Multi-cloud is becoming the norm. What does this mean for data science and data engineering when your data is spread across multiple clouds and on-premise? Learn Data Patterns in a Multi-cloud Future.
The most important asset for any organization is their data. But that is also the asset that’s the most challenging to manage. I’ve written how cloud computing has evolved to keep in tune with the changing dynamic of world economics and trade and why it’s imperative for every organization to have a multi-cloud strategy. While keeping pace with changing dynamics is important for businesses to thrive and grow, it also brings in a new set of challenges.
Especially when you bring in multiple cloud platforms where your enterprise data is spread across different clouds, how does it change the data science equation? Are you still able to leverage all the data from all the clouds and on-premise to still connect the dots and extract meaningful analytics out of them?
Mismanagement of multi-cloud expense costs an arm and leg to business and its management has become a major pain point. Here we break down some crucial tips to take some of the management challenges off your plate and help you optimize your cloud spend.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
The Cloud is a complicated space. It’s not a simple plug and play as most people would imagine. Let’s simplify the Cloud: GCP Edition. The Cloud is a complicated space.
If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out.
**Link: https://www.youtube.com/watch?v=gud65lqebrc** In this [**Google Cloud Training**](https://www.youtube.com/watch?v=gud65lqebrc "Google Cloud Training") live session, you will know everything about google cloud from basic to advance level...