Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed and scale that matches the business’s data ambitions. The vendor states enterprises including Cisco,
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Pricing
Matillion
Editions & Modules
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Offerings
Pricing Offerings
Matillion
Free Trial
Yes
Free/Freemium Version
No
Premium Consulting/Integration Services
Yes
Entry-level Setup Fee
No setup fee
Additional Details
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
Cost and ease of use were better for our purposes. Matillion distinguishes itself from Fivetran and SnapLogic through its user-friendly design, no-code interface, in-depth transformation capabilities, allowing for complex data manipulations directly within the platform, …
We decided to move forward with Matillion because it was the best tool among tools that support both ingesting data from a source system to a target database and running transformation workflows on it afterwards. Fivetran and Airbyte only support data ingestion and we had our …
The Matillion selection was not my decision. But I think it's a good enough choice. It is especially valuable that the team can learn Matillion easily and that the project can be understood by the entire team with the visual environment instead of complex ETLs.
Both the Databricks platform and dbt Cloud are more powerful from the point of view of the development lifecycle and data use cases covered. They are also more complex and require specialized data engineering skills to be used. Matillion has a lower barrier of entry for small …
Removes most of the complexity around setting up and preparing things. If you could describe with words what needs to be done to move data from A to B, the implementation in Matillion would probably be the most similar in terms of simplicity of understanding what you are doing …
Matillion is a good tool for integrating multiple clouds. Informatica has been a market standard for many years, it provides multiple capabilities for data governance, data quality, etc. However, Informatica is pretty expensive compared to Matillion. Also, Matillion is more …
dbt is great for engineers and those comfortable with coding. Matillion is the low-code alternative with a huge emphasis on collaboration with ease. You don't need to checkout a branch, clone, pull, merge etc. in order to help your colleague with a data pipeline. The simple …
Matillion was chosen by Schibsted due to the seamless integration with Snowflake. The ease of use and fast workflow have made it an essential tool in our setup, and with the option to integrate nearly every data source there is, plus the ease of use, it really gives a lot of …
Matillion is much easier to set up and easier to work for the team. Offers a lot more connections which are easier to set up. Environment variables make it easy to set up once and job creation is easy. We use Metadata tables to just loop through the list of tables that need to …
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 …
Matillion is cheaper and we really like the customer support of Matillion as well as lerning materials provided by Matillion were far better. They also made connectors for us for free while others were charging us for it.
I tried to use several opensource tools before Matillion. They were not bad, but I spent a ton of time maintaining the system, and debugging why things wouldn't work. It also seemed like everything needed a hack to get it working properly. With Matillion, it just works outside …
We have selected Matillion because it provides similar if not exactly the same functionality as Qlik at 1/3 of the cost. Matillion are also working closly with Snowflake to integrated all of our Data Platform needs going forward. This will truely add value to our business in …
When compared with other technologies , Matillion was cost effective , more scalable and flexible in terms of complexity and components. The licensing cost of Matillion was also less and the flexibility with components helps in implementing complex business logics. The training …
Matillion is very easy to learn and develop quickly. It also allows complex orchestration on a visual platform. It has many ready-made components, and it is easy to develop new user components. Although Git integration needs some further development, it enables teamwork in the same project with the "Versions."
Static and monolithic, it will show its limits when running multiple concurrent jobs.
Github and versioning implementation is messy and broken. Don't use it.
There's not way to see/query the system resources, just wait for a server to crash due to out of memory. An admin panel would be appreciated + some env variables with updated info.
API implementation is cumbersome and limited.
There's no concept of hub and worker engine, everything happens of the same server (designing workflows and executing them). Having separate light ETL engines to run job could be better. (sort of docker/kubernetes/lambda functions).
Handling of variables is limited especially for returned values from sub components.
Some components could return more metadata at the end of their execution instead of the standard one.
Billing is badly designed not taking into account that the server is hosted by the client. Expensive.
We had several issue with migration where starting a new instance was required and then migrating the content. It was painful and time consuming also have to deal with support and engineering team on Matillion side.
CDC doesn't work as expected or it is not a mature product yet.
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.
Data modeling and SQL knowledge are still heavily recommended when using Matillion because designing a successful and maintainable data transformation requires modeling it with a developer mindset. The data transformation components are often a thin wrapper around SQL concepts, and they don't simplify life that much, IMO. An example is the window component, which cannot facilitate the complex concept of SQL windows just by exposing it to a different interface. On the other hand, low code ingestion is very good and usable, especially for GSheets, Salesforce, and others.
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.
Matillion is much easier to set up and easier to work for the team. Offers a lot more connections which are easier to set up. Environment variables make it easy to set up once and job creation is easy. We use Metadata tables to just loop through the list of tables that need to be loaded. So development is just a matter of seconds when there are new tables to be loaded.
The current connectivity is set to provide information primarily in one direction; from simpler OLTP systems to big data warehouses. Allowing the big data warehouses (e.g., Snowflake) to be used as a source for transmission elsewhere would be a big extension to the product line and provide more flexibility in architecture.
Our embedded data analysts (data analysts that sit in a team outside of the Data team) all now use Matillion to create proof of concepts (POCs). This allows them to debug logic at a component level and quickly explore ideas without investing lots of time and effort.
Since the soft-announcement of ‘Data as a product’ (a beta launch) and demoing Matillion to some of our internal customers we’ve had a huge number of requests from people to get their hands on this new method of self serving data. We’ve yet to release the full product and make a company wide announcement, but early estimates show we can expect around 10-15% of the company to be onboarded and using Matillion as part of Data as a Product. Given the Data team only accounts for around 2% for the company's employees, that’s a huge increase in the number of people using and manipulating raw data!