Maia by Matillion vs. WSO2 API Platform

Great: Need to query simpler APIs, or utilize well known services such as GSheets etc.? Matillion has got some of the best and easiest to use connectors out there. Not so great: Do you need have a competent CI/CD flow that you will be able to update / compare from Matillion as well as other sources at the same time? Good luck, you will need to be extra careful, as you might have to have a deeper dive into your servers Terminal each time you have a git conflict.

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It's free! No argument can win a fight with that! And it's the only reason I gave it a 5. If you have no money to spend, and a simple environment you'll have a nice product. But free does come with a price. After 5 years we're still struggling with ports, and analytics (it just won't work without any errors caused by some configuration somewhere). An API Manager should work out of the box. The only configuration expertise that any developer wants to invest in, is the configuration of API's. Not the product itself... Anyone who've seen the training material, just for installing this thing will agree that this is not the way to go. Of all the API Managers out there (we've tried 4), WSO2 is the only one were you need to know how this dragon of a java application works internally. Did I already mention the humongous amount of config files?

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• The user interface of your data pipelines makes it easier for people who aren’t as techy as data engineers to observe what's going on.
• Customer support is quick, not always as efficient as you would want it to be, but still.
• Nice documentation available.

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• Authentication based on OAuth 2.0 and HTTP Basic Authentication.
• Rate Limiting applied at different levels like Subscriber, API, Resource and Backend.
• Monitoring by exporting the metrics in Prometheus and traces in Jaeger.
• Mediation to perform transformation, orchestration etc.

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• Matillion is brilliant at importing data -- it would be amazing to have more ways to export data, from emailed exports to API pushes.
• Any Python that takes more than a few lines of code requires an external server to run it. It would be great to have more integration (perhaps in a connected virtual environment) to easily integrate customized code.
• Troubleshooting server logs requires quite a bit of technical expertise. More human readable detailed error handling would be greatly appreciated.

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• Better QA testing prior to releases rollout
• Better support needed

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With the current experience of Matillion, we are likely to renew with the current feature option but will also look for improvement in various areas including scalability and dependability. 1. Connectors: It offers various connectors option but isn't full proof which we will be looking forward as we grow. 2. Scalability: As usage increase, we want Matillion system to be more stable.

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We are able to bring on new resources and teach them how to use Matillion without having to invest a significant amount of time. We prefer looking for resources with any type of ETL skill-set and feel that they can learn Matillion without problem. In addition, the prebuilt objects cover more than 95% of our use cases and we do not have to build much from scratch.

Overall, I've found Matillion to be responsive and considerate. I feel like they value us as a customer even when I know they have customers who spend more on the product than we do. That speaks to a motive higher than money. They want to make a good product and a good experience for their customers. If I have any complaint, it's that support sometimes feels community-oriented. It isn't always immediately clear to me that my support requests are going to a support engineer and not to the community at large. Usually, though, after a bit of conversation, it's clear that Matillion is watching and responding. And responses are generally quick in coming.

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We were able to control on access and built various enviroment for implementation

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Fivetran offers a managed service and pre-configured schemas/models for data loading, which means much less administrative work for initial setup and ongoing maintenance. But it comes at a much higher price tag. So, knowing where your sweet spot is in the build vs. buy spectrum is essential to deciding which tool fits better. For the transformation part, dbt is purely (SQL-) code-based. So, it is mainly whether your developers prefer a GUI or code-based approach.

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Providing better capabilities comparing the overall API lifecycle management, especially the availability of API Integration layer and a strong identity layer of their own which provides an end-to-end API ecosystem that would be advantageous in terms of a large software development initiative.

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We're using Matillion on EC2 instances, and we have about 20 projects for our clients in the same instance. Sometimes, we're struggling to manage schedules for all projects because thread management is not visible, and we can't see the process at the instance level.

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• Matillion has been the backbone of my company's analytical functionalities for 10+ years, so it has a good ROI.
• The price is ok for what our company built with it, but it starts to be less competitive if the tool is not used at its fullest.

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• We've moved away from legacy SOAP services where nobody knew what services was used by who. WSO2 eliminated at least 90% of time spend on any service.
• Creating API's (or actually creating the API Management layer...) is so simple that new developers can get away with it in no time. Again, real time gainer.
• Since creating API's is so simple, developers are very fast in adopting a kind of "Domain thinking". In comparison with Azure API Manager: Azure does not demand knowledge of "how" the product works, but it's definitely more difficult to get an API up and running in Azure. And for some reason, azure does not promote clean domain driven architecture. Domain Driven architecture is the greatest time saver strategy possible. And WSO2 fits nicely in there.

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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