AWS Batch vs. IBM Cloud Code Engine

More appropriate if you have a tech group that can use more of the AWS Batch rather than one or 2 things. It works great for me, but there was a huge learning curve the first week of using it. Now, I love it - and I hope to dig deep into other parts not just S3.

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It would be a good solution for running serverless applications. Because infrastructure setup and maintenance expenses can be avoided, the investment will pay for itself. The time to value is short, allowing IT to respond to business demands quickly. It aided us in customizing security as well as operating a personal project using to autoscale up and down approach. Also, because there isn't much hassle, items can be pushed into production as soon as possible. Simply push a container, create an application, and you're ready to go. But, It is less suited when you have a static machine or need to keep data in some way and do not want to utilize network storage or a database.

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• Easy to orchestrate and trigger jobs
• No time limit issues like lambda
• Multiple Jobs can be run in same single compute and job queue
• JOb queue can queue up task for parralled or serialization

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• Managed Environment for partners and customers - shifting skills and speed to CSP
• A variety of programming model support
• Elastic scalability for cloud native development and speed
• Flexible consumption model
• Containerized workloads with horizontal scalability
• Here is an example - live demo walk-thru delivered to partners and community:
• https://www.youtube.com/watch?v=MCBClYgpDFg

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• Jobs monitoring dashboards are not matured
• Documentation and support is something which can be improved
• Sometime i faced the slow response or slow in performance i would say

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• the pricing structure is complicated, and the servers are expensive. I really think they should offer better pricing options and support for more languages
• sometimes the servers go down, and they take too long to respond to support tickets
• uploading documents is slow since I have to do it one by one, making the process much longer than it should be

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Since this capability supports a wide variety of use cases - all on non proprietary and open technology based frameworks

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Key advantages include cost-effectiveness through dynamic resource provisioning and the use of spot instances. It auto-scales to meet workload demands, allowing easy job submission via the AWS Management Console or SDKs. It integrates seamlessly with other services like S3 and CloudWatch. It features automatic retries for failed jobs. It allows for a custom computing environment tailored to specific needs

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Independently Invited by TrustRadius

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Consumers can purchase individual components as well as unlocking new bundles with special features and services including the extensive data management governance capabilities of the Automation range. Kubernetes containerizing for effective service implementation and an agile, flexible multi-cloud data program help both utilization expansion and deployment to be improved by this architecture.

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We wanted to start everything on a scale & with fewer resources to manage the underlying infrastructure.

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What impresses me most about IBM Cloud Code Engine is the container workload management capability and the Cloud services and dataflow monitoring functionalities. Data security and network security control via IBM Cloud Code Engine is quite excellent and very responsive data integration functions and the first deployment is not very technical.

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• Overall over business is able to save the cost
• Saved our times to improve the existing process
• Able to integrate with other applications as well, so that is plus point

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• [One] positive is that we have one tool to learn (and a simple one) for all kinds of deployment. We need less people to maintain the system.
• We have saved a lot of time on trouble shooting. It's clear how it works. It's "just" kubernetes..

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