Gemini Enterprise Agent Platform vs. Oracle Exadata

we used Vertex AI on our automation process the model very useful and working as expected we have implemented in our monitoring phase this very helpful our analysis part. real time response is very effective and actively provide detailed overview about our products.this phase is well suited in our org. this model could not applicable for small level projects why because this model not needed for small level projects and without related resource of ML this model not useful. strictly on non cloud org not suitable means on pram not suitable

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Oracle Exadata is well-suited for environments where massive performance for Oracle databases is required. Storage indexes reduce the unnecessary I/O. Smart Flash
Cache accelerates random reads/writes.

Our OLTP application demands very high concurrency. Multi-node Exadata provides high availability and zero downtime during DB patching. It comes with lots of built-in automations, so it reduces many routine tasks for sysadmins, like network, storage, and VM configuration, and it also reduces many Oracle DBA tasks, like Oracle software installation, patching, and upgrades.

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• Vertex AI comes with support for LOTs of LLMs out of the box
• MLOps tools are available that help to standardize operational aspects
• Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers

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• Oracle Database : Deliver industry-leading security, high availability and scalability with Oracle Database, which has been significantly enhanced to take advantage of the Oracle Exadata Storage Servers.
• Exadata Smart Scan : Improve query performance by offloading intensive query processing and data mining scoring to scalable intelligent storage servers.
• Smart Flash Cache : Transparently cache 'hot' read and write data to fast solid-state storage, improving query response times and throughput. Exadata systems use the latest PCI flash technology rather than flash disks. PCI flash delivers ultra-high performance by placing flash directly on the high speed PCI bus rather than behind slow disk controllers.
• Hybrid Columnar Compression : Reduce the size of data warehousing tables by 10x, and archive tables by 50x, to improve performance and lower storage costs for primary, standby, and backup databases. Query high, query low, archive high and archive low.
• Infiniband Network : Connect multiple Oracle Exadata Database Machines using the InfiniBand fabric to form a larger single system image configuration. Each InfiniBand link provides 40 Gigabits of bandwidth–many times higher than traditional storage or server networks.
• Petabyte Scalability : Easily scale data warehouse to support enterprise data growth.

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• Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
• Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
• Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users

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• The process of patching and upgrade of Exadata server components could be improved with a goal to minimize the overall effort, make it fully automated and transparent.
• Improved guidelines and possibly more sophisticated tools for sizing of new Exadata servers for migration from old legacy hardware.

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I am comparing Exadata with the Oracle RAC database experience. In addition to Oracle RAC features, Exadata provides automatic performance optimization through Smart Scan and storage indexes. Deep integration with the Oracle ecosystem and tight coupling with Oracle Enterprise Manager
for monitoring and management. Some downsides of Exadata are: a steep learning curve, concepts like cell offloading, IORM, and flash cache behavior aren’t intuitive initially. Operating
Exadata requires specialized DBA skills.

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Google is always top notch with their security and user interface performance. We use Google's entire suite in our business anyways, so using Vertex became second nature very quickly. I will say, though, that Google does need to come down on the price somewhat with their token allocation. Also, their UI is very robust, so it does require some time for training to really master it.

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We tend to adapt and use the platform that suits the customers needs the best. We return to Vertex AI because it is the most in-depth option out there so we can configure it any which way they want. However, it is not quick to market and constantly changing or updating it's feature-set. This makes it suitable for bigger customers that have the capital and time to spend on a bigger project that is well researched and not quick to market like some of the other options that feel like a light-version of this.

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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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• It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
• It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
• It'll help you in terms of Security wherein we can use row level security access to authorized persons.

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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.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info