Fivetran vs. Matillion

Mostly Fivetran can be useful for working with risk reduction operations during agile data analysis processes. I.e., in the short term the heaviest data movement operations would be safe. If you need to create an automated infrastructure for the data, the ability to create data list transformations in SQL is useful for keeping the work integrable or with schema changes. For situations that require a lot of speed: setting up the Fivetran platform is very easy, as you only need to authenticate the sources of the data to start working, and this is excellent for covering fast storage operations.

Incentivized

Matillion is great any time ETL or ELT is needed. I've now used Matillion in 2 different companies and would have no problem recommending others to use it as well. The ease of setting up schedules to just take care of data imports and manipulation is incredible. IT has also been an incredible tool at bringing disparate databases together on a schedule so that I never have to think about it.

Incentivized

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

Incentivized

• Excellent tool to do ELT data pipeline using Snowflake
• Easy graphical orchestration to enable complex dependencies
• Easy manage users and group permissions
• Easy to schedule jobs using time dependency and job dependency
• Provides many API's to fetch data from multiple vendors

Incentivized

• More detailed logging
• More flexible choices for time range over which records are synced
• More options for masking and excluding sensitive data

Incentivized

• I think a non-CLI approach to installing and uninstalling python libraries would be nice. I mean, it isn't difficult to install a python library via Linux or CLI, but I imagine most companies don't feel comfortable allowing Matillion users to go on a virtual server and installing it themselves. Requirement.txt file for installing libraries would be simple, and maybe that could also be used to uninstall libraries as well......or maybe the library gets automatically downloaded if it is imported into a python script but the library doesn't exist.
• Python Component is lacking very much in terms of UI. It would be unrealistic for me to suggest Matillion build its own EDI, but it would be nice if a python component could connect to a local IDE. Right now, if you want to write any decent length python code, you are going to be stuck copying and pasting your code from your local IDE.
• It is also very difficult to debug in Matillion because you can't set breakpoints. Local IDE integration can resolve that.
• I would like to have more templates to copy from for certain simplistic scenarios. For instance, a template for a job that fails which sends an AWS SNS with the Job name, component it failed on, and the error message. It wasn't as simple as I thought it would be to figure out and having to use a shared job for such circumstances can be painful because you have to export a bunch of variables.
• There should be a drop down list for Global Matillion variables as it is difficult to remember at times.

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

Just need to input connection info.

Incentivized

Easy to follow steps in building a data pipeline. We have brought in people with minimal SQL experience and they have been able to quickly and successfully being able to pick up the skill set and contributing in reducing the time to market with various data assets. Matillion does provide a long array of components to select from to perform data transformation. But like any other tool, there are more than one way of doing the same thing. Some are more performant than the others, but easy to follow and easy to tweak to improve.

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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

Fivetran came well with the connectors' availability and updates with the source changes. We had an idea on data requirements in our case which helped us to work out on cost implication and take a decision for Fivetran as a data provider for our organization. These were 2 places where Fivetran out-performed, other vendors.

Incentivized

Overall, Matillion is an excellent choice for businesses that need a powerful and easy-to-use data integration and transformation platform that can handle large volumes of data. While it may be a bit pricey for some organizations, the platform's reliability, scalability, and customer support make it a worthwhile investment for many businesses.

Incentivized

Matillion has a nice scalability capacity, and once it has been stored in our AWS cloud it makes it much easier to scale it on demand. The reason I'm not rating it as 10 is that there is no way to migrate or move it to another cloud in an easy way.

Incentivized

• Development cost have reduced for each connector
• The pay-per-use model is still not out their which requires lot of overhead cost

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.