In today’s data-driven business landscape, enterprise analytics plays a crucial role in informed decision-making and maintaining a competitive edge. Microsoft’s Power BI service has emerged as a powerful tool for organizations seeking robust, scalable, and user-friendly analytics solutions. This blog will delve into some of the key features that make Power BI service an excellent choice for enterprise analytics, with a focus on accessibility, integration, and proactive insights.

1. Mobile Access: Analytics on the Go

In an increasingly mobile world, the ability to access critical business insights anytime, anywhere is paramount. Power BI’s mobile app brings the full power of your analytics to your smartphone or tablet, enabling you to: 

– View and interact with dashboards and reports

– Set up mobile-optimized views of your reports 

– Annotate and share insights directly from your device 

– Use natural language queries to get quick answers


To get started with the Power BI mobile app, simply download it from your device’s app store. Once installed, log in with your work email address to access your workspace and Power BI reports. This seamless integration ensures that you have the same secure access to your data on mobile as you do on your desktop, maintaining data governance and security protocols.

• Best Practice: Design your reports with mobile in mind. Use the “Phone Layout” feature in Power BI Desktop to create mobile-optimized versions of your dashboards and reports. 
  The following screenshot illustrates the Power BI mobile app, showcasing how reports and dashboards appear on a mobile device:

2. Seamless Integration with Microsoft Teams

As remote and hybrid work models become the norm, integration with collaboration tools is more important than ever. Power BI’s integration with Microsoft Teams allows you to: 

– Embed interactive Power BI reports directly in Teams channels and chats

– Collaborate on data analysis in real-time with colleagues 

– Share and discuss insights without leaving the Teams environment 

– Set up data-driven alerts within Teams

• Best Practice: Use the Power BI tab in Teams to create a centralized location for your most important reports and dashboards, making it easy for team members to access critical data within their daily workflow.
  The screenshot below shows how a Power BI report can be viewed on a Microsoft Teams:

3. Automated Report Distribution with Subscriptions

In the high-stakes world of business, staying ahead means staying informed. But let’s face it: nobody dreams of waking up to a flood of reports. That’s where Power BI’s subscription feature comes in, turning information overload into actionable insights at a glance. Instead of drowning in data, decision-makers can now receive a concise snapshot of their most critical metrics right when they need it – whether that’s with their morning coffee or just before a crucial meeting. This smart approach to information sharing ensures that key stakeholders are always equipped with the latest data, without the need to dig through dashboards or lengthy reports. Power BI’s subscription feature allows you to:

– Schedule automatic delivery of reports and dashboards via email 

– Set up different subscription schedules for various stakeholders 

– Send snapshots or links to live reports 

– Manage subscriptions centrally for better control and governance 

• Best Practice: Use row-level security in combination with subscriptions to ensure that each recipient only receives the data they’re authorized to view.
  The following screenshot displays the interface for setting up a Power BI report subscription and how the subscription email come in your inbox.

4. Proactive Insights with Data Alerts

To truly excel, businesses need proactive tools that offer real-time insights and early warnings. Power BI’s data alert feature is designed precisely for this purpose, helping you stay ahead of the curve by : automatically notifying you of critical changes and anomalies in your data, allowing you to address issues before they escalate, and making informed decisions with up-to-date information. Power BI’s data alert feature allows you to:

– Set up custom alerts based on specific metrics or KPIs

– Receive notifications when data changes meet your defined criteria 

– Configure alert sensitivity to avoid notification fatigue 

– Share alerts with team members for collaborative monitoring

• Best Practice: Start with a few critical metrics for alerts and gradually expand. This helps prevent alert overload and ensures that notifications remain meaningful and actionable.

The screenshot below illustrates the process of creating a data-driven alert in Power BI:

Overview

Power BI service offers a comprehensive suite of features that cater to the complex needs of enterprise analytics. By leveraging mobile access, Teams integration, automated subscriptions, and proactive alerts, organizations can foster a data-driven culture that empowers employees at all levels to make informed decisions.

As you implement Power BI in your organization, remember that successful adoption goes beyond just the technology. Focus on user training, establish clear data governance policies, and continuously gather feedback to refine your analytics strategy. 

By harnessing the full potential of Power BI service, your organization can transform raw data into actionable insights, driving innovation and maintaining a competitive edge in today’s fast-paced business landscape. 

This blog post is in continuation to “Why Migrate Legacy Applications to Containers and What are the Challenges this Brings?” where we dove into the transformative world of containerization and learnt why migrating your legacy applications to containers not only future-proofs your infrastructure but also enhances scalability, efficiency, and consistency.

In this part, unravel the complexities of planning a successful migration to containers. From assessing your applications to choosing the right tools, get expert insights into each step of the planning phase.

The migration starts with an assessment of existing applications. It is a very exploratory venture. This step is really key, as it tells which applications are going to be the best fit for containerization and which are likely to need too much alteration. Here’s the process of conducting this assessment:

 

• Application Inventory: Inventory of all applications and services that are running in the current environment. The inventory should be covering the software details, version of the software, underlining infrastructure, dependencies, and usage statistics.

 

• Dependency Mapping: Create detailed dependency maps for each application, including libraries, external services, and data stores they communicate with. Define and create such relations in a container environment using a tool like Docker Compose.

 

• Identify Probable Candidates for Challenges: Search for anything that can act as a hindrance to your migration, such as tightly coupled components, stateful applications, or compliance requirements that might drive what applications need re-architecture or migrate first.

 

Choosing the Right Tools

 

In considering a transition to containers, some really key things are identified in terms of the tools and platforms. Docker and Kubernetes are the most popular, but they carry different purposes:

 

• Docker: This is an accompanying tool in running containers, which empowers users to create, deploy, and run them using simple commands and a Dockerfile. In controlling the lifecycle of the container and developing a container-based application in a local environment, Docker would be perfect.

 

• Kubernetes: While Docker orchestrates at an individual container level, Kubernetes does orchestration of containers at a larger scale. It does deployment, scaling, and management of containerized applications across clusters of machines. It has come out with all the prominence and importance in today’s production environments that call for high availability, escalation, and load balancing.

When choosing tools, consider:

 

• Compatibility: Ensure the tools integrate well with your existing CI/CD pipelines and development workflows.

 

• Scalability: Always go for tooling that will scale with the demands of your application. For example: In case your deployment is of large scale, then Kubernetes is a brilliant tool for that.

 

• Community Support: Prefer options that have strong community support and documentation, if available and reflect reliability and long-term viability.

 

Strategies for a Smooth Migration

Approaching migration with a structured strategy can greatly enhance the process:

 

• Start Small: Make sure to use the lowest criticality or simpler applications first. This will enable you to both manage your risks and learn from the process without impacting major systems.

 

• Pilot Projects: Pilot migration projects provide valuable feedback. Choose a project characteristic for a typical application within an organization but carrying no significant business risk.

 

• Gradual scale-up: After your pilot project is successful, you can start to scale up your migration efforts very gradually. Learn from your mission-critical and more complex applications’ mistakes.

 

• Consider refactoring: Some applications may need refactoring before being containerized. For example, refactoring can mean that one would split a monolithic application into a set of microservices or make an application stateless if possible.

 

Ensuring your team is container-ready is as important as the technical migration aspects. Provide training to upskill the existing team on resources available over the internet on container technologies and Kubernetes. For example, there are a number of online platforms providing courses related to this from introductory to an expert level.

Of course, this would be very strategic to bring in an external organization to help in the shifting of legacy applications to containers. This brings out a number of advantages that would help in smoothening the process, reducing the risks, and realizing more benefits from the move into a containerized environment. Here are some compelling reasons and advantages for enlisting external expertise:

 

Access to Specialized Knowledge and Experience:

 

Expertise: Providing years of expertise around container technologies and their migration to success across many industries. They bring experience
involving best practices and potential pitfalls your migration can be involved in.

Stay Abreast with Technology: That’s the sure deal that your solutions are in line with advancements in technology, such as new developments in
containerization and orchestration tools like Docker and Kubernetes. In essence, you will be able to implement the best and efficient state-of-the-art
solutions.

 

Enhanced Focus on Core Business Activities:

• Resource Allocation: Outsourcing ensures that most of the technical complexities involved in the migration are offloaded; this enables your
internal teams to remain focused on the core business functions rather than drift into the many demands of a complex migration project.
• Reduced Learning Curve: Your staff does not need a couple of days or weeks to train in order to be up-to-date with container technology. The outsourced team will help fill the skills gap and assist your business in adaptation to new technologies much quicker and more productively.

 

Risk Mitigation:

• Tried-and-Tested Methodologies: This would mean that, while the provider’s internal team might have much more knowledge of an organization’s IT setup, they would use proven methodologies—developed over many projects—as a much better insurance policy against risks

 

• Unchanging support: They provide unchanging support and maintenance post-migration, which helps in very quickly getting issues resolved and making iterative improvements to the infrastructure.

Cost Efficiency:

• Predictable Spending: The cost of outsource teams may be lower than developing an internal team, for there would be added costs from the companies involving recruitment, training, and the retention of services from experienced IT practitioners.

 

• Scalability: The outer crew can increase their services according to your project needs. This is much more flexible in comparison to hiring employees on a full-time basis, and therefore, much better budget control is allowed.

Accelerated Migration Timeline:

• Faster Timeframe: Having expert external teams with relevant experience and resources will make a huge difference to the timeframe it takes to
complete the migration. This will be enabled by the tools and processes they have, making it easy to transfer the applications with minimal disturbances from the day-to-day operations.

• Immediate impact: from improved scalability, better efficiency, and improved operational flexibility, these benefits of the rapid deployment bring the
containerization in the organization’s life sooner than later.

 

Objective Assessment and Customization

• Unbiased Recommendations: Get the unbiased recommendations for your IT infrastructure or those even changes that your team may overlook.

• Solutions Tailored for You: They bring their knowledge of serving the tailored solutions that fit differing organizational needs and constraints to perfection. So, the migration strategy aligns spot-on with your business goals.

At Mismo Systems, we understand that migrating your legacy applications to containers can seem daunting. That’s why our team of experienced engineers is dedicated to simplifying your transition, ensuring a smooth and efficient migration process. With our expertise, you can unlock the full potential of containerization to enhance scalability, efficiency, and deployment speed.

Why Choose Mismo Systems?

• Expert Guidance: Our seasoned engineers guide you through the entire migration process, from initial assessment to full-scale deployment, ensuring your business achieves its strategic goals with minimal disruption.

• Customized Solutions: At Mismo Systems, we don’t believe in one-size-fits-all answers. We create tailored solutions that fit the unique needs of your business and maximize your investment in container technology.

 

• Ongoing Support: Post-migration, our support team is here to help you manage your new containerized environment, from optimizing performance to implementing the latest security protocols.

If you’re ready to transform your legacy applications with containers, Mismo Systems is your go-to partner. Contact us today to learn more about how we can lead your business into the future of technology.

At this point, you should have a pretty good foundation under you for planning your migration to containers. Remember that the steps above will help ensure that you are not just transitioning properly but in a manner that is sustainable.

 

In today’s rapidly evolving technological landscape, integrating artificial intelligence (AI) and machine learning (ML) into business operations is no longer a luxury but a necessity. Microsoft’s Azure platform offers a suite of robust AI and ML services designed to empower developers and businesses to build intelligent applications seamlessly. In this article, we delve into three core components of Azure’s AI offerings: Azure AI, Azure Machine Learning Studio, and Azure OpenAI, exploring their features, use cases, and real-world applications.

Azure AI

Azure AI is a comprehensive suite of AI services and cognitive APIs designed to help developers integrate intelligent features into their applications without the need for extensive AI expertise. These services include pre-built models for tasks such as vision, speech, language, and decision-making.

Use Cases:

• Image Recognition: Companies can use Azure AI’s computer vision capabilities to develop applications that can identify and classify images, making it ideal for security systems, inventory management, and quality control in manufacturing. For instance, a retail business could use image recognition to monitor stock levels and automatically reorder products when inventory is low.
• Speech-to-Text: Azure AI’s speech recognition can be leveraged to transcribe customer service calls, enabling businesses to analyze interactions and improve customer satisfaction. This is particularly useful in call centers where monitoring and evaluating numerous calls manually is impractical.
• Anomaly Detection: Financial institutions can utilize Azure AI to detect fraudulent transactions in real-time by identifying patterns and anomalies in transaction data, thus enhancing security and reducing the risk of fraud.

Azure Machine Learning Studio

Azure Machine Learning Studio is a cloud-based environment that supports the end-to-end machine learning workflow, from data preparation to model deployment. It caters to both beginners and advanced users, providing a platform for developing, training, testing, and deploying ML models.

Use Cases:

• Predictive Maintenance: Manufacturing companies can use Azure ML Studio to build models that predict equipment failures before they happen. By analyzing sensor data and historical maintenance records, businesses can schedule timely maintenance, reducing downtime and operational costs.
• Customer Segmentation: Marketing teams can leverage Azure ML Studio to segment customers based on purchasing behavior and preferences. This enables personalized marketing strategies that enhance customer engagement and drive sales.
• Healthcare Diagnostics: Healthcare providers can develop ML models to assist in diagnosing diseases by analyzing medical images and patient data. For example, an ML model can be trained to detect early signs of diseases like cancer from radiology images, improving early detection and treatment outcomes.

Azure OpenAI

Azure OpenAI provides access to powerful language models developed by OpenAI, such as GPT-3. These models are particularly suited for tasks involving natural language understanding and generation.

Use Cases:

• Chatbots and Virtual Assistants: Businesses can use Azure OpenAI to create sophisticated chatbots and virtual assistants that can handle complex customer interactions. These bots can understand and respond to queries in a human-like manner, improving customer service and operational efficiency.
• Content Creation: Media companies can utilize Azure OpenAI to automate content creation, such as generating news articles, marketing copy, or even creative writing. This can significantly reduce the time and resources required for content production.
• Code Generation: Developers can benefit from Azure OpenAI’s capabilities to generate code snippets or complete functions based on natural language descriptions. This can streamline the software development process, allowing developers to focus on higher-level design and problem-solving tasks.

Conclusion

Azure’s AI and ML services provide powerful tools for technologists and business users to develop intelligent applications that enhance operational efficiency, improve customer experience, and drive innovation. By leveraging Azure AI, Machine Learning Studio, and OpenAI, businesses can stay ahead in the competitive landscape, harnessing the full potential of AI and ML technologies.

Introduction to Containerization

Containerization is the era to welcome: a time where complexity would confront simplicity in the field of deploying software. The basic idea is to have software packed into lightweight independent units, which are named containers. Each container has everything it needs to run: code, runtime, system tools, libraries, and settings.

This approach is fundamentally different from the classical ways of deployment, when applications used to run directly onto physical servers or virtual machines, being mixed with the underlying operating system. The concept of containers is not new, but adaptation has exploded with the popularity of platforms such as Docker and Kubernetes, making it more comfortable now to create, deploy, and manage at scale.

The benefits of the approach using containers over traditional approaches are many, but what it all really boils down to, in basic essence, amounts to several very important points: portability, efficiency, scalability, and isolation that provides far more resiliency and manageability in deployment environments.

The Benefits of Migrating to Containers

• Scalability: One of the best benefits of containers is their scalability feature. The ability to scale up and down containers is very easy, as this can be comfortably done in case of demand changes. For example, when there is an e-commerce website that normally gets increased traffic due to the holiday season, the website may automatically increase the number of its pools of containers through container orchestration tools. After that period, scaling back would optimize resource utilization and cost.

 

• Consistency According to Environment: The container provides a consistent environment for the application from development through testing to production. It removes the “it works on my machine” syndrome. A leading global financial services firm, for example, put in place containers that harmonized their development and production environments and cut deployment failures and rollbacks by 90%.

 

• Efficiency and Speed: Containers offer very high efficiency as they share the kernel system of the host and have very fast startup times compared to that of a virtual machine. This efficiency translates into faster deployment cycles and hence more agile response to changes. An example is the leading telecommunications provider that has reduced their deployment times from hours to minutes through containerizing their applications, hence ensuring that they can roll out features more frequently.

Why Now?

The digital shift has been toward transformation by way of containerization, not an option but rather a requirement for most sectors. With cloud computing dominating the atmosphere and pressure coming down heavily on business to provide quick services and remain agile, containers provide a solution to keep one’s head above water and at the same time not fall behind.

And the microservices architectures have already seen their growing adoption and complement the container deployment; since it offers the perfect runtime environment for microservices by isolating each other and dealing smoothly with their interactions.

The risks in retaining legacy systems—such as higher operational cost and more vulnerability to security, not forgetting difficulties in integration with modern technologies—all press for a business to rethink infrastructure strategy. They are a drag to agility and innovation, as they lock an organization into old processes, hence hindering the growth of the processes and adaptation to changes that might be happening in the market.

Challenges faced when moving to new architectures like Containers

When companies embark on the journey to migrate their legacy resources to modern technologies like containers, they often encounter a range of technical challenges. These challenges can vary widely depending on the specific legacy systems in place, but common issues include:

Container Compatibility

• Issue: Many legacy applications are not designed to be containerized. They may rely on persistent data, specific network configurations, or direct access to hardware that doesn’t naturally fit the stateless, transient nature of containers.
• Technical Insight: Containers are best suited for applications designed on microservices architecture, where each service is loosely coupled and can be scaled independently. Legacy applications often have a monolithic architecture, making them difficult to decompose into container-ready components without significant refactoring.

Data Persistence

• Issue: Containers are ephemeral and stateless by design, which means they don’t maintain state across restarts. Legacy applications, however, often depend on a persistent state, and adapting them to a stateless environment can be complex.
• Technical Insight: Solutions involve configuring persistent storage solutions that containers can access, such as Kubernetes Persistent Volumes or integrating with cloud-native databases that provide resilience and scalability.

Network Configuration

• Issue: Legacy applications frequently have complex networking requirements with hardcoded IP addresses and custom networking rules that are incompatible with the dynamic networking environment of containers.
• Technical Insight: Migrating such systems to containers requires the implementation of advanced networking solutions in Kubernetes, such as Custom Resource Definitions (CRDs) for network policies, Service Mesh architectures like Istio, or using ingress controllers to handle complex routing rules.

Dependency Management

• Issue: Legacy systems often have intricate dependencies on specific versions of software libraries, operating systems, or other applications. These dependencies may not be well-documented, making it challenging to replicate the exact environment within containers.
• Technical Insight: This issue can be addressed by meticulously constructing Dockerfiles to replicate the needed environment or by using multi-stage builds in Docker to isolate different environments within the same pipeline.

Security Concerns

• Issue: Migrating to containers can expose legacy applications to new security vulnerabilities. Containers share the host kernel, so vulnerabilities in the kernel can potentially compromise all containers on the host.
• Technical Insight: To mitigate these risks, use container-specific security tools and practices such as seccomp profiles, Linux capabilities, and user namespaces to limit privileges. Regular scanning of container images for vulnerabilities is also critical.

Scalability and Performance Tuning

• Issue: While containers can improve scalability, legacy applications might not automatically benefit from this scalability without tuning. Performance issues that weren’t visible in a monolithic setup might emerge when the application is split into microservices.
• Technical Insight: Profiling and monitoring tools (e.g., Prometheus with Grafana) should be used to understand resource usage and bottlenecks in a containerized environment. This data can drive the optimization of resource requests and limits in Kubernetes, ensuring efficient use of underlying hardware.

Cultural and Skill Gaps

• Issue: Technically, the shift also requires a cultural shift within IT departments. Legacy systems often are maintained by teams not familiar with DevOps practices, which are essential for managing containerized environments.
• Technical Insight: Implementing training programs and gradually building a DevOps culture are necessary steps. This might include cross-training teams on container technologies, continuous integration (CI), and continuous deployment (CD) practices.

Regulatory and Compliance Challenges

• Issue: Legacy applications in regulated industries (like finance or healthcare) might have specific compliance requirements that are difficult to meet in a dynamically scaled container environment.
• Technical Insight: Careful planning is needed to ensure that containers are compliant with regulations. This might involve implementing logging and monitoring solutions that can provide audit trails and ensuring that data protection practices are up to standard.

Initial Considerations

Before bounding down this path of containerization, check what you have and your application portfolio in order to find those candidates that can move. Of course, not every application is perfectly suitable for a containerized environment, with legacy applications often requiring quite a bit of heavy modification to fit into one—it might not be the best candidate at the very start. This review should include the app dependencies, the network configurations, and how to scale them. The details will be covered in our upcoming post, which is going to be about planning and tool selection required for a smooth transition

In the realm of cloud computing, the ability to monitor, analyze, and respond to IT environment anomalies is crucial for maintaining system integrity and compliance with regulatory standards. Azure Log Analytics Workspace (LAW) is a powerful service that enables businesses to aggregate, analyze, and act on telemetry data from various sources across their Azure and on-premises environments. This article delves into LAW, its alignment with SOC 2 compliance, and the practicalities of Azure Monitoring and diagnostic settings, offering insights from a recent project implemented for a software development company.

Azure Log Analytics Workspace (LAW): A unique environment within Azure Monitor that allows for the collection and aggregation of data from various sources. It provides tools for analysis, visualization, and the creation of alerts based on telemetry data.

SOC 2 Compliance: A framework for managing data based on five “trust service principles”—security, availability, processing integrity, confidentiality, and privacy. It is essential for businesses that handle sensitive information.

Azure Monitoring: A comprehensive solution that provides full-stack monitoring, from infrastructure to application-level telemetry, facilitating the detection, analysis, and resolution of operational issues.

Diagnostic Settings: Configurations within Azure that direct how telemetry data is collected, processed, and stored. It includes logs and metrics for auditing and monitoring purposes.

Why LAW should be used?

LAW plays a pivotal role in operational and security monitoring, offering several benefits:

Centralized Log Management: It consolidates logs from various sources, making it easier to manage and analyze data.

Compliance and Security: Helps organizations meet regulatory standards like SOC 2 by providing tools for continuous monitoring and alerting on security and compliance issues.

Operational Efficiency: Streamlines troubleshooting and operational monitoring, reducing the time to detect and resolve issues.

Cost-Effectiveness: Offers scalable solutions for log data ingestion and storage, providing flexibility and control over costs.

Configuration Process and Technical Details

Creating and Configuring Log Analytics Workspace

1. Azure Portal:

1. Navigate to the Azure portal.
2. Go to “All services” > “Log Analytics workspaces”.
3. Click “Add”, select your subscription, resource group, and specify the workspace name and region.
4. Review and create the workspace.

Same can be achieved using Powershell cmdlet New-AzOperationalInsightsWorkspace.

New-AzOperationalInsightsWorkspace -ResourceGroupName “YourResourceGroup” -Name “YourWorkspaceName” -Location “Region”

2. Enabling Diagnostic Settings

Azure Portal:

1. Navigate to the resource (e.g., a VM, database).
2. Select “Diagnostic settings” > “Add diagnostic setting”.
3. Choose the logs and metrics to send to the Log Analytics workspace.
4. Select the workspace created earlier and save the setting.

Azure CLI:

There is no corresponding powershell cmdlet however the same can be achieved using azure cli. It is advised that this step be done using the Azure portal unless it needs to be automated, In case of large number of targets consider using a bash script and an csv file for input

az monitor diagnostic-settings create –resource /subscriptions/YourSubscriptionId/resourceGroups/YourResourceGroup/providers/ResourceProvider/ResourceType/ResourceName –workspace /subscriptions/YourSubscriptionId/resourcegroups/YourResourceGroup/providers/microsoft.operationalinsights/workspaces/YourWorkspaceName –name “YourDiagnosticSettingName” –logs ‘[{“category”: “CategoryName”, “enabled”: true}]’ –metrics ‘[{“category”: “CategoryName”, “enabled”: true}]’

 Integrating Data Sources

To configure agents and services to send data to LAW:

1. Windows and Linux Servers:

Install the Log Analytics agent on each server.

During the agent configuration, specify the workspace ID and primary key to connect the agent to your workspace.

2. Azure Resources:

Many Azure services offer built-in integration with Log Analytics.

Use the Azure portal to enable integration by selecting the Log Analytics workspace as the target for logs and metrics.

3. Application Insights:

For application telemetry, integrate Application Insights with your application.

Configure the Application Insights SDK to send data to the Log Analytics workspace by setting the instrumentation key.

Insights on a case study from a Software Development Company Perspective

In a recent project for a software development company, LAW was leveraged to enhance operational visibility and ensure SOC 2 compliance. The focus was on automating log collection and analysis to proactively address system anomalies, secure sensitive data, and streamline the development lifecycle. By integrating LAW, the company achieved:

• Enhanced Security Posture: Through real-time monitoring and alerting capabilities.
• Operational Excellence: Improved system reliability and availability by quickly identifying and addressing issues.
• Compliance Assurance: Simplified compliance reporting and auditing processes, ensuring adherence to SOC 2 requirements.

Conclusion

Azure Log Analytics Workspace is an indispensable tool for organizations looking to enhance their monitoring capabilities and ensure compliance with standards like SOC 2. Its ability to aggregate and analyze data from a multitude of sources provides a comprehensive view of an organization’s IT environment, facilitating informed decision-making and operational efficiency. The integration of LAW, coupled with Azure Monitoring and diagnostic settings, offers a robust solution for maintaining system integrity, security, and compliance.

Mismo Systems implemented a 3-node Azure Stack HCI cluster for one of the clients. The cluster was configured with a dual-link full mesh storage network interconnect (Switchless).

This blog provides an overview of the Azure Stack HCI design, high-level implementation steps, network connectivity of the servers, IP configurations and cluster configuration.

Azure Stack HCI Design

Below is the high-level detail of the above Design diagram:

• 03 Nos. DELL EMC AX-740dx servers, installed with Azure Stack HCI 21H2 Operating System.
• Azure Stack HCI cluster will be created using the three servers.
• The cluster will be created and managed using a Windows Admin Center instance.
• The cluster will be registered with Azure.
• Azure storage account-based cloud witness will be used for the cluster.

High-Level Configuration Steps

Below are the high-level steps performed to complete the cluster configuration:

S. No.	Task
1	Server Racking and Cabling
2	iDRAC Configuration on the servers
3	BIOS Configuration for QLogic NIC configuration
4	Initial network configuration and domain join the servers
5	Azure Stack HCI cluster configuration: – Prerequisite check, feature installation and updates installation – Network and Virtual Switch configuration – Cluster validation and creation – Storage validation and Enable Storage Space Direct
6	Post cluster creation configuration
7	Cloud Witness Quorum configuration
8	Azure Stack HCI registration to Azure
9	Storage volumes creation
10	Virtual Machines creation

Network Interfaces

There were 3 Azure Stack Certified servers – DELL EMC AX-740dx, installed with Azure Stack HCI 21H2 Operating System. The servers had the following network interfaces:

Each of the servers has the following network interfaces:

• 1 iDRAC network port
• 2 QLogic FastLinQ 41262 Dual Port 10/25GbE SFP28 Adapter, PCIe Low Profile
• 1 Intel X710 Dual Port 10GbE SFP+
• 1 i350 Dual Port 1GbE, rNDC

Network Interface Connectivity

The diagram below describes the connectivity of network interfaces and their configuration.

Below tables provides low-level detail of the Azure Stack HCI Implementation:

Network Interface | Purpose	Node | IP Address | vSwitch | Team Configuration
Azure Stack HCI – Network Configuration
iDRAC | IP Addresses	IDRACNODE1 | 172.16.1.5 IDRACNODE2 | 172.16.1.6 IDRACNODE3 | 172.16.1.7
i350 Dual Port 1GbE, rNDC | Management Network	NODE1 | 172.16.1.60/24 | MgmtSwitch | SET Team <NIC 1 and NIC 2> NODE2 | 172.16.1.61/24 | MgmtSwitch | SET Team <NIC 1 and NIC 2> NODE3 | 172.16.1.62/24 | MgmtSwitch | SET Team <NIC 1 and NIC 2> Gateway – 172.16.1.1 Subnet – 255.255.255.0
Intel X710 Dual Port 10GbE SFP | VM Network	NODE1 – 10.170.3.111 | VMNetworkSwitch | SET Team <NIC 3 and NIC 4> NODE2 – 10.170.3.112 | VMNetworkSwitch | SET Team <NIC 3 and NIC 4> NODE3 – 10.170.3.113 | VMNetworkSwitch | SET Team <NIC 3 and NIC 4> Gateway: 10.170.3.1 Subnet – 255.255.255.0
QLogic FastLinQ 41262 Dual Port 10/25GbE SFP28 Adapter | Storage Network	NODE1 – NIC 5 | 192.168.12.1| Storage 1 <Node 1 – Node 2> NODE1 – NIC 6 | 192.168.13.1| Storage 2 <Node 1 – Node 3> NODE1 – NIC 7 | 192.168.21.2 | Storage 4 <Node 2 – Node 1> NODE1 – NIC 8 | 192.168.31.2 | Storage 5 <Node 3 – Node 1> NODE2 – NIC 5 | 192.168.12.2| Storage 1 <Node 1 – Node 2> NODE2 – NIC 6 | 192.168.23.1| Storage 3 <Node 2 – Node 3> NODE2 – NIC 7 | 192.168.21.1| Storage 4 <Node 2 – Node 1> NODE2 – NIC 8 | 192.168.32.2| Storage 6 <Node 3 – Node 2>   NODE3 – NIC 5 | 192.168.13.2| Storage 2 <Node 1 – Node 3> NODE3 – NIC 6 | 192.168.23.2| Storage 3 <Node 2 – Node 3> NODE3 – NIC 7 | 192.168.31.1| Storage 5 <Node 3 – Node 1> NODE3 – NIC 8 | 192.168.32.1| Storage 6 <Node 3 – Node 2>   Subnet – 255.255.0.0

Azure Stack HCI Cluster Detail

Configuration Item	Detail
Azure Stack HCI – Initial Configuration
Azure Stack HCI OS	21H2
Servers Hostname	NODE1.domain.com NODE2.domain.com NODE3.domain.com
Time zone	Central time (US & Canada) UTC -6:00
Joined AD DS Domain	domain.com
Windows Admin Center	https://wac01.domain.com/
Azure Stack HCI – Cluster Configuration
Cluster Type	Standard
Cluster Name and IP	Cluster01 | 172.16.1.63
Cluster Quorum Detail	Cloud Witness | Storage Account – <storageaccountname>
Azure Stack HCI – Registration to Azure
Azure Subscription Name and ID	<Azure Subscription Name and ID>
Resource Group	<Resource Group Name>
Azure Region for registration	West Europe

Chat with Teams will extend collaboration support by enabling Teams users to chat with team members outside their work network with a Teams personal account. Customers will be able to invite any Teams user to chat using an email address or phone number and remain within the security and compliance policies of their organization. 

Will rollout on the web, desktop, and mobile.

How this will affect your organization:

With this update Teams, users in your organization will be able to start a 1:1 or a group chat with Teams users who are using their personal accounts and vice-versa. IT Admins will have the option to enable/disable this at a tenant and individual user level with two possible controls:

1. Control to enable or disable the entire functionality. If disabled neither users in your organization and users in their personal accounts will be able to chat with each other.
2. Control to define if Teams users with a personal account can start a chat or add users from your organization to a chat. If disabled, only users in your organization will be able to start a chat or add users with their personal accounts.

Note: Settings will rollout default on.

What you need to do to prepare:

If you would like to opt-out from this functionality you would be able to do so via the Teams admin portal under the External Access section. Optionally you could use PowerShell commands to opt-out all users or individual users from this functionality. 

Settings to update:

Tenant level: CsTenantFederationConfiguration

• AllowTeamsConsumer
• AllowTeamsConsumerInbound

User level: CsExternalAccessPolicy

• EnableTeamsConsumerAccess
• EnableTeamsConsumerInbound

The cloud service providers AWS and Azure are truly miraculous helping millions across the globe creating a virtual space with a plethora of benefits. This article will delve deep into their pros and cons and look at the wide array of services, benefits and advantages they have. We will consider factors like: the cloud storage cost, the loss rate of data transfers, availability of data and so on.

AWS: It all began with the Amazon’s team recognizing the stagnation and complexity of their IT infrastructure. In order to improvise on their efficiency, Amazon’s team replaced the pre-existing infrastructure into well documented APIs. By the year 2003, Amazon had a moment of realization about their skills that is important for creating scalable and effective data centres. This is how Amazon Web Services came into existence. AWS is one of the leading providers of requirement basis cloud solution providing an IT infrastructure to companies of varying sizes. For companies that run on non-windows services, AWS works most efficiently for them and is a highly customisable podium. Netflix, Spotify, and such eminent companies use AWS.

AWSs’ services remained unparalleled as Google, their first competitor only came up beyond 2009 and Microsoft stepped up by 2010 as they did not believe in the potential of the cloud infrastructure. It is only after Amazon’s successful system that made Microsoft enter the world of cloud. Azure was launched by Microsoft, but their entry was not welcomed pleasantly as it faced several challenges. AWS had already become a giant as it had a lead of 7 years over Azure and provided ample scalable services.

It was about time that Microsoft stepped up and set its firm footing by adding support to various programming languages and operating systems. They got along with Linux and also made their services more scalable. With this redemption, Azure made its name to the top in the list of cloud providers.

Today AWS and Azure have become two prominent names when it comes to cloud service providers. They introduce the world with a virtual infrastructure with Azure holding about 29.4% of the workloads of installed applications, AWS holds a good 41.5% and Google only has about 3%.

There are a few differences between AWS and Azure, and both have their respective pros and cons. These two top players have their list of unequivocal set of advantages as they are great at what they provide.

Services:

Azure and AWS extends on premise data centre into firewall and cloud. VPC or Amazon Virtual Private Cloud helps users to create subnets, Private IP address range, network gateways and route tablets in the areas of networking services when compared to Microsoft Virtual Network which has similar services. When we talk of computing services Azure provides services like App Services, Azure Virtual Machine, Container services, Azure Functions while AWS provides: Elastic Beanstalk, ECS, AWS Lambda, EC2 and so on. Both these services are quite similar as well. While in the case of storage services, AWS provides temporary storage that is specified with the beginning of the instance and automatically dissolves with its termination. They also provide block storage that can either be attached or separated. Azure provides storage such as Blob, Disk Storage and Standard Archives.

Pricing:

Pricing of computing services depends upon the differences in configuration, the measurement of the computing units and the various range of services: storage, databases, computing and traffic.

AWS follows a pay as you go structure of pricing where there is an hourly charge while Azure charges per minute. An AWS m3. large instance is estimated at $0.133 per hour (21 CPU and 3.75 GB memory), somewhat similar pricing is followed by Microsoft in the Medium VM (2×1.6Ghz CPU, 3.5 GB RAM) that costs about $0.45 per hour. Azure can be deemed more expensive as compared to AWS regarding computing, but it provides for good discounts in case of long-term payments. AWS is also known for supporting the Hybrid cloud environment better. Meanwhile the security provided by AWS via user defined roles is unparalleled as it provides security by giving permissions on the entire account.

Open-Source Integration:

AWS employs tools such as Jenkins, GitHub, Docker and Ansible for their open-source integration as Amazon highly supports the Open-Source sect. Azure on the other hand provides native integration for windows development tools namely: Active Directory, SQL databases and VBS. On instances when Microsoft fails to support open source, Amazon is always open to it. Azure works great alongside NET developers and AWS with Linux Services.

Databases:

In order to save your information, a database is required and both our cloud service providers AWS and Azure relational database (SQL) or NoSQL. Microsoft provides their user with an SQL database while Amazon provides RDS (Relational Database Service) and Amazon DynamoDB. These databases provide automatic replication and are extremely efficient and durable.

Advantages of AWS certification:

AWS is the largest cloud computing service provider and has extra weightage to their certification as they have additional marketability because a large number of companies are using their services. AWS certification also gives you access to AWS certified LinkedIn and other certifications for professionals and developers. These include AWS Developer Associate, AWS SysOps, Cloud Architect Certification, gcp certification and so on.

The advantages of Azure Certification:

Azure also renamed as Microsoft Azure in the year 2014 provide additional benefits to those who are aware of their in-house data platforms. 55% of major Fortune 500 companies go for the services provided by Azure, and hence its certification opens a career opportunity for the candidates in these companies. It has been estimated that around 365,000 companies opt Azure every year which creates demand for Azure professionals. Their certification include Architect Microsoft Azure, Developing Microsoft Azure, Cloud Solution Architect, Cloud Architect, Implementing Microsoft Azure and so on.

Azure and AWS: Making the world a better place

Both AWS and Azure have made huge contributions trying to make this globe a better place to be in. AWS is used to scale flood alerts in Cambodia saving millions of lives and is cost effective. Other risky zones now replicate this technology to detect calamities beforehand.

NASA with the use of AWS platform has created a virtual Storehouse of videos, pictures and audio files that can be accessed easily in one centralized space.

The Weka Smart Fridge that has been created using the Azure IoT suite, helps store vaccines helping medical support to make vaccinations available to people easily.

Both AWS and Azure are reliable sources making lives easy for people around the globe.

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Microsoft’s Azure Stack HCI is a hyper-converged infrastructure with virtualization, software-defined networking, and more. What separates it from the rest is it seamlessly integrates with Microsoft Azure. It’s never been easier to unify your on-premises infrastructure with the power of Azure.

We have listed below a few points for why you need this new & exciting hybrid solution for your business:-

Azure Hybrid by design

Extend your datacentre to the cloud and manage Azure Stack HCI hosts, virtual machines (VMs) and Azure resources side by side in the Azure portal. Make your infrastructure hybrid by seamlessly connecting it to Azure services such as Azure Monitor, Azure Backup, Azure Security Centre, Azure Site Recovery etc.

Enterprise-scale and great price-performance

Get infrastructure modernisation, consolidate virtualised workloads, and gain cloud efficiencies on-premises. Take advantage of software-defined compute, storage, and networking on a broad range of form factors and brands. With the new feature update, get powerful host protection with a Secured-core server, thin provisioning and intent-driven networking. Optimize your costs based on your needs with a flexible per-core subscription.

Familiar management and operations

Simplify your operations by using an easy-to-manage HCI solution that integrates with your environment and popular third-party solutions. Use Windows Admin Centre with a built-in deployment GUI to leverage your existing Windows Server and Hyper-V skills to build your hyper-converged infrastructure. Automate completely scriptable management tasks using the popular cross-platform Windows PowerShell framework.

Deployment flexibility

Select the deployment scenario that is best for your environment, such as an appliance-like experience, a validated node solution from one of more than 20 hardware partners or repurposed hardware. Choose optimized solutions that are available on a broad portfolio of x86 servers and hardware add-ons. Manage your solution using Azure or familiar management tools and choose from a wide selection of utility software options within the enhanced ISV partner ecosystem.

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