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SaaS, PaaS, IaaS: why you should (re)consider PaaS for your data projects

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SaaS, PaaS, IaaS: why you should (re)consider PaaS for your data projects

When it comes to your data applications, would you be better off choosing an IaaS, PaaS or SaaS solution? We will admit that we have just beaten the record for the largest number of acronyms in a single sentence. But these are the choices that any organization will face when looking for a solution to deploy its data applications.

Is there an easy way of summarizing the difference between these three variations of the cloud (IaaS, PaaS and SaaS)? Those who enjoy food analogies will appreciate the (famous) “Pizza as a Service” model, except that the article was published in 2014 and now only covers a fraction of what a Platform as a Service is or should be.

PaaS: prioritizing speed and bespoke developments

First of all, let’s go back to basics. IaaS services (Infrastructure as a Service) use the cloud to give companies a chance to tap into raw capacity, meaning processing power, storage and bandwidth. The organization is responsible for deploying and configuring all the required components, from the operating system through to the business applications and infrastructure software (databases, web servers, cache managers, etc.).

At the other end of the scale, so-called SaaS applications (Software as a Service) provide a service that end users can utilize directly. No action is required other than signing up for the service to activate their account. PaaS solutions are based on an intermediate model complete with a ready-to-use technical environment that each company can use to design and roll out its own applications.

The comparative advantages of the three solutions have long been summarized with a diagram representing an extremely dated analysis that does not fully reflect how data applications have developed… and how their requirements have changed:

  1. Accelerate the project lifecycle
    In terms of data projects, the overriding criterion is the “time-to-market“. Digital transformation has left its mark and set the pace. The aim is to design and deploy projects within short timeframes without removing the possibility of developing bespoke applications. On the contrary, an organization’s data contain all the specific features of its business activities, and analytic applications must be capable of extracting the very essence of those data. This dual requirement – speed and personalization – is especially suited to PaaS solutions.
  2. Incorporate technological innovations
    The software stack for data projects is complex and constantly changing. Whether looking at big data technologies or algorithms for machine learning or deep learning, software innovation regularly spawns new components. But organizations still need to be quick in incorporating them so that they can be used to lead projects. That is why it is effective to have a PaaS specifically focused on analytical applications, where the vendor is responsible for qualifying the value of any new features and subsequently ensuring that they are correctly integrated and automated.
  3. Keep the freedom of choosing the cloud service provider
    Microsoft Azure, Amazon Web Services, Google Cloud Platform, OVH… when it comes to cloud service providers, companies are spoilt for choice, especially since they can also opt for a hybrid cloud solution in order to keep their data on their own infrastructures while running applications in the cloud. The good news is that technologies have matured to such an extent, like containers, that PaaS services can now be agnostic. Basically, the resources are available to make PaaS services independent of the underlying technologies and therefore capable of running in any cloud environment.

For data applications, organizations need to take a new look at their potential options with other analytical criteria.

Discover a multi-cloud PaaS solution that is capable of ramping up your most specific data projects. Ask for a demo now! Although the result shows PaaS solutions in a favorable light, it still only offers an initial insight. The success of a data project also depends on the chosen organizational structure and methodology, as well as its ability to engage users. These are just some of the areas where a Platform as a Service can pay dividends…

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