Azure Stack Reviews

My role has been primarily as an architect, utilizing Azure Stack for building data lakes, data warehouses, real-time solutions, and incorporating a little bit of AI for tasks like image recognition and voice-to-text. I am transitioning towards using AI and ML with technologies like ChatGPT.

The scalability of Azure Stack is commendable. It can scale up within two to three minutes upon request. SQL Server is a good use case for data warehouses. It is scalable and can be set to serverless to reduce costs when not in use. 

Power BI Fabric offers a useful way to stack data in data warehousing solutions. Azure Stack provides federated authentication which is seamless with on-premise infrastructure, offering good performance and throughput. The integration with Microsoft's environment is also efficient.

My primary use case for this solution is for cybersecurity and device management.

I find Cloud App Security and activity alerts particularly valuable. They allow me to track all user activities. Everything is stored under one roof, so there is no need for different licenses for different components.

Azure Stack is a well-suited solution, especially for small to medium-sized businesses. While its scalability for large corporations is still untested, it is an excellent option for smaller enterprises looking to leverage the entire Azure platform. Azure offers a wide range of services, catering to various needs. For those interested in no-code or low-code automation, Microsoft provides the Power Platform, which includes tools like Power Apps for automation, Power Pivot, and more. These are ideal for teams or departments looking to streamline processes. If someone wants to use Azure as an enterprise data platform, they offer good solutions like Azure Data Lake, specifically using ADX Gen 2 for enterprise storage, which is commonly used. It pairs well with Databricks, especially when handling performance-intensive tasks, as Azure Synapse can be expensive for certain scenarios, such as data warehousing. For typical ETL tasks using Azure Data Factory and Azure Data Storage, the costs are quite reasonable.

I believe that Azure's Delta Lake features align with the Azure Lake House architecture, similar to the traditional Medallion architecture, which comprises bronze, silver, and gold layers. This structure is valuable for effective data warehouse management. You can store your raw data in the bronze layer, standardized data in the silver layer, and customized data in the gold layer. Tools like Informatica and Data Factory, which serve as ETL (Extract, Transform, Load) solutions, do a commendable job of connecting to diverse data sources, processing them, and loading them into Azure Data Lake Storage Gen2 (ADLS Gen2). Data Factory, in particular, seamlessly integrates with ADLS Gen2, making it a powerful combination for data processing. It offers the option to leverage Azure Databricks on top of this setup. Databricks provides the capability to work with data tables and allows for custom program development using Python, Spark, or SQL. Combining Azure's solutions with Databricks can lead to massive scalability and efficient processing of big data tasks.

We have a product that helps with billing on Azure Stack. Our solution makes it easier for users to manage internal chargebacks or for resellers to provide a billing system for Azure Stack.

We have our own tool that supports Azure Stack as a platform. We've been supporting it for almost four years now. We are an ISP partner for Microsoft.

Our solution is industry-agnostic, as Azure Stack itself has broad applicability.

Our billing support is helping a lot of customers. We've been able to consolidate billing onto a single platform, our Cloud platform. This is supporting many clients, as Azure doesn't have a native billing component. We provide that, and we can handle billing for both Azure Stack and Azure within a single platform. That's our product.

For example, if a reseller has deployed Azure Stack for a customer, we provide the billing functionality. Our tool can manage billing for both Azure Stack and Azure through a single platform. We're actually multi-cloud; our consolidated platform supports AWS, Azure, Azure Stack, GCP, OCI, and more – all the billing can be done through one interface.

We're quite unique. We haven't seen any competitors offering what we do right now – multi-cloud billing with extensive on-premises support. We're in the process of adding VMware as well. With this, we become a truly complete billing platform, also offering optimizations and other features both on-premises and in the cloud. That's what we do, and why we work with multiple clouds.

For hybrid cloud operations, the seamless transition it enables is key. Most enterprises have a lot of workloads on-premises, and they want a smooth way to move into the cloud. 

Some things are on-prem, and some things are on the cloud.  Azure Stack bridges that gap with solutions that work well for their existing on-premises infrastructure.

Our businesses have diverse scopes, leading to a wide range of requirements that we use this solution for. These encompass everything from the initial onboarding process in an on-premises environment to aspects like security, analytics, and integration.

The two most crucial aspects of it revolve around connectivity and security. In terms of connectivity, it's vital to ensure seamless external connections, bridging on-premises and cloud environments through features such as site-to-site VPNs and Access Routes. On the other hand, we're prioritizing security, especially with the use of Azure Firewall, including its premium features, as well as SQM.

Our company provides local cloud services with multiple platforms and Azure Stack is one of our platforms.

The most valuable thing about Azure Stack Hub is that it provides the same control panel for managing cloud services and provides the same experience we have with global Azure, for the supported services.

We use Azure Stack for repository management, storage, and services such as speech recognition, text-to-speech, and speech-to-text. These services are essential for the development lifecycle. You can check the relevant information on GitHub for application storage and CI/CD pipeline releases.

Face recognition for security in web applications can use AI services for processing. To store data, you can use SQL Azure, which involves exposing an API to access information from the face recognition service. You need to connect with Azure SQL Database and handle any issues related to JNC charts or time functions.

For example, you might photograph a person in front of a PC or laptop. This image can be processed using Azure face recognition services. The system compares the image against a repository in SQL Azure. Access can be granted to the application if there is a match with the stored data. If there's no match, the system will notify that the person is not an employee.

We use the solution to run our ERP software and mail servers.

The product has impacted our edge computing needs in a good way.