Likelihood to Recommend It is very well suited for ETL on the cloud. Whenever there is something that can be accomplished with no code or little code, Matillion is a good tool. However, if your pipeline requires a lot of customizations, Matillion should be avoided.
Read full review The best place for Oracle Warehouse Builder is at the business IT level. It's not suited for business-level users. They are easy confused. One way to reduce the confusion for the developers is to set up the workspaces based on the requirements that are discovered in design sessions. Once this is complete, the implementation of Oracle Warehouse Builder can take flight and be successful.
Read full review Pros We leveraged Matillion’s no-code principals to make data manipulation easy for our internal customers. People who don't know how to use SQL no longer need to. Everything in Matillion is self-explained with no or little coding. We connected Matillion to our data warehouse to allow people to read raw data, transform it, then write results back to their sandbox databases. The drag and drop component design allowed customers to create complex data models at the speed of thought without any risk to production data. With sharing capabilities between projects enabled, everyone was able to help each other when questions arose which instilled a strong sense of collaboration and community. Read full review Easy transformation. Easy implementation from Oracle to Oracle systems. Ease of usage and easy to learn. Starting component of metadata management. Read full review Cons 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. Read full review What I noticed is that sometimes OWB doesn't generate the best SQL in the package especially when there are a high number of source tables in the ETL. It would be nice if ETL developers were allowed to update the generated packages in the database directly. Another thing - moving OWB ETLs from one database to another one could be easier - for example it would be nice to just copy the generated packages from one database to the other one without doing the deployment of these ETLs through OWB. Read full review Likelihood to Renew 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.
Read full review Usability 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.
Read full review Support Rating 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.
Read full review Implementation Rating We were able to control on access and built various enviroment for implementation
Read full review Alternatives Considered 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.
Read full review Ab>initio, IBM Datastage 8.0
Read full review Scalability 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.
Read full review Return on Investment 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. Read full review It improved understanding of ETL functions. Data is consistent. The speed is pretty good. Read full review ScreenShots