GitLab vs. IBM Cloud Object Storage

In general it's a good product with a huge number of features. It can do CI/CD checks, deployment, repo hosting, integrations etc. A lot of it can be managed from just the UI but also has an ok API. It does tend to release new features semi-often too which is nice. It can have a steep learning curve for certain features so not a starter-friendly vendor, but great for most businesses.

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In my experience, IBM Cloud Object Storage is well suited for projects like the one I am working on. This includes the use of natural language classification and the uploading of data to train a machine learning model for tag suggestions based on a body of text. Using IBM Cloud Object Storage has helped with this greatly. IBM Cloud Object Storage has also been great for Big Data Analytics thanks to its scalablilty and ease of use for large datasets. Alongside IBM Watson and our team's internal big data tools we've managed to process and analyze data more efficiently, leading to key insights that have driven business value for our clients.

• GitLab excels in managing code versions, allowing easy tracking of changes, branch management, and merging contributions.
• It helps maintain code stability and reliability, saving time and effort in the development or research workflow.
• Powerful code review features, enabling collaboration and feedback among team members.
• Robust project management features, including issue tracking, kanban boards, and milestones.

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• IBM Cloud Object Storage is an excellent choice for disaster recovery and backup solutions. Its high durability and geographic redundancy ensure that our backup data is safe and can be quickly restored in case of a disaster. This capability is crucial for maintaining our business continuity and minimizing downtime. We have deployed our loads in an IKS cluster distributed in 3 different AZs with stateful data allocated in COS.
• We have a video streaming application and need to store and deliver a vast library of video content to millions of users worldwide, so we store our data in COS, which is cheap and reliable.
• We have a bunch of data that must be analyzed and stored in datasets for fraud detection, risk management, and customer insights. In these cases, this data is moved from Onprem to IBM Cloud so we can use cheap storage like COS.

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• CI variables management is sometimes hard to use, for example, with File type variables. The scope of each variable is also hard to guess.
• Access Token: there are too many types (Personal, Project, global..), and it is hard to identify the scope and where it comes from once created.
• Runners: auto-scaled runners are for the moment hard to put in place, and monitoring is not easy.

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• Searching and retrieving—full-text search or metadata search—is one of the significant areas of improvement. It isn't easy to search for data with this.
• Integration with other IBM cloud services is trickier. For example, integrating this with API Connect to access the data from API will be difficult for users.
• Support - I think you should have more support community.

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I really feel the platform has matured quite faster than others, and it is always at the top of its game compared to the different vendors like GitHub, Azure pipelines, CircleCI, Travis, Jenkins. Since it provides, agents, CI/CD, repository hosting, Secrets management, user management, and Single Sign on; among other features

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because it is a robust, safe and flexible product

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- As a combination of features and products, GitLab is a really great tool for developers. - Also, it comes with both light and quick changes in a web browser version, as well as pro work in VS Code mode. However, in certain areas, such as large-scale issues/incidents, management can be improved.

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For my use cases, it has been a very smooth experience. Even my new colleagues have been able to get on top of things very quickly. This shows how easy it is to work with

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I've never had experienced outages from GItlab itself, but regarding the code I have deployed to Gitlab, the history helps a lot to trace the cause of the issue or performing a rollback to go back to a working version

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We rarely face downtime or access issues with IBM Cloud Object Storage. It’s mostly available when we need it, even during peak hours or heavy data loads.

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GItlab reponsiveness is amazing, has never left me IDLE. I've never had issues even with complex projects. I have not experienced any issues when integrating it with agents for example or SSO

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I would give it a 9 because it works smooth with our AI and analytics tools, no major slowdown. Pages and dashboards load fine most of the time, and reports finish in decent time even when data is heavy.

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At this point, I do not have much experience with Gitlab support as I have never had to engage them. They have documentation that is helpful, not quite as extensive as other documentation, but helpful nonetheless. They also seem to be relatively responsive on social media platforms (twitter) and really thrived when GitHub was acquired by Microsoft

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I have been working in IT sector for more than 15 years. I have worked with various vendors. IBM's sales team, support team have been really helpful. After we start to use their product, their UX design team also contacted us to get feedback from us. They are really interested about our experience.

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I just researching and applying the tools on their platforms to ensure a good learning path, based on my needs. Reading the documentation related with resources, tools. Is too big, but I am trying to know more about it every day. It is a good way to know more about their resources. A new way to attract new customers. At the end of the day, we are all involved in improvement and automation of our tasks and resources for customers and end-users.

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Yes Our organization used IBM professional services to implement IBM object storage because of its data consistency and multiple way to upload and download data and its encryption security features. Also that its brand matter for the any organization to secure the layer and storage. It sis also verify that application and system are compatibale for this product

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GitHub is an inferior product from most points of view. We had to use it and the teams finds no positives about it. Everything is a downgrade from our previous GitLab solution. GitLab CI\CD is vastly superior to workflows, for example doing a manual node is just "when : manual" in GitLab while you have to do clickops in GitHub to achieve the same. No overview of code in branches is a minus when we tried to figure out what our colleagues are trying to merge as it looked off.

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Amazon S3 is a great service to safely back up your data where redundancy is guaranteed, and the cost is fair. In the past I have used Amazon S3 for data that we backup and hope we never need to access, but in the case of a catastrophic or even small slip of the finger with the delete command, we know our data and our client's data is safely backed up by Amazon S3. Amazon S3 service is a good option, but based on the features it provides compared with IBM Cloud Object Storage, it is less suitable. IBM Cloud Object Storage is also integrated with more services, like IBM Cloud SQL and IBM Aspera, which AWS does not provide to transfer files at maximum speed in the world.

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I think is very well designed, and like any VCS it works as intended

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Scaling up the number of users can lead to significant increases in licensing costs, which, while not a technical limitation, can be a practical constraint for some organizations

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• GitLab cut down our spent on container, package and infrastructure registry
• Best thing is we can now have everything in single platform which cost effective too
• Quality of support is really good and they do have emergency support team as well which is great

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• This allows us to recommend a platform to our clients that will quickly help them create new, efficient business processes with very little development.
• This saves clients hours and days of manual analysis of images, allowing the system to do the work when attaching Object Storage to models.
• There is a learning curve in utilizing the storage and the modeling, but once up and running, it works well during deployment.

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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. TR verified that a representative sample of customers was invited. More Info