Agile Metrics

Agile Metrics are very important part of an agile software development process. These metrics help software teams to supervise productivity in workflow stages, check, and control software quality. It brings clarity to the development process.

An agile theory is to deliver a fully functional product. Therefore, we need to measure aspects relating to the product quality and timely delivery. Agile Metrics confirms whether you are drawing benefits from the current development process. Are you able to meet the dead lines and maintain the budgets? The development teams and management can measure development quality, the overall productivity, and accurately predict the influence on customers.

Types of Agile Metrics:

1. Lean metrics: This ensures the purposeful activities that create value for customers and business organizations by elimination of improvident activities. It commonly includes the metrics for lead-time and cycle-time.
2. Kanban metrics: It allows us to focus on workflow, properly organize, and prioritize work to get it done. Generally, used metric is a cumulative flow.
3. Scrum metrics: We can predict the deliverables in terms of working software to the customers. One of the common metrics is the team velocity and the burndown chart.

We need Agile Metrics to match the ever-changing requirements of customers. Along with the product development, a simultaneous study using Agile Metrics is helpful. It enables to plan before hand and deliver quality software before time.

Scrum Metrics:

This agile methodology is for managing development projects. The estimation of work and smaller sprints of few weeks enables faster delivery of tested features. Sprint planning allows detailing of tasks for particular feature. We can check to what level completion is possible on weekly basis. Discussions scheduled on daily basis helps the developers to share the obstacles. You can find solutions for the identified issues at early stage. At the end of each sprint, the contributors have gained new insights; they can share to improve the development process.

Agile Metrics effectiveness depends on the planning. It considers sprint frequency, estimated cycle time to implement the idea and release dates. The commitment to deliver the best software with zero defects is no more a dream. Make it possible with powerful agile environment and innovative vision.

Accurate results and precise interpretations are possible when the teams use agile metrics. Define metrics from initialization, blockades, and in relation with the development process. You can have combinations with other metrics. Selecting agile metrics should not be due to its simplicity or complexity. Decision can be on the targets we have defined and the insights we receive. We can measure absolutely anything but need to make up your mind on what is useful. Focus on improving the quality of development, service, and customer satisfaction.

One way of looking at agile metrics is what your goals are. Are there any probable obstacles to achieve them in definite period?

Benefits of Agile Metrics to the business:

1. Frequent delivery of software
2. Continuous observation and improvement
3. Team work of development team and management team
4. Measurable actions and insights
5. Appropriate process to release pressure and fine-tune development
6. Meeting customer and stake holder’s expectations
7. Motivational factor for work atmosphere
8. Define milestones and remove lags
9. Maintain pace, security and readability
10. Create sustainable software with technical excellence

Powerful Agile Metrics for improved release, pre-release and post-release activities:

Sprint Burndown: Focus on how much work is remaining in the sprint. This chart can visualize number of story points completed during the sprint and those remaining. It helps to forecast whether the scope of sprint scope is achievable or requires re-scheduling. It immediate actionable items it gives with clear status of what the sprint has delivered so far. Reliability it brings in development is exceptional with the representation of hours remaining to achieve in particular sprint. It helps in dealing with newly added requirements even after the project definition. This agile metrics can ensure that the productivity of your team remains high.

• Agile Team Velocity: Velocity measures story points completed by a team on an average in past few sprints. Estimating time required to accomplish the remaining in upcoming sprints is simpler. This result metric calculates the value delivered to the customers in cycle of sprints. Comparison across teams is not what you can expect from velocity as the story points and its definition is different.
• Escaped Defects and Defect Density: We can track defects during iteration and production. This metric is crucial, as it shows number of bugs experienced by users in productions. The escaped defects should be ideally zero as it shows quality of software. Defect density measures the number of defects against the software size, say length of code. The scrum teams need to implement quality agile practices such as test driven development. Continuous integration can add to effectiveness of teams and help deliver faster to meet the market demands. Agile techniques in combination with scrum framework can minimize the defects. We can track the defects and refine the process to prevent the recurring issues and improve the project processes.
• Lead Time: It lets you measure the total time taken from story enters the system until the sprint is completed, or released to the customers. Lead time enables you trace the value of your software for the business. The reduced lead-time suggests efficient development in project.
• Code Coverage: This metric measures code coverage that shows the percentage of your code tested. Code coverage is measured using number of methods like statements, branches, or conditions executed in test suite. It projects basic picture of how much codebase has been tested. A low code coverage points towards low code quality but at the same time the high code, coverage does not indicate high quality. You can even run code coverage automatically as integral part of every build. The relation of coding with the UI or integration are not counted thus it presents crude visualisation. Code coverage provides concrete view on progress of your project.
• Failed Deployments: This metric measures the number of deployments in test and production environments. The reliability of environment helps to judge the quality of software created by the teams. Clarity it brings by indicating that the release is ready for production can save from failure in deployments.
• Time to Market: The time taken by projects to start providing value to the customers and generate revenues for business is time to market. Its calculation is on basis of length and number of sprints before release to production. Value for business is relative term as the ability to use a product is dependent on various factors. Hence, the measuring strategy should change accordingly.The time to market is the number of days from start of development until release. It is irrespective of feature release at the end of every sprint or after multiple sprints. Even the delay in internal processes or testing is part of the time of release. This metric helps companies to be familiar with the ongoing value of scrum projects. Budgeting for projects also considers time to market.
• ROI: Return on Investment from a scrum project calculates total revenue generated from a product as compared to the cost of sprints required for development. We can generate ROI faster by adopting Scrum instead of the traditional development methods. Every sprint creates more features and they reach the customers early. Faster release thus translates in revenue growth. It inverses the costs of projects and enables immediate income generation as soon as the sprint ends.
• Capital Redeployment: It measures if it is beneficial to continue a scrum project. It indicates whether you are drawing value out of it. You can decide if the redeployment of teams can make projects profitable. Capital redeployment confirmation depends on the actual cost, project backlog, number of sprints required to complete the tasks, and available alternative projects. Determining the cost of development can increase the ability to generate revenue. Budgets and opportunities are limited thus; allocation of funds and moving them to different projects can be beneficial.
• Satisfaction surveys: Scrum team’s highest priority is to satisfy the users of their products. The satisfaction of teams can boost morale, create good work environment, and launch surveys. Survey can have different perspectives like the happiness of employee to work with project type and people leading those projects. Surveys can bring enthusiasm in the team and make them share their thoughts through specific questions, which they would have not shared in face-to-face discussions. Scrum team turnover if low suggests healthy team environment. While high scrum team turnover countenance in companies is due to many factors. It can be inefficiencies of development teams, product owners, and their inability to remove the hindrances within projects.A customer satisfaction survey shows the satisfaction level, customer experience with the project as well as the scrum team. One of the well-known metrics used to measure customer satisfaction is the Net Promoter Score.Release Net Promoter Score is ultimate test of agile development, to find whether it is providing value to customers. After release of new software, will the users recommend it to others? It is an important measure of customer satisfaction as it indicates successful software made by the development team.

Involving the teams in using this metrics can change the ROI ratios. They get to know how it is for betterment of teams and organizations. When the scrum team knows about advantages of metrics implementation, the members are likely to follow the process. They can take help of those metrics in product and process improvement.

The Missing Scrum Metrics are the ones that we are able to identify with experience. Mainly the quality issues found after software release in production. Metrics cannot address all quality issues. You can try other automated processes like static code analysis that provides insights into code quality. It can auto clean and correct minor level errors of coding.

The missing data that you find manually without help of agile metrics can also help in testing the product. You may want to figure out minimum resources to make the project work or the ratio between the managers and contributors on a project.

Finding right balance can reduce dependencies of teams.

Dependencies of teams in Agile:

Inter-team dependencies will be different in each project but it can be limited by taking appropriate actions. Depending what you want to achieve, these dependencies can reduce by restructuring products, changing architecture or adapting newer technologies.

Even teams built to handle the project cannot be just the frontend and backend teams. Identify resources for planning, budgeting, visualization, of release. The preplanning sessions are needed for each sprint and synchronize the inter-team dependencies. Companies can build the agile teams and assign roles to them.

Skill versatility of the strong scrum teams increases the ability to produce high quality software at higher pace. If the individual skills and level are tracked, and matched with the organization’s skill level and gauge the growth.

Project attrition rate is not same as capital redeployment. Thus, the project’s that have pre-closures or extend beyond expectation requires scrutiny. Project attrition reasons are lack of proper planning, unorganized priorities, unaddressed impediments, or operational issues.

Conclusion:

Being on right track and continuous improvement can satisfy the contributing teams and stakeholders. Agile metrics thus converts to reliable software developed by organizations. The business value it carries supports the team, leaders, and owners to maintain transparency.

Agile Metrics is an ongoing help that is available to throw light on each stage of production. Charting and escalating remaining work to finish the release cycle or what value adding did the program do. Just as it shows the estimation accuracy, it also shows the team’s efficiency to deal with complexities and the growth during the process.
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Container Management – Top 7 Container Management Solutions

Container Management Software are product based solutions. They simplify the administration of large number of containers on IT infrastructure. Automate and create software, deploy it or use CMS for scaling containers.

Container Management Solutions are preferred as they provide operating system virtualization. Container approach eradicates the use of hypervisors; the virtual containers provide alternatives to hypervisor-based virtualization.

Containers enclose the applications and everything that is required to run them. They are isolated units on the operating systems consisting of the runtime environment. It offers application, its configuration files, libraries, and its dependencies everything to be in single location. Containers share the resources of a particular operating system.

Advantages of Container Management Solutions:

• Automating the rollouts and rollbacks
• Containers are the platform for cloud-native to applications
• Manage containers in production
• Monitor system health
• Scaling and flexibility of applications
• Management competence
• Ability to integrate containers with existing hardware and software
• Working across many different environments
• Easy to share containers
• Cluster managers provides load balancing
• Divide the applications into containers, container cluster or domains
• Downloads and runs instantly due to small size of container packages
• Ensures uptime during rollout
• Expansion of container technology offers choices to store old versions & deploy new images.
• Makes debugging easy
• Spontaneous testing speeds the operations
• Operating system shared by all containers are light weight compared to the traditional virtual machines
• Host more containers than VMs
• Improves both resource usage and operations of data centres
• Containers lets you obtain the copy of a system that you want to deploy
• Ideal for microservice application development
• Containers can be duplicated at high pace
• Platform specific security and governance

Limitations of Container Management Solutions:

• Investments and costs of containers can be on higher side
• Non –Interoperable Windows and Linux containers
• Not suitable to take work load for Monolithic applications
• Full utilization of containers requires expertise of software team
• Portability amid servers is challenged by dependencies on containers
• Containers hugely consume computing resources without recognizing due to duplication
• Finding good container developers is difficult
• Substandard container architectures can cost projects
• Huge numbers of containers are too complex for IT team to control
• Developers need to remain updated with skills due to continuous automation

Points to consider while selecting container management solutions:

• Reliability of container management solution
• Persistent storage management
• Application-centric container management eases deployment, monitoring, scaling, updating
• Basic container platform its orchestration capabilities and public cloud tools
• Easy integration of containers with the existing technologies
• Good Configuration and security container software
• Transition to Microservices should be smooth in order to save you from major release
• Free selection for deployment of application in data centre or public cloud
• Match the ability of current infrastructure, and application operators to manage container environments
• Manage complexities of resource abstraction and orchestration
• Upgrade of containerized applications without infrastructure imposed restrictions

Container Management Solutions

They consist of open source and commercial products.

Top 7 Container Management Solutions

1. AWS Elastic Container Service (ECS):

Amazon EC2 Container Service -ECS is a container management service that supports Docker containers and Fargate technology both. The highly scalable platform allows users to install and operate their own container orchestration software. It manages the cluster through virtual machines eliminating the need to install and manage separate container orchestration software. Easily manage and scale a cluster of VMs, or even schedule containers on virtual machines. Users can run applications on a managed cluster of Amazon EC2 instances.

It provides clear documentation, is more scalable & flexible. It is easy to configure and deploy. Scaling happens in just a few clicks, it saves time and cost of users. The type of machines they have suit requirements of various organizations. It also saves from the server downtime or concerns of project size to match the growth. Auto recovery of container, compatibility with windows containers, simplified development helps developers. The task definition, integrated elastic load balancing, and availability of containers are few great features of AWS.

Rebooting time is concern for small instances. Moving instances to other regions is difficult.

2. Azure Kubernetes Service (AKS):

Azure Kubernetes Service -AKS offers reliable tool to use and orchestrate containers. It scales infrastructure and applications with dynamism.

The AKS provisions clusters using the Azure portal and Azure CLI. Its feature provides visibility over the health of container. Its main attraction is auto upgrades and curative nature. Azure automates and simplifies the processes to compliment container management.

It has ability to view all aspects of program thus is user friendly and up -to-date. Integration with Docker is appreciable. It is very satisfying experience for developers and the IT teams. Simple tools help to implement required business restrictions in the environment. Its innovative command line interface makes working easy.

After setup, you cannot change the node pool and its type; similar is the case with the VM and disk size before you create the cluster. Manual changes to the VM node can hamper the upgrading of systems. Response time in cloud application is somewhat slower. In case of smaller deployments, reusing the server for database and frontend equally is not possible. Too many animations on the portal make the web browser slothful.

3. Docker:

Docker is a leading container platform that made containers easier and safer to deploy. Containerization with docker saves energy of maintaining virtual machines. It allows deploying to the cloud easily and scaling up. This open source platform introduced the concept of containers and containerization. This technology is available for Windows and Linux both.

Standardized container system reduces complexity of operating system. Fast integration with deployable application and compilation makes it dependable. User can package applications and all its dependencies in a virtual container to run it on a Linux server.

Developers can easily run software inside dedicated container that is free of configuration. It is suitable for large microservice system deployment. In case of large microservices some additional orchestration solutions such as DC/OS or Docker Cloud is required.

Docker guarantees scalability, reliability, and secures runtime environment for daily workflows. Do not install a program to run them on Docker thus saves valuable space on computing cluster. Thousands of pre-burn images can run any software in no time. The big size Windows images compared to Linux limits its running on containers.

Tougher for those who do not have knowledge on how to use Linux bases environments. The documentation is easy to understand thus helps developers to learn faster. It has advance features, which simplify management.

Docker provides an uninterrupted end-to-end experience for development and scaling of distributed applications. It is the fastest way to use containers and kubernetes. This great deployment tool requires minimum setup and is a lightweight environment. It supports most of the popular operating systems. The compose feature of Docker considers a large number of containers and environments using a single configuration file.

4. Google Kubernetes Engine (GKE):

Google Kubernetes Engine provides advance level of flexibility to organizations using containers and microservices. It deploys, manages, and scales containerized applications, powered by Kubernetes. It is a production ready infrastructure useful in implementation of containerized applications. There remains no need to install, handle, and operate clusters.

The open-source container orchestration platform is a powerful tool to automate, deploy, and manage components. Kubernetes can run on a private cloud such as Microsoft’s Azure Container service, Amazon AWS, and Google Cloud Engine. It incorporates numerous cloud platforms to works with various container tools like Docker.

Kubernetes is comparatively portable with the features such as inbuilt load balancing, easy to use web interface, auto-scaling, auto-upgrading, and docker image support. It updates production code seamlessly, and is a secure container management solution.

Effortless setup, easy configurations on Google cloud or on-premise and least time consuming deployment of clusters are of great advantage to users. Features such as identity access and container optimized operating systems are a great deal for developers.

Kubernetes API allows scheduling of pods, inside of which the containers reside.

Limitations that exist are difficulty in understanding of tool, difficulty in detecting errors, setting up cluster manually and deploying the automated fix.

5. Hyper-V Containers:

Hyper-V Containers are entirely isolated virtual machines. It includes independent copy of Windows kernel, which has memory assigned directly to them as basic requirement of strong isolation. It runs non-trusted and multi-tenant applications on the same host. It is lightweight configuration platform, an alternative to the traditional virtual machines. It makes nested virtualization easy to handle. Manage Hyper-V Containers with Docker or via new Windows PowerShell cmdlets.

Administer the containers using the Docker CLI -command-line interface. These commands are same as used to run Docker containers on Linux.

These Hyper-V Containers are interchangeable with Windows Server Containers for applications pushed or pulled from Docker. Use Microsoft Azure to build nested virtualization in Hyper-V. It supports running Hyper-V Containers on Virtual Machines. The isolation needed for the workload is available without being concerned about managing physical machines.

Hyper-V based on Windows forgives your client from learning Linux to use it. Server virtualization enables to run multiple servers on a computer resulting efficient use of resources.

Hyper-V can save finances in the long term on the maintenance costs and additional licenses. Microsoft provides better support for its server applications if run directly on Hyper-V than third-party virtualization platforms.

6. AppFormix:

AppFormix is for operations management and optimization on public, private and hybrid cloud. This product has lot of easy to use features. The software allows users to visualize and analyze the physical and virtual environments. It provides end-to-end visibility of your multicloud environment. Through this, the companies can avoid potential issues. It also simplifies operations to increase productivity.

Its ability to manage automated operations that brings visibility and its real time reporting helps the users. Its consistent running and optimal utilization attracts developers. AppFormix consists of network device that has tracking function. It provides real-time infrastructure performance and monitors data centre networking devices.

This smart-monitoring tool helps in identifying issues and automatically equips you for corrective actions. It brings in large integration possibilities with various cloud service providers. It tracks and analyzes programs operating on public clouds such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. It provides actionable insights from immediate actions.

This comprehensive container management system is easy to learn, install, and use. Containers and container cluster management have monitors helping to automate applications. AppFormix proffers REST APIs to configure and integrate with other systems.

7. Diamanti D10:

Diamanti D10 is bare-metal container platform. It offers a combined solution that hosts and runs containerized applications. Easy integration with Docker and Kubernetes enables easy migration of data and applications. Plug & play networking with existing VLAN and DNS infrastructure.

Deployment in minutes and testing is easy and administered process. Simple commands create clusters, networks and configure storage. The built-in templates empower to manage all tasks.

Developers can interact with the cluster; using CLI to create volumes and networks or add and delete nodes. Diamanti provides real-time service levels and enables high utilization of resources. It has compatibility with Docker and Kubernetes.

Continual storage on VM and connectivity between nodes brings high performance accompanied with low latency. Creating clusters, networks, and volumes is simpler with its storage capacity. Multiple pods can share same back-end drives. Role-based access control and Active Directory (AD) useful for authentication, helps managing containers. It provides simplicity, efficiency, and control to developers.

Selecting a container management solution is dependent on the needs of business enterprise. It should assist the teams performing to achieve for the organization. The Top 10 container management solutions can torchlight your search for perfect solution of container technology that bears workload.

Container Management Solutions establishes standardization, flexibility, velocity, and security to these applications. The capital investment, complexity of arranging containers and finding right development skills is challenging.

Most medium and small enterprises benefit from a stable, scalable container if the strategy and approach is right.

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Introduction to SQL Server 2019

Developers Software engineers and IT leaders have been enthused with the SQL Server 2019. A database management system requires SQL to handle the structured data and organize the data elements. Popularity of SQL has not fallen from 2012 and is on top even today, when checked against the DB- Engine ratings. Surveys have proved that almost 58% developers prefer SQL Server to other available database platforms. In Nov 2012, the DB-Engine Ranking of Relational DBMS was 1356.836 and in Aug 2019 Microsoft SQL Server ranks on 1093.179.

The companies prefer SQL for the ample of reliable functionalities available in budget. This stable database management engine is available on both the platforms like Linux and Windows. Microsoft products and SQL Server 2019 work peacefully, have gained new energy with inclusion of AI.

SQL Server also has some great features including visualisations on mobiles and the ability to change and track performance levels, which can potentially save time and money.

What are the reasons to choose SQL Server 2019?

• Big Data: SQL Server store the big data clusters.
• Artificial Intelligence: Bring AI for better operations and reduced workloads.
• Data Virtualization: It runs queries across the relational & non-relational data without replicating or moving it.
• Interact with Virtual Data: Explore and interact with the virtual data to analyse for better insights.
• Real-time Analytics: The operational data is concurrent and lasting memory enables analysing of data in real-time.
• Resolve Performance Issues: Automatic correction works because of the improved query processing ability of SQL Server 2019.
• Database Maintenance Costs: Reduced costs and increased uptime due to more online indexing.
• Data Protection: Encryption secures the SQL server, boosts the security of data. Security of data on cloud is vital due to DBAs facing cybersecurity issues with the growth in online storage.
• Data Discovery & Labelling: The assessment tool can track the compliances using powerful resources of SQL server.
• Flexibility: Run the Java code on the SQL Server to analyze the graphical data on Windows and Linux servers.

How useful it is for data visualization?

SQL Server 2019 and data virtualization eliminates the need to use the alternative ETL. This extract transform load with the copies of data loaded to the systems, process, and analyses data. ETL may bring value to business with its analysis but has several issues like high development and maintenance costs. It also requires extensive efforts to create and update the process of ETL. The delay it causes in processing can range between 2-7 days; this hampers the accuracy of analysis. You may lose business opportunities due to this delay. The storage space required for each data set and the security concerns affects the budget.

Alternatively, data virtualization allows integration of data from varied sources, saved in different locations and design formats. It does not require moving or replicating of data. The single virtual layer created with this data provides cohesive support to multiple users of different applications. User’s data access to sensitive information, if dynamically and centrally controlled can avoid the threat and loss that delay causes.

SQL Server 2019 identifies and addresses the need to store the variety of data, which is mixture of relational and non-relational data. Maintain the relevance of data, store and use from separate locations yet combine it to analyse on a single platform.

How flexible is SQL Server 2019?

The capability it brings to performance of business applications is mainly because of its transaction processing speed. SQL Server possesses TPC- E3 performance benchmark for On-Line Transaction Processing –OLTP that considers the transaction per second rate. TPC- E3 is highly effective in financial transactions at brokerage firm but can be applicable for other online business trades.

With owned TPC-H4 performance benchmark it serves effectively for data warehousing. It principally multiplies the capacity to run and populate data even for the ad-hoc queries of businesses. It can examine voluminous data, execute complex queries, and sustain concurrent data mutation. The reliability it brings to business decisions is astonishing. TPC-H is the performance metric for Composite Query-per-Hour. This includes the query processing when submitted by multiple users simultaneously.

Security with SQL Server 2019 is consistent, thus enhances the capacity of data management and applications that are data-driven. As per the National Institute of Standards and Technology, the proof of the high security is the low concerns raised, on security attacks by the database vendors in the span of 7-8 years.

Enhancements/ Additional Features in SQL Server 2019:

• SQL Server 2019 extended competence of PolyBase, with new connectors allows us to create external tables that can link to a variety of data stores such as SQL Server, Oracle, MongoDB, etc using ODBC driver.
• We can choose the language and platform to include additional container scenarios to embrace higher extensibility.
• Java language extension will allow you to call a pre-compiled program and securely execute this Java code on the SQL Server. You can stipulate the Java runtime by installing the JDK distribution and selected Java version to meet your requirement.
• Support to Persistent Memory SQL, now directly accesses & picks the files stored on PMEM devices circumventing the operating system.
• SQL Server 2019 provides better integration with big data systems and connectors for data visualization. It leads to right use of data lying scattered on the business systems.
• Columnstore Index features advances to deliver improved metadata memory management, low-memory load alley for columnstore tables, bulk loading, and maintenance of columnstore.
• Built-in Machine Learning and Artificial Intelligence supports additional scenarios like failover cluster instance. It maintains the zero downtime and consistent availability of applications and services.
• SQL Server 2019 has intelligent query processing, added security, and features that support the GDPR compliance. The data protection and data privacy makes business applications safe and secure.
• The memory grant feedback again calculates the memory required for the repeating workload in case of the query repetition. This memory grant feedback is available for match and row mode processing.
• The lightweight query-profiling infrastructure helps in trouble shooting and diagnostics by reducing the overhead of CPU to just 2%.
• SQL data discovery and classification integrated to the SQL server engine with new metadata meets the compliance needs of databases.
• Assessment of vulnerability on SQL server and Azure database instances has a standard security procedure introduced for best practice.
• Newly introduced enclave technology secures data and client applications on server side from malware by encryption.
• Configure one primary and four secondary replicas, in total five synchronous replicas in AG (availability groups). It is effective due to programmed failover between these replicas. The client applications can connect with any replicas of the availability groups.
• UTF-8 character encoding support allows us to create char or varchar column to store data and improve data performance and harmony.
• You can expect support from SQL Server 2019 for the resumable online index creation just as in SQL server 2017.
• It brings better scalability and balance as it automatically redirects connections on basis of read/write intent.
• Use SQL Server 2019 to configure Always on Availability Groups using the Kubernetes as orchestration layer.
• SQL Server Configuration Manager, this functionality makes certificate management efficient. We can view the valid certificates installed on the SQL server instance and know about their expiry dates. It allows us to deploy certificates for the Availability Group instances, instigating from primary replica.
• SQL Graph enriched with inclusion of restrictions on connections with nodes adds to security. Now add, update or delete operations on target table and merge your existing graph data.
• The Power BI apps for mobile devices (Windows 10, iOS, and Android) allow easy access to the dashboard and the mobile reports in online /offline mode.

Conclusion:

There are multiple database combinations but most of them include MS SQL.

Thus, MS SQL Server 2019 allows you to add more data sources and volumes of data. Reliable help from SQL will encourage developers to build intelligent enterprise applications. Exploration of data in real-time using interactive analysis enables immediate issue tracking and solution. New features address many challenges powerfully than the earlier versions. Simply explore the features that make applications smarter and easy to access.

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Compare Azure Cloud Services to Service Fabric -When to go for migration?

 Compare Azure Cloud Services to Service Fabric -When to go for migration?

While building a native Microsoft Azure application, there is always a dilemma of choosing architecture models. The two main prevalent architecture models are Azure Cloud Services and Azure Service Fabric. Out of these two that you can leverage, there is always a question mark of making a choice and when they should be used. To remove this quandary it is important to know their complete architecture and compare the two major azure architecture models: Azure cloud services vs Azure service fabric. This will allow you to get a clear picture and make the task of selection much easier, as there are so many Azure cloud contributions in the platter from Microsoft. Case in point, it looks like that every single month they turn up with fresh services and add them to their load of offerings. For this very reason, it is essential to have a perfect understanding of these services.

Aiming mainly on architectural and design differences between Cloud Services and Service Fabric, let us make an attempt to describe these cloud architectural models in simple terms. This will help in the proper and a clear understanding of these models and what to choose from Azure for your following enterprise venture! First and foremost, let us explain these two entities so that there is a proper description of these architectural models, and then we will evaluate both these models.

Visions and a general idea of Azure Service Fabric

Azure Service Fabric is a scattered systems architecture model. It allows a platform to implement Micro Services architecture in Microsoft Azure. This huge scale platform makes some of the infrastructure trials in Microsoft Azure Cloud Services easier. It signifies the following generation middleware platform for constructing enterprise cloud-scale applications. In this model, applications run on a pool of Virtual Machines, known as Cluster.  Diverse parts of the cloud applications can be scaled individually, and the software developers have more agility to provide the way out. Also, dissimilar programming models are available for Azure Service Fabric. Azure Service Fabric is comparatively a novel offering from Microsoft, but it is been used internally in Microsoft cloud for application development and cloud offerings like Microsoft Azure SQL Server, Skype and other services. With so many features to offer, it is a trustworthy and fast service to provide enterprise scale applications to prospective clienteles. What is more, Service Fabric is responsible for hosting containers providing a platform on top. At this time it backs windows server containers and Docker containers on Linux. As a result, it offers you the elasticity to set up your codes as a container in Azure Service Fabric and track them beside built-in Azure Service Fabric methods in lone cluster! It is a next-generation cloud application platform meant for very accessible, extremely trustworthy distributed applications. It keeps on offering a lot of fresh features. In turn, all these fresh features, introduced every now and then are used for packing, arranging, improving, and handling distributed cloud applications. Let us understand the advantages and features offered by both of them to get a clear picture of both the models to avoid the confusion of picking up any model, as per the need.

Is Azure Service Fabric beneficial – is it worth the deployment?

Azure service fabric application model offers a lot many profits. They are as follows:

• Offering quick setting up, it saves a lot of time and energy. As building VM cases in point takes a lot of time. In this type of model architecture, VMs are merely arranged as soon as a cluster is made that hosts the Service Fabric application platform. At this juncture, application packages can be organized to the cluster in a rapid fashion.
• This application platform consists of a scattered application management, hosting distributed applications. It also offers in dealing with the lifecycle freely of the hosting VM.
• The most important profit is Service fabric platform is that it can run on any machine giving an edge over its counterparts. Windows Server or Linux machines, whether it’s Azure or on-premises, it can run anywhere. It offers an abstraction layer over the fundamental set-up. This layer allows the application to run on diverse settings.
• What is more, it also offers high-density hosting. As we all know that, VM only hosts one workload in the case of cloud services platform. Whereas in the case of Service Fabric platform, applications are not attached with the VMs that run them. This means that a huge number of applications to a lesser number of VMs can be deployed. By doing so, the whole rate for bigger deployments decreases saving a lot of money.

Comparison of cloud services and Azure service fabric clarified – when to select either of them?

Service Fabric is a runtime service. Running on windows and Linux, it is an application platform level. On the other hand, Cloud Services is a scheme for arranging Azure-managed VMs with workloads closely knitted. This can be well understood with the diagrams shown below for both of them.

Fig 1: Cloud services deployment

Service Fabric clusters can be created in a lot of settings, together with Azure and on premises. Maximum Cloud Service applications are composed of more than one layer. In the same way, a Service Fabric application is made up of a lot of services.

Fig 2: Service Fabric Model

A lot of dynamics are involved that differentiates the two platforms and which one to use for your software mission. Let us probe into them one by one. Here they are:

• Firstly, you need to see whether the project is the result of an undeveloped development. If something needs to be added or updated or the enterprise project is started from the scratch. Ever since Azure Service Fabric took over from Azure Cloud Services, it makes sense to go for Azure Service Azure Cloud Services model must be used when you there is no need of commencing from scratch. Also, when the migrations of the codes to Azure Service Fabric is not needed then keep on using that model for the rest of your application.
• Also, for developing scalable products or applications need competent professionals. Hence, the present skillsets of the team members must be taken care of by providing them training, if needed, otherwise keep on using the same technology of cloud services.
• As the Azure Cloud services does not back up high-tech architectural model, there is a probability of an application with a more complex design. Then, in this case, Service fabric model fits the bill.
• For developing a hybrid environment with containers in the cloud, Azure Service Fabric seems to the best choice for you. In an Azure Service Fabric, all the procedures and additional accessible containers can be run alongside consistently and can be measured centred on your requirements.
• With Azure Service Fabric offering a naming service for you, communication between instances is done easily. This leads us to a modest communication model as compared to Azure Cloud Services.

Conclusion

This is a simple guide to make you understand the architecture and advantages for making the migration easier without changing the overall architecture of your application. This can only be achieved when the overview along with the architecture of both of them is understood well and in depth. For successful porting this is suggested as the conversion and changing of the packing and configuration files to service fabric model would be much easier offering myriad profits. In a nutshell, migration of the cloud applications to service fabric is pretty easy, however, when to go for it must be understood prior to the migration. However, your choice must be centred on which application/product you are demanding to form, exactly how much time you want, and when you need to release? Select any one as per the need and parameters out of these two discrete platforms, following diverse development models.

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