Matillion vs. Windsor.ai

If the target dwh is one of the big players it could be a good option. Can retrieve data from multiple different sources and handle them internally. Expensive and being hosted by the client there's also the infrastructure burden of maintaining/paying for the server. Considering the resources needed hence the license cost that scales with them (despite the fact that you host and pay for them already) I wouldn't suggest the tool to a small company and, once you are big enough you probably want to jump on bigger more mature tools. Matillion is a nice niche player with some nice to have feature that are probably suited for a mid-size company with some money available to pay for the license but still a small infrastructure that just require one/two Matillion servers installed. A global company with multi regions needs will drown under the burden of handling/updating/maintaining all the servers independently and pay for the cost of each one of them.

Incentivized

• Matillion has a rich transformation library. It provides multiple functionalities, such as join, group by, pivot, various sources, and sinks.
• It provides the security capability as well. All the credentials can be securely stored in Matillion.
• Reusable templates can be built which reduces the redundancy.
• Time to production is very minimal.

Incentivized

• The new DPC version of Matillion uses Git principals like Commit, Push, Merge etc. This is perfectly fine for engineers, but for our use-case it means our customers will need to understand a basic level of Git. It would be great if they had an auto-commit-push setting which does it all for them.

Incentivized

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.

Incentivized

It has been easy to train new employees who don't have previous experience with Matillion. It is quite self-explanatory. There are quite a few things that can be done with Python, however, we have not really looked into this feature much but likely will do in the future. Mostly, it is drag and drop of components and environments can be set up so easy to set up connections as well.

Incentivized

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.

Incentivized

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

Incentivized

Matillion ran circles around Stitch and Striim both in functionality, setup, and performance. There was no real comparison. Fivetran massively outperforms Matillion in pretty much every facet of the production from setup, maintenance, visibility, and usability. It already has the ability to connect any data source to a destination regardless of database type. Why we chose Matillion over Fivetran is that, for our current needs, Matillion provides us with the functionality that we need and a much more competitive price for a smaller company.

Incentivized

I have been able to connect Matillion to AWS Aurora Databases, MySQL databases, Rest APIs, Files in AWS S3, etc. Being able to load all of that disparate data into one datalake has made data mining and reporting a lot simpler. I wish everything could be implemented as easily as Matillion.

Incentivized

• Saving us time reduces our need for headcount
• Allows us to collaborate on data-eng pipelines in a transparent way with non-technical stakeholders, ensuring accuracy and continuity
• Allows us a single platform to log and manage all of our pipelines to pin-point where something failed and why
• Always has a solution to very common data engineering tasks, i.e., real-time data, connectors, pre-built workflows, etc.