Matillion Reviews

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Score 8.8 out of 100

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Reviews (1-25 of 88)

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November 30, 2020
Matthew Burr | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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We use Matillion to bring together data from multiple sources - our application, Salesforce, Qualtrics, etc. - into a central data warehouse for use throughout the business in reporting, both internally and externally. Matillion performs the ETL that transforms this raw data into a structure in Redshift useful for reporting.
  • It integrates well with Amazon Web Services, like S3 and Redshift
  • It makes good use of Redshift to perform ETL quickly
  • The ability to parameterize ETL jobs with variables makes it possible to get a lot of reuse from ETL jobs
  • Integration with source control is a challenge; we had to roll our own solution to pull our Matillion jobs via its API into files we could add to source control
  • It can be a challenge to avoid conflicts when multiple people are developing jobs in the same project
  • It's only available on Redhat flavors of Linux (e.g. Amazon Linux, Redhat, CentOS)
Matillion is really well suited to environments using Redshift or Snowflake, and that rely on Amazon Web Services. It is also well suited to scenarios where you need to perform a lot of similar ETL tasks with small variations that could be parameterized. It's great if you want to get up-and-running quickly, and don't want to spend a ton of time in configuration and setup; you can get going very quickly out-of-the-box. It would be less appropriate in on-premise scenarios, where all of your data is stored on-premise. If you don't use AWS, you won't get as much value out of it. Also, in environments with large teams and lots of developers modifying jobs simultaneously, it can be a challenge to coordinate work and manage changes.
It's very easy to use because of its graphical nature. Ultimately, it is a wrapper for activities you could do using SQL, Python, and AWS services, and by presenting those activities in a simple interface it makes it easier to perform them without having to worry about a lot of extraneous details.
We were able to get up and running with Matillion within a month. With no real training in the tool, we were able to develop useful jobs and an entire job framework on Matillion within that month. As we got more familiar with it, within three months, we were able to take advantage of additional features to create a framework where bringing new data in from our application is as simple as adding an entry to a table.
We don't have a particularly large volume of data (less than 200GB), but as we've added more and more data, we are able to fully reprocess our data in about 2.5 hours (vs. over 6 hours it used to take to incrementally process our data in our old ETL system). We're not even using the Enterprise version of the product; if we did, we would get even greater scale.
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 Matthew Burr's full review
November 24, 2020
Sudarshan Kothari | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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iQuanti is a Digital Marketing organization, which drives strategic decisions based on a data-driven approach and for this we use Matillion as our ETL solution tool for our organization, which is consumed by our Data Management team. We have large numbers of the digital platform from which we report and analyze the performance. Matillion helps us to automate most of our reporting needs by providing connectors to digital platforms like Google Analytics, Google Adwords, Facebook, Bing, JIRA, Google BigQuery, and various Data source connectors like Postgres, SQL, MongoDB along with AWS support as SNS, S3 etc, which provides lots of flexibility in today's world.
  • Supports a wide variety of digital platform connectors, which could be helpful for any industry working to automate any of their reporting needs.
  • Support for AWS technologies adds to greater advantage.
  • Takes up unique functionality provided by the database into account which is very helpful.
  • Also provides direct SQL query feed-in option for any migration of existing solution.
  • With the added functionality available in Matillion, the understanding to use complex features becomes challenging for a new development team.
  • Updates are regularly provided by Matillion support team but then they fail to specify the release document, new features or updates carried out in each update.
  • Validation failure in scheduled jobs is sometimes encountered without any reason or proper RCA.
  • Complex user management flow.
Matillion works best when automating the workflow from the various digital platforms. It provides the best use-case for automation of any dashboarding/reporting requirement where data is stored in one of the databases and updated regularly. Matillion might not be the best use-case for core ETL operation as it lacks stability for enterprise (but is improving to high standards with every update).
Matillion providing underline features of the database and unique functionality in connectors, increase our day-to-day work solution using the tool. For example, Snowflake database supports for JSON file system and thus using Matillion with Snowflake offers to flatten as an additional component is exceptional. Another example: Data could relive under FTP, sFTP, Cloud system (S3), databases and matillion have support to all, thus increasing the usability.
Matillion being ELT cloud tool offers easy to implement which requires an AWS EC2 instance and licensing.
Training on Matillion and getting the development team on-board was quite a challenge. Would require immense experience to explore the core functionality of Matillion. Matillion was running with-in a week time after acquiring a license but for the development team to work efficiently it took a couple of weeks.
Scalability is only one of the things which we need to keep back of our mind. While using the product for more than 18 months, we have expanded our team from 2 users to 10 users and increased utilization, Matillion performance is not par with the expectation and maturity. We always encounter the Matillion broken or cool-down period too long.
The support team was always very helpful most of the time, only have faced few issues because of time-zone difference when solutions are required immediately and I couldn't reach to anyone. In most of the cases, the support is exceptional.
Read Sudarshan Kothari's full review
August 14, 2020
Britton Gray | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
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We use it to take data from many sources and ingest them into our data lake. We then use Matillion to orchestrate transformation jobs on that data to eventually land it in our data warehouse.
  • Variety of connectors
  • Graphical interface
  • Source control integration
  • Some connectors have significant limitations (web services, NetSuite)
  • Runs out of memory easily
  • Logging not easily exportable
It's very well suited for data ingestion. Many connectors and loop components particularly make it easy to grab lots of data in a source system programmatically. Python scripts make it extensible. It's not as good for modern data warehouse ELT - you can use it as a "traffic cop" in those situations - but is it really work so much money per hour at that point?
Pretty good UI, especially for a completely web-based system. Source control integration is a bit clunky at times, but overall makes code versioning and branching much more simple, since it's largely within the same context as the rest of the code. Finding some core settings can be difficult - they're just not where one would expect them to be.
We were able to get up and running very quickly. This is really Matillion's strength. Once we got past the peculiarities of setting up our environment, we could connect to data sources right away and ingest data into our data lake. Putting on more pieces after that was easy.
That's the beauty of a cloud-based solution, right? We can scale up if needed, although the cost jumps pretty significantly. And we can always add more developer nodes as needed given source control integration.
Read Britton Gray's full review
August 13, 2020
Hyrum Ward | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Matillion is being used by Data Engineering, which is a part of the Purple Analytics department. No other departments are using it. Matillion helps us automate our data pipelines. It gives us a platform on which we can schedule Python scripts, and provides an easy workspace where we can design and implement our workflows. The Matillion server is easy to maintain, which was a problem before we switched from our previous platform. Matillion makes it easy to perform ETL operations on our data, whether it starts in our data warehouse or not. It gives us plenty of flexibility in how we design our jobs. And it's super easy to use.
  • The workspace is drag and drop, which makes it intuitive and easy to use.
  • Server creation and management is robust. We haven't had to worry too much about it once we got it created.
  • The job scheduler is very simple and intuitive.
  • Matillion allows you to run Python, which grants almost unlimited flexibility, even without using any other components.
  • The Python script component needs a friendlier window in which to edit your scripts. The script is not searchable, and tabbing is frustrating.
  • The High Availability server configuration was not working for us. It was allowing duplicate jobs to run, and causing a lot of confusion in the scheduler. Love the idea, but the implementation fell short.
  • Better alerting around queued jobs would be nice. Sometimes jobs start queuing and nothing runs. Usually this is the result of a badly written job, but it would be nice to get alerts.
Matillion is a one stop ETL shop. It is extremely flexible, and has been able to manage anything we've asked it to do so far. We love it for any API calls that we want to make. It is also very useful for transforming data that is already in our warehouse.

Matillion might not be appropriate for a very small startup that is relying on free software. Airflow is a better choice in that scenario.

For large datasets being pulled from third parties that have complex schemas, Matillion can be a lot of work. It is possible, but there are other tools that specialize in that. Fivetran is a tool we use in tandem with Matillion to great effect.
Matillion is drag and drop. It is extremely flexible, intuitive, and easy to get started with. Python scripts allow you to do pretty much anything you need. If you don't know scripting, there are widgets that can do what you need as well.

Scheduling a job is a few clicks. Couldn't be easier.
We tested Matillion for about two weeks. By the time we were done testing, we were writing transformations. At that point, we were extremely comfortable with the tool, and we successfully migrated all of our existing code over to Matillion in a few weeks. The learning curve for Matillion is shallow.

Our entry level Data Engineers don't need any hand holding with this tool. A very quick demonstration is generally all that is required. After that, the tool is intuitive enough that even a beginner can use it.
Generally, Matillion allows you to scale the size of the servers to fit your needs. It could be easier to increase the size, but it also could be that I've been spoiled by Snowflake in this regard. Having said that, we've already had to increase the size of our server, and did it without too much trouble.
Read Hyrum Ward's full review
October 06, 2020
Adel Helal | TrustRadius Reviewer
Score 3 out of 10
Vetted Review
Verified User
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We still have an old-fashioned process in our monolith environment to extract-transform-load from a SQL server data store. Currently the monolith is the main product of the CM brand and is still being heavily maintained. The way we are moving forward is to "unlock" the data from its current storage in order to have better analytics using event streaming. For the time being, the only way we are able to extract large amounts of data from our relational SQL data store is using the ETL process through Matillion into our data warehouse solution in Redshift.
  • The easy-to-use GUI makes it easier for our team to pass on the knowledge and upskill engineers on our ETL processes.
  • The feature set is rich with many options to allow us to try different ways to transform our data without having to code.
  • Many different integration points allow us to plug straight into services like SQS to help us communicate with our own internal services.
  • Matillion does not scale well. It has a hard limit on the hardware / EC2 instances it can use. Most of the time that does not provide enough parallel processing for the millions of records we want to transform.
  • It is expensive considering the infrastructure cost is added to Redshift costs, so the overall value for analytics is something we are constantly challenging.
  • Constant Java heap space errors, again this is because of hard limits on EC2 instance hosting.
Essentially Matillion is a nice tool to build a bunch of Redshift queries with added benefits of having a range of integration features. Considering the annual cost is almost equivalent to a junior engineer, we could spend the time to manually write the Redshift queries we need to transform and load our data that can run on our own microservices, and scale them as we see fit so we aren't faced with the hard limits of EC2 instance sizes. We also wont have to worry about hitting Java heap space limits since we can focus more on the Redshift query/transforms optimizing.
Extremely easy to use, extremely well documented, very familiar workflow layout to other comparable ETL tools out there, very interactive drag-drop features...
From the moment we bought it on AWS marketplace to connecting to our monolith database it took a matter of hours. Deciding the Redshift table structure we needed for storage actually took longer than the actual setup time! That is a definite plus for anyone who wants to get up an running as soon as possible.
That is the one sticking point we have with Matillion. We have hit its hard limits and constantly face Java Heap space errors. Our recommendation from the support team was to spin up another new Matillion server which is a separate cost entirely, making it equivalent to buying two separate products off the shelf, rather than incorporating it into the one solution. One Matillion server is expensive enough, two is not feasible.
Read Adel Helal's full review
October 05, 2020
Dan Reiland | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Matillion is primarily used by our Data Engineering team to produce highly curated datasets for use by our clients. We transform extensive volumes of client and vendor data into a highly clean and valuable artifact. Matillion is our primary ELT tool and has displaced a number of other tools previously used by our organization.
  • Clean interface.
  • Features.
  • Diversity of supported data warehouse platforms.
  • Ability to rapidly onboard new users.
  • Support can be slow to respond.
  • Minor UI irritations.
  • Upgrade process is onerous, often requiring manual intervention to successfully complete.
  • Git integration needs improvement.
Matillion is an excellent fit for organizations that have expertise (and data) in one of the supported engines (BigQuery, Redshift, and Snowflake). ELT workflows are easy to implement and automate against data of varied size. The UI successfully hides the complexity of SQL for users not well verse in it, but provides expert SQL users the ability to implement complex transformations not easily achieved with the standard components.
Matillion has a clean and consistent interface that is readily approachable by anyone experienced with a visual or flow based ELT tool. The ability to rapidly onboard new users, reaching productive output in a few hours, is a key part of the value proposition. That it is usable by a wide range of skill levels is testament to the thought put into its overall design.
Our initial POC demonstrated that migrating to Matillion was the logical next step for our organization. Our expectation had been six months to migrate our production loads from legacy tooling to Matillion. We were able to migrate complex production workflows, validate the result, and promote the Matillion generated artifacts in less than two months. Processing time for each workflow was cut from three days to a few hours. The productivity gains were stunning. Team members have been freed to focus on deeper and more valuable work as a direct result of our implementation.
Matillion, because it relies on the computing power of cloud data warehouses, scales effortlessly to process massive data volumes. Scaling user count is the only limiting factor to growth, however, Matillion provides a number of licensing options to suit the needs of a single department to an large scale data engineering practice.
Read Dan Reiland's full review
October 01, 2020
James Hull | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Matillion is our ETL tool to populate our data warehouse running in Google BigQuery. In conjunction with Google Data Studio, we have a daily process used across the whole business with up to date financial, commercial and project data. It is managed by the Technology Team but the whole business sees the results.
  • User friendly.
  • Build complex workflows visually.
  • Support is good.
  • Easier email integration to mail out results.
  • Version control of jobs.
  • Ability to use external APIs to push data not just pull.
Well suited - Gathering data from multiple sources to store in a central repository.

Less suited - Pushing output other than CSV or writing to tables.
Very visual, drag and drop etc but still some complexity there. Some of the structure is not entirely obvious (e.g. projects, environments etc) so it might not be being used as effectively as possible.
Used the trial edition initially and was able to set up an ETL process for production, which then started populating real reports in 6 weeks. Previous process was quite manual, a combination of Alteryx and Excel which took a lot of time.
Read James Hull's full review
October 01, 2020
Chandana M | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Matillion has been used to clean, integrate and transform data from multiple sources are prepare data for data visualisation to answer key business questions at our organisation. Our business problem was finding an ETL solution as we moved towards Cloud data warehouse. We right away knew Matillion is the best fit for us.
  • The API component is very powerful. At our company we integrated Google Analytics and custom APIs.
  • Easy of use. Transition from an SQL developer to learning Matillion takes less than a day!
  • Last but not least is the amazing support team! We tried to integrate Elastic Search using API into our warehouse but had many issues. Finally realised that Matillion has an ES component. But the journey wasn't easy either. We had weeks long conversation with the support team in resolved one issue at a time and they in fact shared their sample server for us to test on! Hands down! They were very keen on resolving our issue rather than just giving out suggestions. And they sure did!
  • Large complex workflows can exhaust Matillion's memory. If they can work on this, it will save us memory and time to create multiple staging/temporary tables.
  • It would be great if the SNS component can also include the error message when a job fails. So, as a user I don't have to login to Matillion to check the exact reason for my job failure.
I would 100% recommend Matillion for any business who is looking for an ETL solution for their cloud data warehouse especially Snowflake. What worked for us with Matillion is the ability to integrate our external sources (Elastic Search, Google Analytics), transform, automate and create a single data source for further data visualization.
I can say Matillion can improve on a few things as answered in the previous question. But, I would still rate it 10 because I am completely dependent on it in my day to day activities. One of the reasons I took up a job is to be told in the interview that they were intending to purchase Matillion and they did!
Overall, Matillion is quite easy to use for any one with basic understanding of SQL. Can integrate external data sources with simple setup. For me, helps in answering all the business questions from my stakeholders with Tableau as data visualization tool.
Setup took about 3 or 4 days. And I was able to begin transformation process right away. All in all, less than a week!
Read Chandana M's full review
October 01, 2020
Paul Cebulski | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
We started using Matillion a couple years ago across all areas of our business. We use this tool to gather data from various data sources (SQL, Google Sheets, API calls, etc.), and transforming it and eventually outputting to our data warehouse for data visualisation.
  • Data transformation.
  • Pulling data from various sources.
  • Speed of data transformations.
  • Automatic jobs backup.
It's perfect for pulling data from SQL server no matter of the amount of the data, recently we have pulled 14 millions of rows and transformed this data in the matter of minutes. Matillion is not great to pull large volumes of data from Google Sheets.
The system just works and it works well. We can always achieve our objectives using the system.
On Google Cloud platform you just start a VM directly from Marketplace, it took us 5 minutes to start the instance.
Read Paul Cebulski's full review
September 08, 2020
Viswanath Manne | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Here at ReportGarden, we use Matillion to fetch data from multiple databases and construct a compact and easily referable database using various data blending techniques with the help of various blocks like Python script, SQL, joins, etc. We use the combination of SNS and Lambda to make sure tasks run seamlessly every day and to stop EC2 instances once tasks are done, which is of great use in saving unnecessary costs.
  • UI is very simple to understand and documentation complements it very well.
  • Able to mail logs for processed and errors in daily tasks.
  • Having a detailed log of daily tasks maintained in the UI is a good addition.
  • Slightly over priced for a startup.
  • I feel support is a bit slow.
  • Not all the libraries are installed in Python, would be easier if we could install them from the user side.
It would suggest Matillion for any medium level startup that is dealing with huge data and multiple sources of data, as it is easier to pull and push from/to multiple databases. Matillion might be a costly solution for early startups in my opinion, but if you are a huge data-based company, I would suggest you check out Matillion.
It is for sure a good ETL tool that is ready to use without much deployment need. Matillion is getting better by adding more and more features.
Almost everything is up and running, one needs to figure out Lambda and SNS stuff to maintain scheduled tasks.
It is pretty scalable, you just need to weigh the run speed of your task verses cost when you scale-up the machine.
Read Viswanath Manne's full review
September 06, 2020
Dan Dow | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Review Source
We use it as our ETL tool across the entire organization including marketing, finance, customer service, and support. We pull data from our proprietary product use as well as Salesforce to merge data about our product use, customer service, employee time, billing and finance information, and process handling in our AWS cloud core.
  • Hides the SQL code
  • Creates an intuitive UI for data flows
  • Interfaces with many third-party platforms
  • Moving data through a flow can be very tedious.
  • Errors are often obtuse and hard to pinpoint.
  • They used to auto-complete typing table names, but they removed that, making point number one all that much worse.
Matillion is really good at giving you a visual reference to look at data flows. The layout of orchestration and transformation jobs is intuitive enough that I have been able to show people who have never coded SQL in their lives how to use the product and they can understand how the data is moved and how it is transformed.

However, say you need to add a field to a top-level table that you have not written an extract for. Moving that new field across each transformation job for each intermediary fact or dim table can be very time-consuming and tedious and it seems it should not be that hard to add a feature--"populate through transformation as {field name}" should be possible.
It's easy to understand when everything works, but difficult to diagnose when it doesn't.
A couple of weeks were enough to get me comfortable. A couple of months were enough to make me feel like I understood it intuitively.
The block is really how tedious it can be to publish a field through transformation to transformation from extract to publish.
Read Dan Dow's full review
September 03, 2020
Aleksa Topalović | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Within the Business Analytics team, Matillion was introduced as a new ELT tool after team extension. In that time, we had four active BI developers and the main need was a centralized project repository that will make the daily workload easier. We are using Matillion for development and maintenance of the data warehouse and integration of more than 20 data sources.
  • Tailor-made for Redshift, including most of the features like Spectrum.
  • Cloud-based solution with centralized project repository, easy team collaboration.
  • Built-in scheduling and monitoring, everything in one place.
  • Awesome support, the short time from raising a question to the solution providing.
  • Documentation is not always updated in time when new stuff is implemented.
  • Some connectors already implemented still have some bugs which make them useless for our use case.
  • Small fine tuning still missing, in terms of covering all use cases of some connectors.
If you are in a situation that includes Redshift, a whole AWS platform and developing modern analytical solutions, Matillion is the tool that could improve your whole platform. Instead of writing complex scripts you have possibilities to design the whole process that is also understandable for business users also at the first look - implementing business logic is faster. Using the power of the Redshift you have an impression of instantly doing a job. Matillion also covers some other use cases like processing and integrating unstructured data so that all business needs could be satisfied. At the end synchronization with other tools also works fine so the whole BI process from data to insights is shorter.
I'm really satisfied with the whole Matillion/AWS/Redshift environment. For me, it was easy to switch to an ELT approach and to rebuild all jobs. After just two months, I felt familiar with the tool itself and built some complex stuff really fast. I measured my experience, and after working with different enterprise BI tools, I can say that Matillion is really user-friendly and useful for many business cases.
After just two months we had complex processes running on a production environment. And now after six months, we became really proficient users of Matillion which are also taking care of optimization improvements and not just a development.
For most of users support can provide needed help. And it is always in a really short time frame.
In some really advanced cases there was necessary to include highly tech people and there was a lot of back and forth.
Because I have a case that is not resolved for more than a year and the bug is based on initial bad implementation (Google AdManager component) I would suggest that sometimes don't rush to publish something that is not ready.
Read Aleksa Topalović's full review
August 31, 2020
Eduard Matei | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use Matillion as the main ingestion tool within the business intelligence department.
We use it to ingest data coming from different legacy systems, outputting data in the cloud datalake solution.
It's really easy to learn, configure and spend less time ingesting the data and more time getting insights from the data.
  • Python integration.
  • Easy to set up, back up and restore.
  • Scalable, works for any and all file types.
  • Python libraries.
  • Bigquery metadata.
Matillion can be designed to ingest a multitude of file types through an automatic process, allowing less technical users to create and control the end to end process of ETL.
API queries, python code, bash script processing, Matillion can be used to solve complex problems, creating an easy-to-follow process.
Regardless of one's needs, Matillion has different tools to address even the most complex issues that can occur in the ETL process.
It's easy to use and get along with, whether it is using the drag and drop of built components or creating your own from scratch.
It takes less than two hours for Matillion to be up and fully operational. If creating a backup instance, all code and settings can be downloaded as json files and then reuploaded in the new instance.
We have been using the same instance of Matillion from when there were about 20 jobs created. At the moment, we are creating about 10 new jobs per day, with a couple of hundred jobs running every day, with performance not being affected at all.
Read Eduard Matei's full review
August 29, 2020
Antonello Supino | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
We use Matillion for Redshift as to orchestrate data pipelines as our main data integration tool. We are pleased by the usability and reliability along with multi-user interface, git integration, and lots of plugins. Matillion has helped us to have strong data pipelines that we can monitor to run hundreds of jobs a day. Support is also proactive. It's overall a great ELT tool.
  • Visual flow of ETL
  • Integrations with GIT
  • Multi-user editing
  • Shared components portability
  • Having other databases other than Redshift as targets (such as MySQL)
Matillion is great when you want to integrate the data into a single DWH such as Redshift or Snowflake.
Also the task history is very clear along with all the detailed reporting on each single operator.

When you want to export data back to MySQL or to other data sources it is quite difficult, that's an area of improvement.
The interface that can be edited by multiple users at the same time makes it very usable. It's not a script based tool but it can be so it is great for all levels of professionals. The interface is responsive and there is no need to install desktop clients which is great. Sometimes after upgrades, we have to manually clear browser cache which is a bit annoying.
We got Matillion from the AWS marketplace, and we got up and running within less than a week. Starting the server was simple, then we had to do a bit of configuration and integrations with our servers adding the credentials, git repos etc. The experience was really good as Matillion blends perfectly within the AWS ecosystem.
With Matillion, we push most of the computational load down to redshift so it's not an issue to scale up. Perhaps the instance sizes available may not fit if the load becomes too high, however, we did not face any of this issue. We are using multiple projects with multiple users and all is going well.
Read Antonello Supino's full review
August 26, 2020
Andy Lai | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Review Source
I use Matillion for one project. I use Matillion to solve my data loading issue because Matillion supports many data sources. I use this to aggregate all the data sources to my data warehouse. Before using Matillion, I needed to write my own data loader, which was very time consuming and created a lot of stability issues. By using Matillion, I can load my data within an hour to my warehouse.
  • Quick to set up
  • Tailor-made for data warehouses (Bigquery, Snowflake, Redshift)
  • Graphical UI to connect all the modules
  • Easy to learn
  • Customer response time needs improving
  • SAAS model instead of charging hourly
  • Lack of documentation
  • Versioning logs not updated
Matillion is suitable for a use case that needs to support multiple data sources or where you have much data to load into the data warehouse. It is not suitable for a budget-tied project, as it is quite costly if you just deploy it to use it as a data loader.
Matillion has a graphical user-friendly interface, you can drag and drop to control the details of the component and connect to others. The version stays updated and you can easily update to the latest version. It provides an easy way to set up from the cloud marketplace. It is easy to schedule jobs.
It took me about a few days to study those advanced features, due to not much community support, but I still was able to get support from Matillion. For those standard features, you can run it maybe within a few hours.
Because Matillion uses instances to run the service, I think in terms of scalability, it should be within computing power only (CPU power). This is in comparison to those services that run serverless, which you don't need to worry about the CPU usage.
Read Andy Lai's full review
August 16, 2020
Daniel Zielinski | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
We utilize Matillion to transform data from multiple source systems into a cloud-based database. Our department is the sole user of Matillion and is managed exclusively by our team. Matillion allows our organization to consolidate our various data sources into a singular environment where our dashboard tools access data to provide valuable insights into the various units within our organization.
  • Connection to numerous data sources
  • Validation of objects and components
  • Ease of use to schedule run-times
  • Variable driven code development
  • Documentation examples
  • Speed of processing
  • Requires upfront investment in design of system processing.
  • Assistance with upgrades
Matillion is perfect for our organization since we are migrating to a more hybrid cloud-based architecture. I especially appreciate how changes to code are reflected immediately and can be seen by all developers instantaneously. This is of particular importance when employees are working remotely. The data that we ingest is processed twice a day, which is ideal for our organization. Any company that wants to invest in Matillion needs to determine and test the frequency of data ingestion.
In previous organizations and projects, I utilized Microsoft's SQL Server Integration Services (SSIS) to perform all data transformation. I found the transition to Matillion to be much easier and flexible to fit the needs of our organization. The key benefit that we experienced with Matillion is the reduction in the amount of time required to deploy changes and work through modifications to the code. Being a cloud-based system, it is very easy for all of our team members to access the most recent code and not have to concern themselves with source control problems.
Matillion was already implemented at our company and any time requirements to learn the tool was based on my ability to translate my previous ETL coding experience into Matillion's structure. Our team inherited an existing ETL process built-in Matillion and were able to add/modify/delete new objects and components within several weeks of having access to Matillion. It was necessary to understand the business rules prior to making any changes, but the tool itself is very intuitive.
The architecture of our enterprise data warehouse drives the scalability of the application. It is paramount to invest time and resources into building a proper data warehouse. By having a well-designed data warehouse, our ability to add new input data sources to our system allowed us to take advantage of Matillion's flexibility.
Read Daniel Zielinski's full review
August 14, 2020
Avishua Stein | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
It's being used by our BI team, to ingest data into our Data Warehouse and distribute primarily to clients via Tableau, GSheets, emails, SFTP, etc. We also use it internally. Almost all our usage is for loading data although we use some transformation components when bringing in semi-structured data. Our team supports the entire organization.
  • Move large amounts of data quickly from various sources into Storage/Data Warehouse. This is important for any ETL tool.
  • The built-in API connectors are very useful and are usually granular enough to save us loads of development time.
  • Matillion usually responds to support tickets in a day or two.
  • A couple of times, we've had components stop working. We were basically told to keep checking the release notes for fixes. It would be nice if they prioritized maintaining existing components over developing new ones.
  • It can be hard for multiple users to work on the same job.
  • Most of their documentation is good, but sometimes their support pages link to missing docs.
Matillion is well suited for ingesting data from multiple sources, especially when the raw data is being stored in cloud storage. We have AWS Lambda connected to S3 and Matillion so triggering ingestion jobs when data is delivered is simple. Sending alerts for job failure or success to Slack/email is easy.

Matillion is probably less appropriate for scenarios where multiple people are going to be working on one job.
The GUI is simple and easy to work with. Components connect to each other and you have options for how to handle successes/failures. Managing schedules is easy in the GUI and doable in bulk through their API.
It has scaled well for our usage, but we have some worries about what will happen when we begin working with larger datasets (10TB for one table) each day.
Read Avishua Stein's full review
October 01, 2020
Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Matillion is being used by the BI Team to orchestrate the population of the Data Warehouse. The Warehouse, in turn, is used to produce a range of reports across all business functions in the organisation. We use Matillion to ingest data from an application database (hosted in AWS), Salesforce, Netsuite and Google Analytics
  • Matillion's UI makes it easier to understand the flow of data in your data pipeline.
  • Custom Python scripts make it easier to manage and manipulate variables and also to create custom functions (e.g. we use one to post messages to Slack when jobs have failed/succeeded).
  • Handling failures in processes is straightforward.
  • Passing variables between jobs (orchestration or transformation) feels a bit clunky. It can also be frustrating that you can't pass a variable back up to the calling orchestration job, you can only pass it down to child jobs.
  • It would be great to have some kind of debug mode, through which you're able to 'step through' the various tasks in an orchestration/transformation job.
  • Matillion's generic API functionality is difficult to understand. Things like handling pagination and rate limiting are complex. Although I understand improvements have been made in recent versions.
Matillion is great for handling bulk and 'delta' loads to your warehouse, i.e. updating the warehouse with only those records updated since the last execution of the pipeline. The components and Python scripting provide a huge amount of flexibility in what the pipeline does, but you do need to have the expertise to know how to implement it properly.

Matillion has been less good at extracting data from APIs. The functionality was found to be complex and it was unclear how to manage things like pagination and rate limiting in API calls.

I find Matillion very intuitive and quickly got to grips with it. I have found that developers with a more purist coding background have struggled to get to grips with it more, particularly in relation to managing variables. For me personally, this has not been an issue and the online documentation has been excellent in digging deeper into specific functional areas
Within a month of getting Matillion we had an established pipeline that was extracting data from the company's application database and updating the corresponding data in the warehouse. Additional data source (e.g. Salesforce and Netsuite) were quick to implement (c. 2 weeks per data source) including any relevant testing.
We've not had a need to scale past the smallest version of Matillion and so I can't comment on this.
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January 05, 2021
Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
We use Matillion for Redshift to ETL data from various sources into our data warehouse which is used across the organization with the desire to share it with external users in the market too. It allows us to seamlessly integrate within the AWS stack and even outside so we don't have to think twice about whether we will be able to ingest data from any new source.
  • Components available to do work with any source
  • Ability to connect to sources without preconfigured component with extensability
  • Able to kick off jobs from SNS, SQS
  • There are so many options that the learning curve could be long for a newbie
  • Can only parallelize the load in 16 partitions so it can't make use of parallelism of Redshift
  • Menu items for admins may not always work and would have to resort to shell scripting (offered)
ETL development can happen without much programming using graphical ETL dev tools and Matillion makes it simple. If data movement is what your organization needs then Matillion is your tool. If you need heavy programming for most performant or reusability then Matillion is not very useful.
Matillion makes it easy for novices to quickly perform data movement and create transformations.
We have a 24 node Redshift cluster but COPY can utilize only 16 multi-part upload due to Matillion which is annoying.
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October 05, 2020
Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Matillion was used in order to create a Redshift Data warehouse solution. Where the Data warehouse followed the Inmon module and on top of that is a Data mart with multiple aggregations.
It was the product of choice for BI needing to move from an old ETL tool and DB.
  • Ease of use.
  • Multiple source feeds.
  • Very good integration with Redshift.
  • Provides a lot of flexibility with Python scripting.
  • Target should always be Redshift or it gets complicated.
  • Python scripts not in Jython don't comply with commit/rollback blocks.
  • Excel input component is too slow and you are better off processing it in Python.
It is well suited for anyone needing to create a BI (ETL/ELT) solution on Redshift.
It is very easy to use and provides a lot of components similar to other ETL tools.
Please note all processing is pushed down to the DB (think ELT).
The only downside is some of the under the hood logic is sometimes not acting in the documented/desired fashion, requiring workarounds.
It is easy to use and provides a lot of connections, components and ability to adjust things such as API profiles.
To migrate an existing ETL solution from an old product to Matillion, will require a compete rework with some redesign.
Estimate depends on the complexity required. Would estimate anywhere between 3-9 months.
The EC2 instance size determines the licensing costs and in our case we found it to be stable but not overall scalable.
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October 05, 2020
Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Matillion is being used only by data engineering team, which is part of Data Science department. It's our ETL tool to populate our data warehouse on Snowflake. Orchestration jobs are running on a daily basis and are basis of BI reports, which are used across the whole organization. Except that, it's used to generate new feature sets for data scientist for modelling.
  • Super easy to use. Anyone can start using it with very little previous experience.
  • Lots of connections available to fetch data from most popular sources available.
  • Great UI.
  • Not very much scalable. Sometimes there are server shutdowns when it goes out of memory.
  • Speed of processing can be improved, but not bad.
  • SQL compilation errors are very vague and there is no way to understand what the actual error is. It steals extra time to debug.
It's perfect for the teams who have very little experience with ETL and want to start creating pipelines with very little effort. It's also a very good solution if pipeline complexity is simple and does not require too much scalability.
User experience is very intuitive and takes no time to get hands on it. There are lots of documentation which go pretty much into deep details.
Within 2-3 weeks from starting Matillion, we had our first simple pipeline in production: extracting data from our operational database and putting it into data warehouse.
For the first year, scalability for pretty good. We didn't have any issues with number of transformations or overall speed of jobs. However, after we got more experienced with it and started to create more complicated jobs, scalability suffered a lot. Our jobs exhausted memory many times and we couldn't find any way to solve it. Only way was to get to the highest subscription level, which required more money investment and we didn't go with it.
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October 01, 2020
Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Matillion is being used primarily by the Analytics team, which is part of engineering, to orchestrate data transformation jobs from our application databases to our data warehouse.
  • Extremely user friendly.
  • It has many different job components already built.
  • Data loads very fast.
  • Makes organizing data jobs very easy.
  • The Git workflow could be enhanced, the UI is confusing.
  • The text editor for writing SQL is too basic and could be enhanced. Because of this, I often write my code in a separate text editor and copy/paste.
  • Great if you have many developers working at the same time.
  • If you want to build a pipeline quickly and schedule it right away.
I like that all of the components have descriptions and examples with links to online documentation. It makes using everything that much better.
About 1 week which included hands on and looking at online documentation. Part of the delay was just getting access to internal company data.
Our company's data is growing and we've had no issues with consuming more and more data.
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October 01, 2020
Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
We use it as an ETL tool to process data from Redshift and other software, create tables in Redshift, and transform data as it goes.
  • Fairly customizable: can piece together components like Legos.
  • Responsive support and good standard documentation.
  • Deployment, upgrades, and migration are very smooth.
  • Many connectors, APIs, python scripting.
  • Bugs in Zuora API; no SFDC push component.
  • Few “best practices” like Legos without instructions.
  • Next to no information in "google land" or stack exchange on gotchas or tips. Too much help in video form and not enough searchable text.
  • Pay AWS and Matillion by the hour.
Good for highly customizable jobs, takes some learning curve but then very flexible.

Less appropriate: the Zuora API has some issues and we had to build the data pull from a bunch of basic API components instead of the proper highlevel one.
It's a mixed bag of usability: takes some getting used to, learning curve (not sure why some components can be used in "transformation jobs" but not "orchestration jobs" ?) but once you understand it, it's fairly usable.
Took 1 day to deploy, get logged in, and connect to Redshift.

Took 1 month to make it useful in production, and 9 months in, I'm still learning features and quirks of Matillion.
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September 16, 2020
Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Matillion is used by the data engineering team in order to build most of our data warehouse.
  • Development speed
  • Number of connectors
  • Load/unload speed
  • GIT integration
  • CI/CD integrations
  • Failure messaging
Matillion is well suited for small teams that require fast and graphic development of dataflow jobs. Not so great with its support (it might take several days to get any response and that is not good when pipelines are business critical). When it comes to orchestration of big teams working with GIT, it can become a nightmare: single-lined big jsons not specifying what work was done.
Matillion is not as robust as SSIS, but it has nice features.
It has to be taken into account that the learning curve is high. It is a new tool that requires some understanding about its myriad of functionalities. However, jobs and ETLs can be done fast after getting a grasp on its components.
It's not horizontally scalable, not distributed, offers few EC2 choices.
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September 08, 2020
Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Used by developers and business analysts in the IT department to extract and transform data into our Snowflake data warehouse. We have different data sources after a company merger and needed combined reporting. We also needed to pull data from third-party data sources such as Xero, Google Analytics, and so on.
  • Breaks jobs down
  • Graphics Interface
  • Has lots of AWS documentation but not as much Azure.
Matillion is very well suited to connecting to online data sources, as it has an array of connectors that are already set up with third parties. It is easy to get started and can be hosted in AWS or Azure. You only get charged per hour so it is cost-effective if set up correctly.
Works well for people that like to visualize things, as each job and transformation is displayed on a graphical interface. A job or transformation can be a large set of tasks or can be broken down into individual ones so that diagrams do not get too complicated and out of hand.
Proof of concept took a couple of weeks. Getting access to private SQL databases was a little fiddly as we needed to install some drivers which we would have expected to be installed as default, but once up and running, we have had no issues. Tweaking firewalls settings to allow Matillion access took a while.
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Feature Scorecard Summary

Connect to traditional data sources (86)
8.3
Connecto to Big Data and NoSQL (53)
7.5
Simple transformations (87)
8.7
Complex transformations (85)
7.6
Data model creation (34)
7.6
Metadata management (41)
6.6
Business rules and workflow (75)
7.4
Collaboration (75)
6.8
Testing and debugging (77)
6.2
feature 1 (4)
8.2
Integration with data quality tools (23)
6.6
Integration with MDM tools (21)
6.3

What is Matillion?

Matillion is data transformation for cloud data warehouses. According to the vendor, only Matillion is purpose-built for Amazon Redshift, Snowflake, and Google BigQuery enabling businesses to achieve new levels of simplicity, speed, scale, and savings.

Quickly develop custom Transformation jobs by combining Filters, Joins, Aggregates, Calculators, Ranks, as well as more complex transformations such as Rankings, window calculations, and change-detection by dragging and dropping these onto a canvas GUI. Use Orchestration features to automate your ETL workflows with scheduling, notifications and alerting, and control flow.

Once your jobs are built, run them with confidence with component level validation and data sampling. For any custom or unique needs, Matillion also has Bash and Python Script components giving you the ultimate extensibility and flexibility.

Shared Job templates, Dynamic and Grid variables, and Version control help your development teams share resources across your Matillion instance.

Enterprise customers benefit from additional auditing and permissions, automatic job documentation, data lineage and more. Learn more about enterprise features here.

Each product comes with a set of warehouse specific features that help you get the most out of the platform. See our Product Feature pages on matillion.com for more information.



Categories:  Data Integration

Matillion Features

Data Source Connection Features

Has featureConnect to traditional data sources
Has featureConnecto to Big Data and NoSQL

Data Transformations Features

Has featureSimple transformations
Has featureComplex transformations

Data Modeling Features

Has featureBusiness rules and workflow
Has featureCollaboration
Has featureTesting and debugging

Matillion Screenshots

Matillion Videos (6)

Matillion Integrations

Matillion Pricing

  • Has featureFree Trial Available?Yes
  • Does not have featureFree or Freemium Version Available?No
  • Has featurePremium Consulting/Integration Services Available?Yes
  • Entry-level set up fee?No

Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.

Matillion Support Options

 Free VersionPaid Version
Email
Forum/Community
FAQ/Knowledgebase
Video Tutorials / Webinar

Matillion Technical Details

Operating Systems: Unspecified
Mobile Application:No
Supported Countries:Global
Supported Languages: English