogi Adityanath to discuss the digital transformation of the state’s government ecosystem. This followed a broader initiative Microsoft had launched in August 2019, when the company organized a series of workshops in Lucknow aimed at equipping government IT officials with AI and cloud computing skills. The programme targeted training 5,000 government personnel over 12 months, with a focus on leveraging AI and secure cloud technology for more efficient and transparent governance.

Microsoft was empanelled as a public cloud service provider by India’s Ministry of Information Technology (MeitY) in late 2016 and has continued to hold certification as a cloud provider for multiple state governments. According to tech and policy advisor Prasanto K. Roy, Nadella’s engagement with state-level BJP leadership was part of a broader strategy to reinforce Microsoft’s relationship with both central and state governments.

“Nadella will want to assure the government of his and Microsoft’s complete support for government projects and policy, irrespective of his personal views. A deeper engagement with Uttar Pradesh and a meeting with CM Adityanath is part of that assurance and strategic engagement with both Centre and state governments.”

Prasanto K. Roy, Tech and Policy Advisor

Azure’s Growth and Microsoft’s Cloud Business Momentum

Nadella’s India visit came on the back of strong Azure performance. In its Q2 FY2020 earnings report, Microsoft disclosed that its Intelligent Cloud segment — which includes Azure — generated $11.9 billion in revenue for the quarter, representing 27% year-on-year growth.

Microsoft rebranded Windows Azure as Microsoft Azure in 2014 and has since built it into one of the world’s largest cloud infrastructure platforms, holding the position of second-largest cloud provider globally behind AWS. In India, enterprises such as InMobi and Myntra were reported to be moving to Azure as their preferred cloud platform.

Jayanth Kolla, Partner at Convergence Catalyst, noted that Microsoft had been expanding its research activities in India and running AI and deep-tech pilots in sectors including healthcare — areas where cloud computing and machine learning are increasingly seen as transformative tools.

“From a business point of view, Microsoft has been increasing its research activities in India and running a number of AI/deep tech pilots in India in areas like healthcare.”

Jayanth Kolla, Partner, Convergence Catalyst

Key Challenges: Data Localisation, Reliability, and Market Competition

Data Localisation Requirements

One of the most significant policy challenges Microsoft faced in India at the time of the visit was data localisation — a regulatory push requiring companies to store certain categories of data on servers physically located within India. For a cloud provider like Microsoft, complying with expanding localisation requirements would mean investing in additional data centres at both national and state levels, driving up capital expenditure and ongoing operational costs.

“This for a company like Microsoft that is running a cloud service like Azure and growing it would mean opening more and more data centres locally in India and at the state level too. This would increase the capex cost and the operational expenditure of the company.”

Jayanth Kolla, Partner, Convergence Catalyst

Service Reliability and CIO Concerns

Despite Azure’s growth momentum, enterprise customers in India expressed concerns that Microsoft still needed to address. According to Sanchit Vir Gogia of Greyhound Research, CIO feedback highlighted issues around Azure outages and support quality — specifically around cost transparency and response timeliness.

“Our CIO customers do often share with us their concerns with Azure outages and also support issues around costs and timeliness. Both are extremely important topics for organisations to ensure availability of resources in production.”

Sanchit Vir Gogia, Founder and CEO, Greyhound Research

A Competitive Cloud Market

In India, Microsoft competes directly with AWS and Google Cloud for enterprise workloads. While Microsoft had established a strong foothold and maintained decades-long relationships with the Indian government, the cloud market remained fiercely contested. Analysts noted that Microsoft had much to prove before it could claim cloud dominance in the Indian market, particularly as AWS continued to hold significant mind-share among large enterprises and startups alike.

Start-ups, Developers, and the Partnership Ecosystem

Beyond government and enterprise engagements, Microsoft’s India strategy at the time rested heavily on cultivating relationships with the start-up community and the developer ecosystem. India’s large pool of engineering talent and its rapidly growing start-up sector made it a natural fit for Microsoft’s cloud-first, AI-focused product strategy. Programmes targeting start-ups through Azure credits and AI tooling formed a complementary layer to the government-facing initiatives announced during the visit.

Context: Broader India-Tech Industry Dynamics

Microsoft’s visit took place against a backdrop of complicated dynamics between large technology companies and the Indian government. Several IT majors had scaled back bidding for government contracts citing delayed or non-payment issues. Wipro had, around the same period, suspended services related to Assam’s National Population Register (NPR) project. This environment made Microsoft’s careful diplomatic engagement with state and central government stakeholders all the more significant — maintaining trust while navigating politically sensitive terrain required measured communication at the executive level.

For Nadella, whose family roots are in India, the visit carried both personal significance and substantial business weight. As Microsoft continued to build out its Azure infrastructure globally and deepen AI integration across its product suite, India’s combination of engineering talent, a rapidly digitising government sector, and a large enterprise market made it a region where both the stakes and the opportunities were high.