Matillion

Matillion

Top Rated
TRUE
Score 8.6 out of 10
Top Rated
TRUE
Matillion

Overview

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.

Users can develop...

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Recent Reviews

Matillion Review

9 out of 10
July 09, 2021
I have been using Matillion since March and it has been a nice experience. It's helping a lot during our ETL process from RDS to Redshift, …
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How Matillion Differs From Its Competitors

Time to Value

We have started doing some research about ETL tools since we decided to do not to develop it a house, and after talking with some colleagues and reading about Matillion, we decided to move it forward, and that was our time schedule: Research and ETL tools try 45 days Developing our project on …
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Cost Savings

The long term savings from Matillion have primarily come through decreased work hours associated with building, maintaining and trouble shooting ETL processes.
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Business Outcomes

Matillion's connection options are robust. We have been able to connect to many different database types, as well as build direct connections with our partner APIs.
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Onboarding

Maybe two weeks to get over the initial hurdle and then we were off to the races. For the most part Matillion is very intuitive, simple, and easy to implement.
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Time to Value

My learning curve was long. Matillion is simply not like any other ETL / ELT tool out there, because Snowflake isn't like other cloud databases out there. I had to first learn a different pattern of data movement and error checking. Next, I had to learn which components to use and when. There …
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Onboarding

a full year to get fully implemented. The onboarding was immensely painful given the documentation was lite and incorrect in many places. Many patterns of use I had to figure out for myself. At the time of implementation there was no developer community and the only company contact was through the …
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Time to Value

The internal communication and collaboration than the setup of Matillion, once the EC2 instance triggered, we were able to get started with transformation jobs within 1 hour.
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Time to Value

Matillion was implemented within one week in our organization for full functionality. Our company secured its deployment within this period of time and all our programs were able to run smoothly. The learning curve was easy to master and get it run as expected within a short period of time. Our …
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Time to Value

I started reading about Matillion and after one week and talking to their support team I started my project and it was up and running after 2 weeks of developing.
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Time Saved

Yes, after I started using Matillion our ETL process became much faster and we are running the instance on AWS only 1 or 2 hours per day which means we are not spending too much money but we are having many benefits once the process is much faster now and our visualization team can find the data …
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Cost Savings

Before Matillion we used to use software created by our own company which was already old to date and it was really hard for our new employees to get in of it. Using Matillion we are saving money on training for new partners and now we have a much more organized ETL environment.
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Business Outcomes

Yes. We don't have a lot of data sources but all of them we were able to connect smoothly and import our data easily.
As an example of our data sources I can mention, Relational Databases on AWS running SQL SERVER, we have some input from S3 (text files) and of course, some excel files.
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Onboarding

Matillion's implementation was really really easy and smooth, most of my questions I could find solve questions using their forum or sending them an e-mail which was replied to within 1 or 2 days. Their documentation about all the components I consider complete and with good examples of how to …
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Time to Value

It took some days in order to figure out how to configure the environment and all the basic configurations. Writing simple jobs took a really short time to learn (hours), while in some other days I was able to prepare a full orchestrator. Parameters configuration is a little more complex and I had …
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Time Saved

We're using Matillion for ELT and processes are slower. It is a mix of Matillion efficiency and DB performance, but the total result is better than before. We are using a Large account and it is the right choice, while with the Medium size we weren't able to performe the same results and we had …
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Cost Savings

We are reading from many different sources. Extraction times are better than before and we found almost all the connectors we needed and the support helped us to obtain the best extraction performance. Matillion costs are coherent with the usage, and it is useful that we are not paying when the …
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Business Outcomes

We were able to connect all data source, but we had some issues with SAP: the native Netweaver connector do not allow to read SAP tables in join, as the 2 tables are loaded in memory and the join is apply at the end. Therefore we had "out of memory" issues with this strategy. But we could find …
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Onboarding

Matillion is really easy to use, so we were able to start writing our flows easily. The activation is still easy, but you need to have some tech skills to work with putty and install what is missing (specific ODBC etc). For these aspects we moved questions directly to Matillion's support and the …
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Time to Value

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) …
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Time Saved

Yes we did. It was possible to update the data warehouse database every 3-4 hours instead of every 24 hours. In addition, and perhaps more importantly, it provided a more stable ETL process. The previous one was prone to breaking/failing and Matillion provided a more reliable process and one that …
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Cost Savings

In terms of infrastructure costs, our costs have gone up using Matillion. Historically we used AWS DMS to clone the production database into a Postgres database. This old process, however, was prone to issues and failures, e.g. when the production database was updated with new column(s)/table(s) …
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Business Outcomes

We were able to connect to our main data sources using Matillion (a MySQL database, Salesforce and Netsuite). Other data sources, that are API based and didn't have specific components in Matiilion were more of a challenge at the time. Although API functionality has been improved since we …
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Onboarding

Onboarding was quick and relatively pain free. The team did have experience using Matillion prior to implementation which obviously helped. However, once the server was setup with the appropriate security groups to access data sources, building the Matillion jobs was straight forward and the UI …
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Time to Value

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 …
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Time Saved

Reports that took approximately 3 hours to pull together and performed on a manual monthly basis were handled automatically and daily in a matter of minutes. These manual reports were pivots in Excel and were pushing Excel to its limit (Excel would freeze). There was a lot of data being shifted …
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Cost Savings

As it was automating a manual process, the savings were more in terms of saving resource. Automating has also opened up the report to more internal users.
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Business Outcomes

We are able to connect our bespoke systems and third party systems. One that proved quite difficult was the Xero connector. Although hooking up with Matillion was straightforward, the data Xero connector was not provided the data we needed so are looking at our ETLs for this this specific issue.
Mor…
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Onboarding

The project has been stop and start over a couple of years. We moved from AWS Matillion to Azure Matillion but it is now stable and pulling various sources in reliably. Setting up success/failure notifications has been key.
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Time to Value

I watched training and example videos/seminars for about two weeks before getting an instance set up for the company. And from there, I had a usable workflow going within another week.
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Time Saved

Yes, Matillion allowed for daily schedules to provide updated data for reporting at the same time each morning. It also removed any need for manual data updates.
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Business Outcomes

Yes, Matillion allowed for connections to all SQL based data sources via JDBC, all files from FTP sites, and all REST API sources. It simplified all of the different data pipelines and allowed for more interaction between them.
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Time to Value

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 …
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Time Saved

Yes, there were absoultely increased in reporting speed that our team was able to delivery. Intially we had our own scripting tool to generate result which took 2 weeks (around 80 hours of development time + testing time) whereas using matillion we were able to complete the development in 32-40 …
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Cost Savings

While moving away from legacy system towards matillion it offered some significant cost on matillion hosting and licensing part but we were able to save some on legacy system hosting but majority of saving where on people resource which was huge cost saver for our organization. along with that …
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Business Outcomes

Our organization work in digital marketing industry and matillion do provides lots of connector related to digital marketers. Matillion was able to solve 90% of our data sources connection problem but there were few of the rarer sources which matillion din't provide the connection to. Hopefully …
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Onboarding

Our team member had already the knowledge of other ETL tool in market which made us easier to transform to Matillion as ETL tool. Initally when there were more rigrous update on tool, it took us few months to understand all the different features of the product and use them. We used to reach out …
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Time to Value

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 …
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Time to Value

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 …
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Time to Value

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.
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Time to Value

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.
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Time to Value

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 …
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Time to Value

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.
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Time to Value

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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Time to Value

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.
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 12 features
  • Connect to traditional data sources (91)
    9.2
    92%
  • Testing and debugging (82)
    9.2
    92%
  • Simple transformations (92)
    9.0
    90%
  • Complex transformations (91)
    7.8
    78%

Reviewer Pros & Cons

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Video Reviews

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Pricing

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What is Matillion?

Matillion is data transformation for cloud data warehouses. According to the vendor, only Matillion is purpose-built for Amazon…

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

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14 people want pricing too

Alternatives Pricing

What is Fivetran?

Fivetran replicates applications, databases, events and files into a high-performance data warehouse, after a five minute setup. The vendor says their standardized cloud pipelines are fully managed and zero-maintenance. The vendor says Fivetran began with a realization: For modern companies using…

What is TIBCO Cloud Integration?

TIBCO Cloud™ Integration is an enterprise iPaaS platform. It offers a drag-and-drop and API- led design approach for user-friendliness.

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Product Demos

Introducing Matillion ETL for Amazon Redshift | Available on AWS Marketplace
01:15
Introducing Matillion ETL for Snowflake | Available on Azure, AWS and GCP Marketplaces
00:53
Introducing Matillion ETL for BigQuery | Available on GCP Marketplace
00:53
Siemens Power and Gas Use Matillion ETL for Redshift from AWS Marketplace to Speed Data Analytics
04:30
Thrive Market - "Able to Deliver Better Value and Service" | Matillion ETL for Amazon Redshift
04:34
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Features

Data Source Connection

Ability to connect to multiple data sources

8.9Avg 8.4

Data Transformations

Data transformations include calculations, search and replace, data normalization and data parsing

8.4Avg 8.4

Data Modeling

A data model is a diagram or flowchart that illustrates the relationships between data

8.9Avg 8.1

Data Governance

Data governance is the practise of implementing policies defining effective use of an organization's data assets

8.2Avg 8.2
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Product Details

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.

Users can 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 ETL workflows with scheduling, notifications and alerting, and control flow.

Once jobs are built, run them with component level validation and data sampling. For any custom or unique needs, Matillion also has Bash and Python Script components to give users extensibility and flexibility.

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

Enterprise customers gain 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 to help users get the most out of the platform. Their Product Feature pages on matillion.com provides more information.



Matillion Features

Data Source Connection Features

  • Supported: Connect to traditional data sources
  • Supported: Connecto to Big Data and NoSQL

Data Transformations Features

  • Supported: Simple transformations
  • Supported: Complex transformations

Data Modeling Features

  • Supported: Business rules and workflow
  • Supported: Collaboration
  • Supported: Testing and debugging

Matillion Screenshots

Screenshot of Matillion uses an easy to follow GUI. Orchestrate your jobs quickly and simply with control data flow functionality, automating the ETL process.Screenshot of Bring your data together. Use Matillion to read and combine data across your target warehouse external storage, such as S3 or Blob. Then prepare your structured and semi-structured data to create clean data sets that can be used with your BI/reporting/visualization tool of choice.Screenshot of Develop confidence in your ETL jobs with self-validating components, sample and row counts. If a job does fail, use warehouse queue services with Matillion to get an alert to your email or Slack account.Screenshot of With hundreds of pre-built connectors out of the box, Matillion is well equipped to handle your most complex transformation needs. In case you need a bit more flexibility for unique use cases, use the SQL component to run custom scripts from within Matillion.

Matillion Videos

Matillion ETL for Amazon Redshift
Matillion ETL for Snowflake
Matillion ETL for BigQuery
Siemens Power and Gas Use Matillion ETL for Redshift from AWS Marketplace to Speed Data Analytics
Thrive Market Use Matillion ETL for Amazon Redshift to Revolutionize Their Data Warehousing Strategy

Matillion Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
Supported CountriesGlobal
Supported LanguagesEnglish

Frequently Asked Questions

Reviewers rate Connect to traditional data sources and Testing and debugging highest, with a score of 9.2.

The most common users of Matillion are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews

(1-25 of 93)
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September 21, 2022

Matillion, a nice ETL tool.

Score 10 out of 10
Vetted Review
Verified User
We are using Matillion as our main ETL tool in order to move data from our RDS databases to our DWH (Redshift); during the process, we extract the data, perform some transformations, aggregations, and so on before inserting it on Redshift, where this data is used afterward by Quicksight and others B.I tools.
  • I is really simple and good.
  • Documentation
  • Drags and Drops.
  • Performance
  • I think the price could be a bit cheaper.
I always suggest Matillion for organizations that do not want to spend money and time developing their own ETL tool, Matillion is a mature one and meet all requirements about it. It is important to check if you have any edge case and have a look if it is covered by Matillion or not.
I really like how simple it is to do workflows on Matillion and their really detailed documentation where you can easily find how to do what you need in your projects. They also have a really nice connection with different data sources, which makes their solution very trustable and complete to all our requirements.
We have started doing some research about ETL tools since we decided to do not to develop it a house, and after talking with some colleagues and reading about Matillion, we decided to move it forward, and that was our time schedule: Research and ETL tools try 45 days Developing our project on Matillion Platform: 90 days Tests and adjusts: 30 days.
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.
Score 10 out of 10
Vetted Review
Verified User
We use Matillion for all our redshift data warehouse ETL needs.
  • Run stored procedures on AWS Postgres RDS instances
  • Sync data from diverse data sources including production databases and APIs to Redshift data warehouse
  • Version updates often are not backward compatible. As a result updating to a new version requires a huge LOE.
As a drag and drop ETL solution, Matillion is efficient, easy to use, and reliable. It is well suited to ETL data into a data warehouse. It is less appropriate for running maintenance on other data sources.
Matillion overall is easy to use. Sometimes the documentation is slightly lacking, and mass delta updates are not as easy as we would prefer.
Matillion was up and running almost overnight. Our ops department spun up in an instance and we were off to the races.
Matillion has scaled well with our organization's growing needs and has easily handled very large ETL orchestrations.
June 09, 2022

Matillion review

Sonali Aggarwal | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I have used Matillion in various projects in my company for different pharma clients for Life Sciences data. We were building solutions for clients which can help the analytic users and other down streams to consume data as per their requirements. In other Healthcare Analytics Projects, we were extracting the data from different sources like Facebook and Google Analytics and building a repository using Matillion ETL. Matillion was user-friendly and easy to learn and easily integrated with AWS Redshift and Snowflake DBs.
  • Easy integration with AWS Redshift and Snowflake.
  • Pay for what you use.
  • Ease of service.
  • Constant Java heap space errors because of hard limits on EC2 instance hosting.
  • It is expensive considering the infrastructure cost is added to Redshift costs.
  • Matillion does not scale well. It has a hard limit on the hardware / EC2 instances it can use.
It is very well suited for beginners as it is easy to learn and work upon. It is also easily integratable with AWS; however, if you have cost constraints it may seem a challenge.
In ETL processes,most of the basic transformation and orchestration components are used and it very easy to learn
Very quickly, I was a fresher and was very easily able to learn
Matillion does not scale well. It has a hard limit on the hardware / EC2 instances it can use
Jay Archer | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
We are using Matillion to centralize our data to a new data warehouse. Our primary use case is pulling data from a relational database (MySQL). I've been able to implement three different flavors of data sourcing, the two primary types being a full table load and incremental data loading of changed data into Slowly Changing Dimension and Fact tables in the DW. The performance of Matillion combined with Snowflake is astoundingly fast. We are also able to hit APIs to Zendesk and Hubspot easily to round out our integration with other SaaS vendors in our stack.
  • Push down query performance with Snowflake.
  • The ability to hit any API using Python.
  • A robust offering of pre-built connectors to databases, APIs, and other SaaS vendors.
  • No user community site for experienced developers to share their patterns and help grow the dev community
  • Documentation can get stale or be changed without notice.
  • Several aspects of the product are not user-friendly, and if implemented by an experienced product/UX person it would make the product easily 2x to 3x better.
  • No ability to vote on what features are in the pipeline.
Great if you need a visual, customized, powerful data engineering and data integration platform that can do pretty much anything. I have yet to hit a situation that I can't solve one way or another. Not great if you only have time for a plug-and-play solution. This is not Stitch or FiveTran, but if you invest the time to learn and use Matillion you can end up with a vastly better solution to your data needs.
I would like to give it a 9.5 or so. Only a few aspects of the usability need some work. These are trivial things to fix, but really annoying to an experienced user.
My learning curve was long. Matillion is simply not like any other ETL / ELT tool out there, because Snowflake isn't like other cloud databases out there. I had to first learn a different pattern of data movement and error checking. Next, I had to learn which components to use and when. There aren't any good in-depth how-to guides out there. There aren't really detailed instructions on how to implement a specific pattern and what challenges you are going to face and how to handle them. There isn't a site for users and developers to hang out and ask questions, there is only the support ticket queue and some very knowledgeable and helpful Matillion engineers to answer your questions.
I've only had a handful of performance issues that are clearly in the realm of Matillion's logic. The vast majority of features and functions perform as if they are operating directly in the instance with the database with no overhead.
Score 10 out of 10
Vetted Review
Verified User
Matillion is helping resolve use-cases related to transparent orchestration workflows. These workflows often involve custom Python, SQL, staging locations, and sending data to external systems such as Salesforce and cross-channel marketing platforms all in one workflow. When FiveTran & DBT don't meet our exact requirements and use-case, we use Matillion to help resolve these use-cases in an easily understandable workflow that technical users, new engineers, or non-technical users can understand/troubleshoot.
  • Extremely user-friendly workflow orchestration between multiple languages such as SQL, Python, bash, and various API connectors
  • Salesforce connectors to pull and push data between systems save us a ton of time
  • Matillion Exchange workflows allow for easy sharing of templated best practice transformation jobs with ease
  • Very responsive support
  • GIT Functionality needs works, has unnecessary steps and needs "GIT DIFF"
  • A cloud hosted version would help resolve a lot of issues
  • Serverless solutions for scaling up storage and compute for certain jobs in Matillion if we wanted to run data science workflows
If your company has ever had issues with un-transparent data pipelines, difficulty understanding orchestration of workflows, and issues with data engineering retention and ramp-up, then Matillion is likely the right solution for you. It doesn't require heavy technical skills, just a strong understanding of Data-Eng Concepts. It's low-code/no-code optionality allows for everyone to come in and quickly understand how jobs are interconnected without having to cross navigate multiple systems for connectors, transformations, loads, and logging. It's an all-in-one solution that complements more specialized solutions like Fivetran and Hightouch. You can even set up python connections to orchestrate Fivetran and Hightouch into a Matillion workflow.
I was able to get up and running with Matillion in 1-2 hours with a simple job my first time using it. It's pretty intuitive and straightforward. No YAML's, config files, local installations, reading of complex documentation, or understanding of unique open-source frameworks.
The internal communication and collaboration than the setup of Matillion, once the EC2 instance triggered, we were able to get started with transformation jobs within 1 hour.
Overall I think Matillion is very flexible and scalable. The only thing Matillion could improve on is a serverless backend to handle heavy compute workflows rather than an EC2 instance. At that point, I feel it's basically the SNOWFLAKE of ELT tools.
Austin Lee | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Matillion is being used to transform and integrate data in our organization. Most of the data that we highly rely in decision making and planning is well analyzed by this great product. Data is safe for use in our departments can easily be saved in the cloud for free for future use.
  • Data integration and transformation.
  • Saving data in the cloud for future referencing.
  • The user interface can be simplified to enable users to learn the functionality curve within a short period of time.
It is very well suited in data management and integration. It has saved our organization from data fraud and mismanagement of information from various sources. It has driven our company forward by delivering useful information that is suited to support our goals. Taking data from various sources and sorting it and uploading it in a cloud warehouse has enabled our departments to handle data properly.
It has given us excellent services and lead to the growth of our organization. Our organization departments usually get an opportunity to work on data that is secure and make informed decisions. In overall data, Matillion has been very effective in data management. Distribution of data across our organization and effective storage for referencing has contributed to excellent data handling.
Matillion was implemented within one week in our organization for full functionality. Our company secured its deployment within this period of time and all our programs were able to run smoothly. The learning curve was easy to master and get it run as expected within a short period of time. Our teams commitment and flexibility of this tool contributed to quick implementation.
The platform flexibility in data integration and transformation has enabled me to give it this rating. The performance from my view have been awesome and there is no complain from any department since we deployed it. The work across our organization and management of data increased our trust on Matillion. The platform operation across our organization has been very recommendable in data integration and offering solutions to any uprising challenge.
July 09, 2021

Matillion Review

Score 9 out of 10
Vetted Review
Verified User
I have been using Matillion since March and it has been a nice experience. It's helping a lot during our ETL process from RDS to Redshift, both on AWS. I really appreciate the software features and their support team is always able to clarify doubts and lead us to the best solution for our business.
  • It's easy to understand
  • Support team
  • Good performance
  • The step of transformation is not 100% clear.
  • The price could be better.
  • Their forum is not that good.
Matillion is nice if you don't want to spend time developing a home-built application to perform the ETL step and of course if you have a budget to spend on it. I don't suggest Matilion if you want to customize a lot or if you have a really specific environment to handle data.
It's easy to understand how the flow works and all the features provided by the tool. You also can find some information on their website or Youtube channel.
I started reading about Matillion and after one week and talking to their support team I started my project and it was up and running after 2 weeks of developing.
It looks like we will not have any problem with scalability.
They have the answers and looks like they have a really technical team ready to reply to you but unfortunately, they are not so fast (I don't know why) which can put you in troubles if you are running against the time and if the whole project depends on this answer to keep going.
Score 9 out of 10
Vetted Review
Verified User
We have been using Matillion for a specific project in the SupplyChain area. The ETL works well and it was really easy to implement. We didn't use many objects, but we still were able to implement everything we needed, such us datamarts and file exports. We also tested the connection with SAP and we were happy to see it worked correctly. Something I think could be useful is a tool to move jobs between different environments: export/import upload. Json is ok but doesn't allow us to manage to version, and moreover, if you are moving many files, you need to manually substitute the original job with the new one.
  • Easy to use
  • Flexible in the use of parameters.
  • Well integrated with insertable Json code.
  • Tables comparison automation works well.
  • Pay for use
  • It was difficult to understand how to use parameters.
  • Job validation takes long when you run a job.
  • Logging for debug is not always so clear.
It is useful when you need to create a Datawarehouse and put together a complex workflow. The development time is reduced compared to other ETLs. When you are more confident, you can also create complex flows using different job parameters and orchestrator jobs. Buttons for start and end success/end errors are also useful. Connection with source systems is also good.
It is good. Matillion is easy to use, the connection is web-based, fast, and always works. The "search" into each object simplifies the construction of transformations and joins. The SQL is always retrievable from jobs, therefore the debug phase is also easy to analyze. Moreover, in case of need, you can also write your own SQL into the appropriate object.
It took some days in order to figure out how to configure the environment and all the basic configurations. Writing simple jobs took a really short time to learn (hours), while in some other days I was able to prepare a full orchestrator. Parameters configuration is a little more complex and I had to figure it out in some hours together with a colleague of mine.
I appreciate that you can execute every needed step without the necessity of creating a full orchestrator. Therefore, when developing many jobs, you can test and monitor each of them in an easy way. Sharde jobs are really useful when you want to simpify the development and standardize some common steps.
Score 9 out of 10
Vetted Review
Verified User
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.
Score 9 out of 10
Vetted Review
Verified User
We use it to get data from various sources into our data space. We use it within our company and also share it externally with our clients. It is very much useful.
  • Just drag and drop, good to do.
  • Very simple to use.
  • Good features.
  • Support responds a bit late.
It is very use to use and just pushes the data you need to our database warehouse. Great for dataflow jobs.
It performs quick data, has graphical easy nature use. It presents the activities in simple interface.
It has valued money as well as time. It just saves lots of time for us.
We have a huge amount of data that is processed and Matillion is doing complete justice to it. We have increased number of users from the time we have taken this.
Score 9 out of 10
Vetted Review
Verified User
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.
Seems to working well and we are now look to upgrade to a higher plan
Score 8 out of 10
Vetted Review
Verified User
We are implementing ETL workflows for other organizations that we consult for. It is currently being used for smaller sized clients across the whole organization. We use Matillion to automate the ingestion and transformation of data pipelines. We utilize Snowflake as a data warehouse, so we use Matillion as the tool to load data into the Snowflake database.
  • Automation and scheduling
  • Security and authorization
  • Ease of use
  • API Calls using python
  • More community support and forums
Matillion works well if the data sources are coming from common applications or databases.
It is generally user friendly in terms of the various tools, but I think there is room for improvement in terms of seeking support or examples from other users.
I watched training and example videos/seminars for about two weeks before getting an instance set up for the company. And from there, I had a usable workflow going within another week.
June 29, 2021

Matillion Review

Sudarshan Kothari | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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.
Score 8 out of 10
Vetted Review
Verified User
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.
Within 2 days
We have a 24 node Redshift cluster but COPY can utilize only 16 multi-part upload due to Matillion which is annoying.
Score 9 out of 10
Vetted Review
Verified User
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.
November 27, 2020

Good at what it does.

Score 9 out of 10
Vetted Review
Verified User
Being used by the financial services department for ETL processes. It does the job of cleaning and transforming our data for internal and external reports.
  • Easy to use GUI.
  • Grid variables and other variables make it reusable.
  • Task history helps us identify issues.
  • Need source control for the ETL scripts.
  • Need to undo features for mistakes.
Well suited for orchestrating ETL task of mid-level complexity. Lack of source control is a major issue when large teams or high complexity is involved.
It's good at what it does, just wish it had more functionality.
Up and running in 2 months.
Pretty scalable, but needs source control to get to the next level.
We havent had to use them a lot, but they have done an excellent job when we did.
Score 8 out of 10
Vetted Review
Verified User
Matillion is used by the software department for data integration, for syncing up data from the OLTP database to the OLAP system, and populating data for our data warehouse.
  • Easy to build data integration job
  • compatible with AWS cloud platform
  • lots of components for different use cases
  • No powerful job monitor console
  • Flexibility for supporting scripting languages
  • Expensive license fees
Matillion is well suited for building a data warehouse on AWS Redshift, but less suited for on-premise data integration.
It's good for building a data warehouse on AWS Redshift.
It's easy to set up an environment and start a project quickly.
Matillion supports multiple DBMS systems and multiple data format files, which means it has strong scalability.
Most of time Matillion provided support on time and helpful.
Adel Helal | TrustRadius Reviewer
Score 3 out of 10
Vetted Review
Verified User
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.
Score 8 out of 10
Vetted Review
Verified User
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.
October 05, 2020

Simple and Powerful

Score 7 out of 10
Vetted Review
Verified User
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.
Score 9 out of 10
Vetted Review
Verified User
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.
Score 9 out of 10
Vetted Review
Verified User
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.
Score 8 out of 10
Vetted Review
Verified User
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.
Score 7 out of 10
Vetted Review
Verified User
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.
Score 10 out of 10
Vetted Review
Verified User
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!
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