Enterprises operating in today’s post-pandemic world face priorities that previously were not considered. For example, many companies had to adjust to accommodating a remote workforce motivating leaders to embrace a hybrid IT model. 

Oftentimes it is difficult for IT leaders to support a new business model with existing legacy infrastructure. An option to consider would be to combine the best technologies through a hybrid integration model. 

Gartner defines hybrid integration as the ability to connect applications, data, files and business partners across cloud and on-premises systems. Even though the IT world is shifting to the cloud, a considerable amount of data and processes will continue to be managed on-premises.

Hybrid integration allows your legacy on-premises systems to align with cloud technologies. It enables improved connectivity throughout the enterprise and increases visibility into all your data.

Implementing a hybrid integration also offers a organization with an opportunity to improve business processes. 

As you build the roadmap for your hybrid integrations, you may want to strategize answers for the following questions:

  • What data and application functionality do your users need to work at peak efficiency and security? 

  • Are there organizational or regulatory requirements for storing data in on-premises systems?

  • Could you potentially leverage previous investments and extend the life of legacy systems with hybrid integrations to connect those systems to all other systems or those of partners and customers?

  • Do you have existing integrations?

  • Can your internal IT team maintain the existing integrations and integrate more solutions when needed, or should you outsource?

  • Is it a better use of your IT team’s expertise to support improvements to your organization’s core products and services, and outsource the complexities of hybrid integrations?

  • Do the solutions you’re connecting have APIs or EDI connectors?

  • Do you have a detailed plan for managing the various data standards, protocols and other integration elements to achieve seamless communication, analytics and process efficiency between all your systems?

  • What are your requirements for scaling the hybrid integration up or down as the business case changes, as data is processed on mobile or IoT devices, and across multiple private and public clouds?

  • What are your hybrid deployment requirements to run solutions on premises, in the cloud, or in combination?

  • Is your best option to use a hybrid integration platform (HIP) and if so, which one? Will your complex integration requirements still need outsourced development to maximize the HIP performance for your unique environment or workaround the HIP’s functionality gaps?

Hybrid integration allows organizations to unleash data and business logic from legacy systems to support innovation in customer and partner relationships, enhanced by powerful cloud solutions.

Here at Polyrific, we have a team of experts that can help you fully optimize your data and applications as you transition to a hybrid tech stack. 

Polyrific partners with clients to deliver hybrid integrations that achieve a fast return on value. Contact us today to explore how we can help you better leverage all your systems. Our team will augment your IT team and guide you to avoid setbacks in your hybrid integration and cloud migration projects.

Data is always at the core of every business and process, but data is not always perfect, and it can take time and effort to properly identify when something has gone wrong.

As working in the Cloud becomes more inevitable with every passing moment for businesses looking to grow, some features and services can benefit your business with considerable savings and ease implementation and maintenance times.

One such service is available through Azure with SQL PaaS (Platform as a Service), which takes all the heavy lifting out of managing a database, letting you focus directly on your data and the applications that interact with it.

But what about everything else that is involved with handling a database? You do not have to worry about anything else; Microsoft has you covered on this front so you can concentrate on what matters.

This includes patching and updating the back-end, additionally assisting in identifying, reporting, and taking actions on any potential threats to your business data and databases.

Azure SQL PaaS also allows you to grow as much as you need when you need it, at a fraction of the cost from your on-prem and so large it can easily handle big data without breaking the bank.

The added advantage to this model means that your data will always be available with minimal to no downtime for your applications to work at full speed, wherever you are, cutting the need to synchronize multiple databases around your business units.

One key feature for Azure SQL PaaS is the ability to use it to have it as a redundant backup for your in-house on-prem applications when moving them to the Cloud is not always feasible.

There is also the possibility of migrating all of your data through tools provided directly for Microsoft. There is no need to build special tools or perform complicated migrations that can take a large amount of manpower, money, and months to be completed.

Microsoft also ensures that backups are performed continuously, creating a robust redundancy to assist in rapid recoveries and storing them for extended periods in case you need to roll back through a considerable period of time.

Being part of the Azure services, there is an integration made available with the Azure AD, allowing for Single Sign On (SSO) and faster account handling, avoiding creating additional accounts or handling multiple user databases on-prem.

Are you interested in moving to the Cloud and moving your database information to Azure SQL PaaS? Contact us through the provided method below, and we will get back to you to assist with all your migration and integration needs.

Machine learning is a system of algorithms aimed at detecting patterns in big data and then learning from those patterns without being explicitly programmed by a human operator. These algorithms take a probabilistic, rather than a deterministic, approach to accomplishing goals. Let’s take a quick look at what that means by way of example:

Deterministic Approach

A human programs software in no uncertain terms to remind him to bring his umbrella to work if the chance of precipitation in his area is greater than 40%:

//Get the weather forecast
var chanceOfRain = myWeatherForecast().precipitationChance;

//Send the message
if(chanceOfRain > 40%){
    sendEmail("Bring your umbrella to work!");
}

This approach is deterministic because the outcome is predetermined by the author of the code in no uncertain terms.

Probabilistic (Machine Learning) Approach

An algorithm crawls big datasets such as tweets about the weather, weather news, forecasts, umbrella sales, and supervised feedback (we’ll get to that later) such as, “did you bring an umbrella to work today?”: