Managing a Multi-Cloud Data and Analytics Platform

Multicloud adoption is gaining momentum. Gartner predicts that by 2022, 75% of enterprise customers using cloud infrastructure as a service (IaaS) will adopt a deliberate multicloud strategy. Enterprises adopt multicloud strategies for various reasons, such as preventing vendor lock-in, enabling access to best-of-breed cloud services, regional requirements, and so on. However, there are challenges with this model. Every cloud has its way of doing things, and they don’t play nice together. What if you had a data and analytics platform that extended across the major cloud providers? How would you manage it? In this session, you’ll learn how simple it is to manage the Databricks Lakehouse Platform — one platform for data, analytics, and AI across AWS, Microsoft Azure and Google Cloud.

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[kembhootha]

What industry standards govern the "what" aspect - i.e. the models - are there widely accepted standards for the "federated model" ? There is CIM, there is ODI etc, but is there a common modeling approach across the multicloud arrangement?

[joydeepbhattacharya]

Per my understanding, API is the way for now
Any other platform feature?