Cloudera Data Platform vs. Hugging Face vs. Oracle Exadata

I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.

Incentivized

If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face

Incentivized

• First, get the database on Oracle. If you are in an Oracle stack, it would be much better to use the Oracle products. If you are driving a Ferrari, you wouldn’t put a Mercedes engine in it. If you are writing a query, you cannot rely on other brands. Since I'm an architect, when I look for a product, I look for performance.
• The installation is easy because it comes out-of-the-box and you just start using it.
• Previous to Oracle Exadata, we were using a normal Oracle RAC service. We were just waiting for this product to come out.
• I'm currently writing a data warehouse on Exadata. Before this solution, we were aiming for this to be completed by 8 a.m., when our ETLs would finish. With the help of Exadata's special features, this was reduced to 3 a.m. This solution allows us to bring more data within the same time period. It provides us with more subject areas that provide more reports to our users. Our ETL times reduced to 65%, then to 50%.

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• Scales
• Highly available

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• Model APIs
• Hugging Face Spaces for deploying demo apps
• Latest updated models available easily
• Vast support for language parsing and other relevant tasks

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• High speed of SQL operations due to a unique design of Exadata with offloading of SQL processing to storage cells
• Built-in High Availability of a DB server due to it's base architecture of a multi-node Oracle Real Application Cluster
• High overall sever performance due to its use of a proprietary "Smart Cache" feature utilizing a high speed flash memory
• Excellent scalability of a DB server by adding cluster nodes as well as expanding it into a network of serially connected clusters

Incentivized

• Constantly changing costs
• Log visibility

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• Most of the Hugging face models are of big size, hence difficult to work if there is no access to high computational system like GPU.
• It’s good to have some visualization tool in hugging face for viewing model architecture.
• I recommend to implement hugging face lite version so that it can run on any system with less specifications.

Incentivized

• Integrating with different types of databases can be a challenge
• Having a hardware platform optimized to run specific database software surely comes with a price
• Single vendor to support same hardware and software will make migrating to different hardware and/or software become a hassle

Incentivized

Excellent machine for your database needs . Don’t have to think twice if you have the budget to own it

Incentivized

We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.

Incentivized

IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.

Incentivized

There are some other services offer similar capacity as to Hugging Face, but not entirely the same. For example, amazon web services have a machine learning service called Comprehend, which offer a set of easy to use APIs to do machine translation and entity recognition and some other common NLP use case.

Incentivized

Oracle Exadata Database Machine had the best performance overall hands down. It clearly beat the competition and we were seeing 1000X improvement on SAP HANA. Oracle Exadata Database Machine beat that without us refactoring our code. To achieve that in HANA, we had to refactor the code somewhat. Now this was for our limited POC of 5 use cases. Given the large number of stored procedures we had in Sybase, we need to capture more production metrics but we are seeing incredible performance.

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• Reduced operational costs
• Speed to market

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• Hugging Face is cost and time saving.
• Pay is less, you pay what you use, doesn't affect much.
• Overall positive impact on business.

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• Single support from a single vendor with both machine and database from Oracle, which is costing us less.
• With Exadata, we need less technical manpower and less technical support. A business transaction with the integrated and centralized database helps us focus on other business needs.
• We don't need to buy additional licenses and Hardware for the next 3 to 5 years.

Incentivized