Filter Ratings and Reviews
Filter 144 vetted Matillion reviews and ratings
Reviews (1-25 of 88)
- 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)
- 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.
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.
- 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
- 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.
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.
- 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.
- 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.
- 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.
- 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.
- Data transformation.
- Pulling data from various sources.
- Speed of data transformations.
- Automatic jobs backup.
- 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.
- 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.
- 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.
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.
- Visual flow of ETL
- Integrations with GIT
- Multi-user editing
- Shared components portability
- Having other databases other than Redshift as targets (such as MySQL)
- 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
- 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
- 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'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.

- 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)

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.
Estimate depends on the complexity required. Would estimate anywhere between 3-9 months.
- 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.

- 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.

- 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.
Took 1 month to make it useful in production, and 9 months in, I'm still learning features and quirks of Matillion.

- Development speed
- Number of connectors
- Load/unload speed
- GIT integration
- CI/CD integrations
- Failure messaging
- Breaks jobs down
- Graphics Interface
- Has lots of AWS documentation but not as much Azure.
Matillion Scorecard Summary
Feature Scorecard Summary
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.
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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.
For the latest information on pricing, visit https://try.matillion.com/matillion-a-general-page-demo/
Matillion Support Options
Free Version | Paid Version | |
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Forum/Community | ||
FAQ/Knowledgebase | ||
Video Tutorials / Webinar |
Matillion Technical Details
Operating Systems: | Unspecified |
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Mobile Application: | No |
Supported Countries: | Global |
Supported Languages: | English |