I don't clearly catch the difference betwenn these two concept. Someone told me that the essential différence is that the cloud computing give you a large space of storage and the grig give more advantages than storage, we can profit to much power with this last.

 

Does any one know more clearly these two concept; and tell us?

Views: 232

Replies to This Discussion

I don't claim to be the expert, but the difference is (I think) in use.

 

Grid represents a scalable framework.  You write your algorithm and your code and use as much computing power as you wallet can afford.  (Useful as some work can be highly parallelizable) .

 

Cloud computing offers storage (true) but it's also represents the applications as well.  Ideally with cloud computing, you don't need to have certain applications on your desktop - as long as you can hit the cloud, you can get, update, and use your data.  

Thanks thomas;

What  I got :

 

Grid - much computing power and can be highly parallelizable

 

Cloud - Storage and dont need to have  certain applications on your desktop ( that's just like server application?)

 

Someone can tell us more?

I think if you look at the history, you will understand some difference.

In my own experience, the grid began with Oracle using it as a type of metadatabase, which would point to multiple databases residing on different but uniform hardware systems.  So if a company had multiple unix boxes and needed to increase the size of their database, instead of purchasing additional hardware they could implement the grid database and combine their multiple unix servers into one database resource.

 

Cloud is much more in terms of it offering not only a database, but also an entire server including the operating system.

The cloud exposes an operating system, whereas a grid exposes a database.

 

But I am no buzz word expert so I might be wrong.

I just talked to a buddy about this, essentially the Oracle Grid product is differant because it runs the DB in memory. So access times are a lot quicker. I don't think it is really a matter of Vs. so much as Grid computing is a way to handle db transactions in a faster way.

 

He said their grid servers had something like 72gbs of ram. Freaking crazy

Please Bradley, wha do you think about Jackie's reaction?

RSS

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

Google DeepMind Introduces ATLAS Scaling Laws for Multilingual Language Models

Google DeepMind researchers have introduced ATLAS, a set of scaling laws for multilingual language models that formalize how model size, training data volume, and language mixtures interact as the number of supported languages increases.

By Robert Krzaczyński

Presentation: Foundation Models for Ranking: Challenges, Successes, and Lessons Learned

Moumita Bhattacharya discusses the evolution of Netflix’s ranking systems, from the multi-model architecture to a Unified Contextual Recommender (UniCoRn). She explains how they built a task-agnostic User Foundation Model to capture long-term member preferences. Learn how they solve system challenges like high-throughput inference and the tradeoff between relevance and personalization.

By Moumita Bhattacharya

Swift Cross-Platform Framework Skip Now Fully Open Source

After three years of development, the team behind Skip, a solution designed to create iOS and Android apps from a single Swift/SwiftUI codebase, has announced their decision to make the product completely and open source, in order to foster adoption and community contribution.

By Sergio De Simone

Railway Highlights the Importance of Logs, Metrics, Traces, and Alerts for Diagnosing System Failure

Railway’s engineering team published a comprehensive guide to observability, explaining how developers and SRE teams can use logs, metrics, traces, and alerts together to understand and diagnose production system failures.

By Craig Risi

Google BigQuery Adds SQL-Native Managed Inference for Hugging Face Models

Google has launched SQL-native managed inference for 180,000+ Hugging Face models in BigQuery. The preview release collapses the ML lifecycle into a unified SQL interface, eliminating the need for separate Kubernetes or Vertex AI management. Key features include automated resource governance via endpoint_idle_ttl and secure identity-based execution using existing data warehouse permissions.

By Steef-Jan Wiggers

© 2026   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service