Private: Scalable Collaboration and Governance
Collaboration features make it easy to share knowledge amongst team members and onboard new users much faster.
Learn MoreA recent Gartner research survey on cloud adoption revealed that more than 80% of respondents using the public cloud were using more than one cloud service provider (CSP).
Gartner, December 2019
What Is Hybrid Cloud?
Hybrid cloud is a solution that combines on-premise data centers and private cloud with one or more public cloud services, with proprietary software enabling communication between each distinct service. A hybrid cloud strategy provides businesses with greater flexibility by moving workloads between cloud solutions as needs and costs fluctuate.
It’s also a good solution if an organization needs to offer services both in private data centers and via an on-cloud subscription. They would then be able to build web applications and services or machine learning models and use them both on-premise and on-cloud, as well as take advantage of their hybrid architecture to maintain communication between applications or data flow between the cloud and the on-premise infrastructures.
According to Gartner, the main challenges of hybrid cloud adoption are the following:
After observing a few projects, you can confirm these two problems but also realize that they are two faces of the same coin. The ability to create a sustainable Hybrid Cloud data platform lies in the location of the data but also the circuit through which the data circulates during processing.
If this is obvious on the macro scale (that is to say on the inter-application scale), this is not always obvious on a more reduced scale of data processing. It’s mainly due to the fact that in data science projects, you traditionally add more and more data sources over time. Thus for a given use case it’s rare to have a preconception of what the final data flow will be.
It is therefore a question of choosing the right location but also of the technologies and storage formats compatible with the operation carried out in order to maximize productivity during the design period and optimize and control costs during the production phase.
Dataiku DSS is now considered a cloud-native platform because we have integrated compute execution into native hosted services from all major public cloud providers. All Dataiku DSS processing, notebooks and webapps can be integrated with a scalable hosted service in the public cloud. Artificial intelligence and any data driven project rely on a resilient data ingestion at some point. Bringing elasticity to every atomic process of your workflow will drastically reduce if not erase refactoring:
Collaboration features make it easy to share knowledge amongst team members and onboard new users much faster.
Learn MoreThe age of AI presents additional risks across the enterprise that require a tighter — yet more flexible — governance structure.
Learn MoreDataiku makes it easy for administrators to search and organize datasets as well as monitor access and user activity.
Learn MoreHow can IT organizations scale to meet the demands of the modern AI-powered company?
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