Matillion Reviews

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Adam Labay profile photo
Score 9 out of 10
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We used Matillion on a small, 3-person team to manage ingestion and transformation for our data warehouse, pulling from numerous datastores and vendors to a common warehouse schema.
  • Shallow learning curve - Matillion is powerful, but only as complicated as you need at a given time.
  • Superb helpdesk and documentation.
  • Jobs are translated directly to SQL which makes them easy to debug.
  • Parsing of flat files is inconsistent depending on the source
  • The product is constantly evolving (good!) but that means relearning parts of the interface every few months
  • Support for a larger set of APIs and database engines would be helpful
Matillion is great for a team's first foray into ETL. It holds your hand exactly as much as is needed, and scales brilliantly within the scope of a small team. It becomes less ideal as you add more data sources and processing. It interacts well with Python, but any other scripting is harder to integrate.
Read Adam Labay's full review
Patrick Hildreth profile photo
Score 10 out of 10
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We are currently using Matillion as our primary ETL/ELT tool both in our department and supporting others in the organization as well as introducing it to other Business units under the Cimpress brand. For us and others it primarily solves the need of loading and manipulating raw data from multiple sources and making it available in both Redshift and Snowflake. Additionally, the flexibility of the tool and its seamless integration with the complete AWS Services library allows us to perform many operational tasks that are completely unrelated to the ETL/ELT workload.
  • Integrates very well with most, if not, all AWS services - Easy to get data from anywhere inside your AWS account into Redshift or Snowflake databases.
  • It has many available integrations and datasource connectivity outside of AWS and other cloud service providers - Makes it easy to get data from many sources and systems in more traditional on or off premise technologies as well as vendor-specific data and application interfaces.
  • Very intuitive, easy-to-learn-and-use interface with a large collection online tutorial articles and videos - makes for an extremely easy onboarding of users who may or may not have past experience with other similar tools.
  • Limited selection of instance sizes - Larger organizations and groups my need to set up multilple instances depending on estimations on concurrent users.
  • Lack of complete github, gitlab, or other source control integration - it has internal versioning but it is limited. Manual job exports currently don't lend well for useful DIFFs comparison when using those technologies.
  • Needs more options for authentication and security - User creation is very manual and can be tedious and difficult to manage for larger installations.
It is very easy to set up and get working on right away, anyone with access to an AWS account and the Marketplace can have a fully capable ETL server up and running in under 10 minutes and the 14-day free period allows an adequate amount of time to decide whether the tools works for your use-case. It has a direct hourly billing option that bills through your monthly AWS bill and the option to pay-as-you-go or pay up front and reserve an instance at nicely discounted rate. Overall it's the usability, flexibility, and capability that made Matillion the choice for me.
Read Patrick Hildreth's full review
Sudarshan Kothari profile photo
June 11, 2019

Matillion Review

Score 8 out of 10
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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).
Read Sudarshan Kothari's full review
Kris Shinn profile photo
Score 8 out of 10
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Matillion is used in our Data Analytics and Data Engineering department. At the time we purchased it, we had no dedicated data engineers. We needed something powerful enough to handle more complex jobs, but simple enough that a semi-technical analyst could use. We were using a lot of different tools at the time and were evaluating a lot of different ETL solutions in the space.
Matillion provided a solution that was simple, easy to connect, and also provided Internet connectors to some of our most crucial systems, such as Salesforce and Jira. It has allowed us to consolidate much of our processes onto a single system that is easily understood.
  • Cloud connectivity: It makes pulling data from cloud services like Salesforce super simple and easy to bring into a data warehouse
  • ETL Orchestration: The drag and drop interface makes it easy to compose new orchestration layers in our ETL. It's something that does not require a Data Engineer to complete.
  • Enterprise integration: It was really easy to configure into our LDAP system, and that makes administering the box really easy.
  • Variety of Data sources: It is pretty easy to bring data into Matillion to process into the data warehouse.
  • The Gui provides other non-functional visual elements to mark up the job. This is great for team members to communicate complicated parts of the ETL or to otherwise label parts of their ETL.
  • Matillion has no clustering ability. For particularly large jobs or large data sources, processing can take a long time and it does not have the ability to map-reduce, like Spark.
  • The output is limited to Redshift. Often times we would want to drop a Parquet or Avro file into s3 as the output of our ETL.
  • We often get OOM errors and other server related constraints. We need to be very careful about how our jobs are scheduled in order to make everything work well.
  • It is not clear from the documentation how to organize work in Matillion. Between environments, projects, and jobs in a project, we've had to organize in a way to accommodate for Matillion's limitations, which doesn't allow us to organize our jobs in a way that makes sense for us.
Matillion is great at processing an ETL for cloud-based systems (Jira, Salesforce, Google Analytics, etc). It reduces (or in some cases eliminates) the need to put together a custom software interface into these systems. It is also great for non-technical users who want to put together some ETL processes for analytics, but do not want to invest into a Data Engineering team. It's also great for landing data for consumption into end datastores like Snowflake or Redshift.
Matillion is not great for large datasets or prepping for data science. As a single vertically scaled solution, it does not have the power of a cluster oriented ETL technology like Spark. Additionally, to prepare datasets for data science where you would want to bring in a processed dataset in Parquet or Avro formats, it requires you to land the data into Redshift and then dump it back out, then format it, in order to get it into a portable format for something like RStudio.
Read Kris Shinn's full review
Matthew Burr profile photo
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Matthew Burr's full review
Aleksa Topalović profile photo
Score 10 out of 10
Vetted Review
Verified User
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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.
Read Aleksa Topalović's full review
Bryan Boutwell profile photo
Score 5 out of 10
Vetted Review
Verified User
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It is being used across all of our internal data to bring data together for reporting. This includes
Sales, Marketing, Billing, Support and ustomer activity. We have processes that run every minute as well as batch processes that run every day.
  • Quick access to Google Sheets data.
  • Utilizes SQL well.
  • Supports custom API data sources.
  • Pricing is by server size not # of data sources or volume.
  • Source control integration is archaic and not implemented with teams in mind.
  • Has some performance issues related to memory issues.
  • Documentation is lacking and there is no real training available.
Matillion is well suited for people with SQL skills and makes it simple to get data into a database quickly without much dev time required.

Source control is not well thought out and is basically unusable.
Read Bryan Boutwell's full review
Bianca Levy profile photo
Score 8 out of 10
Vetted Review
Verified User
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Matillion for Snowflake is used by our Data Engineering team for developing data flows for extracting and transforming data into our Snowflake data lake. The data lake is available to the entire organization for analysis and decision support. We needed an ETL tool for batch processing for replacement of Airflow.
  • Ease of use.
  • Suitable for developers from junior to senior.
  • Connects to a variety of data sources and platforms.
  • Matillion has many tools available for transforming data.
  • Does not integrate easily into the source code control system.
  • The small instance needs more concurrent user connections. Two is too few, and moving to the next instance size does not make sense in our development environment.
  • Matillion for Snowflake does not have a Dynamo DB connector.
Matillion is great for scheduled batch processing, but not so great for stream processing.
Read Bianca Levy's full review
Travis Schaugaard profile photo
Score 10 out of 10
Vetted Review
Verified User
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Matillion is being used by Xyngular to ETL data from multiple environments into an AWS Redshift database. We also use it to pull data files from FTP and load them into the database. It also allows us to be very flexible on the scheduling of the jobs.
  • Loading an FTP file into the database.
  • Transforming data before loading it into the database.
  • Flexible scheduling of jobs.
  • Straight data copy from one database to another.
  • When I make changes to a job and add fields to what is being pulled, I have to drop the entire list and repopulate it.
  • Honestly, the first thing is the one part I have had issues with.
We also use AWS pipelines to pull data from one environment to another. It works great for a straight data copy. However, it doesn't allow much transforming of the data. Matillion provides a much better way to make transformations on the data prior to loading it into the database.
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Joshua Stewart profile photo
Score 8 out of 10
Vetted Review
Verified User
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We currently use Matillion for Redshift as our ELT tool for data retrieval, aggregation, and management. It is currently being used by multiple departments in our organization, including our data team and operations team. Having used Apache AirFlow, Matillion is a breath of fresh air. It is easy to use, easy to implement with a variety of technologies, and the support system is quick and convenient.
  • Easy to setup and provision
  • Expert customer support
  • Can take some getting used to
  • Inability to update/install Python libraries
For our organization Matillion has been a great tool for us and allows easy compartmentalization of processes that allows for easy debugging and extending capabilities.
Read Joshua Stewart's full review
Arnob Bordoloi profile photo
Score 10 out of 10
Vetted Review
Verified User
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Currently driving the ETL /ELT processes to load a Redshift Data warehouse. We have 5+ data sources that include on-premise/cloud databases, CRM and marketing tools that are feeding the data warehouse. Matillion is also used to automate various marketing processes.
  • Cloud-based solution
  • Seamless integration with Redshift
  • Minimum scripting required
  • Good feature documentation
  • Easy to learn/good tech support and tutorials
  • Installation/setup
  • Better out of the box support for CRM tools like Zendesk would be helpful.
Suited for:
1. People looking for cloud-based ETL/ELT solutions.
2. Works great with Amazon Redshift data warehouses.
3. Large transformation jobs, as it leverages the power of redshift to run transformations.
4. Great UI for users to create, maintain and document jobs.
Read Arnob Bordoloi's full review
Tanishka Tikoo profile photo
May 22, 2019

Matillion For All

Score 9 out of 10
Vetted Review
Verified User
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Matillion is being used in our company as an ELT tool. We load data into it from various sources, such as Salesforce, NetSuite, etc. We are exploring more such options, from there we can pull the data into matillion. Once in matillion, we transform this data using various components provided by the matillion tool to modify the data as we need. Then, once all the modification are in place we load the data into Snowflake which is our data warehouse
  • Data extraction is really quick and hassle-free.
  • Transformation is very easy to perform as it provides an elaborate list of components that can be used to create and optimize your query.
  • Scheduling is very manageable and easy to monitor and review.
  • UI is very user-friendly, every component and sub-component can be understood by the help option provided as a hyperlink.
  • There can definitely be some improvements w.r.t the NetSuite orchestration component. We have had lots of trouble connecting it to Matillion during POC.
  • Although the UI is quite user-friendly there is room for much improvement.
  • There should be requirement specific customized training before a company starts working with Matillion. We got general training which definitely benefited us, I just think a more project-specific training would have been more useful
Matillion is well suited for all such projects where there is a need for extraction transformation and loading made easy. You can avoid the long cumbersome SQLs by using the various components provided by matillion. It is a fresh new tool which has a lot of potential and a lot of exploring is still required.
Read Tanishka Tikoo's full review
Srinivas Velamuri profile photo
Score 7 out of 10
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Verified User
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We're exploring Matillion as an ELT tool when we migrate our workloads to the cloud. This software is not in production yet as we're still exploring. Once accepted, this software will be used in our Information Management department. This business problem that is addressed by this software is ELT.
  • Drag n Drop to build orchestration and transformation jobs
  • Simplicity
  • Pay as you Go
  • Ability to scale up and down
  • Minimal code compared to the competition
  • Cloud based
  • Ability to migrate jobs created for one platform to other easily (for e.g. from Redshift to Snowflake).
  • There is a scope of improving developer productivity by enhancing the user interface. Sometimes the UI is confusing.
  • Some times the orchestration and transformation job diagrams become very complex. Need to come up with design patterns for proper diagram preparation.
For simple business logic, Matillion seems to fit the bill. As the business logic becomes more and more complex, Matillion has to show customers how can they build transformation jobs with complex logic.
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Clark Huang profile photo
Score 9 out of 10
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We are using Matillion within the Analytics department. We had a core need to be able to move data from multiple single-tenant client database sources into a proper data warehouse (AWS Redshift) for analytical reporting.
  • Easy drag and drop logic/control functions.
  • Ability to script (in Python) when out of the box components are not enough.
  • ELT vs ETL allows for super fast transformations done directly in Redshift.
  • We have had issues with out-of-memory errors when Matillion is up and running for a long time. For this reason, we've implemented an automated monthly restart job which works around this issue.
  • We do a lot of "reverse ETL" processing. For certain use cases we need to run extracts out of the analytical data warehouse, massage the data, then move it back to our transactional databases for certain operational tasks. Although it is possible with certain components in Matillion, there could be more enhancements to those components to make life easier for some tasks.
If you have lots of data from multiple transactional data sources and schemas that you need to iterate through and combine into a data warehouse, then Matillion was the easiest solution we found when we were in our proof of concept phase 3 years ago. We had some performance issues initially, but Matillion support and account management were able to help us through those, and now it has been running very smoothly for the last year.
Read Clark Huang's full review
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Score 8 out of 10
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Matillion is used by two different departments at our company - engineering and professional services. For engineering, it is used as an ETL pipeline tool for ingesting data into our internal data warehouse from a variety of 3rd party platforms, ultimately for the purposes of advanced analytics. For professional services, this tool is being used to automate client data load processes.
  • ELT - out of the box support for a variety of popular APIs.
  • Support for the big players in the cloud data warehouse marketplace - Snowflake, Redshift, and Google Big Query.
  • Strong documentation and technical articles, including data models for each supported external data source.
  • Support for other programming languages beyond Python.
  • Expanded the number of concurrent users (limited depending on license level).
  • Increased number of project environments (limited depending on license level).
Great for batch processing structured or semi-structured data. Streaming large data sets will be more difficult but this is not really what the tool is designed for.
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Score 9 out of 10
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Few months ago, we'd decided to migrate on-premise BI stack to cloud as on-premise solutions were not able to meet growing demands. Clearly, we were looking for cloud-only solutions. After extensive research and POC of several tools, we've onboarded Matillion. Matillion is primarily used by BI developers for ELT pipelines (External applications, API, Database, CSV ..) and transformation but the resulting data is used by the whole organization via the dashboard or Excel extracts. Direct data integration connectors, powerful transformation capabilities, user-friendly interface, error handling, logging, tight integration with Snowflake, extensive usages documentation and excellent technical support inclined our decision towards Matillion. It reduced the development time drastically. Data load performance is pretty impressive compared to on-premise deployments.
  • Seamless connectivity with Snowflake. Direct data connectors are available for popular applications
  • Powerful data transformation capabilities. Several components available to support complex data transformation
  • Python and Bash script component allows endless possibilities
  • Pretty impressive performance
  • Excellent Technical support and usage documentation
  • Timely upgrade and bug fixes. New features included in every release
  • Integration with SOAP API's especially Amazon MWS is not straight forward
  • Collaborative and autosave feature sometime become painful when multiple developers are working on the same Project
  • Merge Job or changes feature is not available which makes production deployment time consuming
Matillion ETL for Snowflake is an excellent tool to create ELT pipelines to integrate in-house and external applications. It has several integration components which simplified data loading. Provides powerful transformation capabilities which require hours of coding otherwise. Complex transformation requirements can be achieved using the Python component.
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Score 9 out of 10
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We use it as the main ETL for our DWH ingesting data from transactional apps and various web sources, mostly marketing related to social networks. It is being used across the whole organization.
  • Simplicity
  • Performance
  • Documentation
  • Dealing with semi-structured data
  • Data profiling
It is well suited to ingesting data from web sources, such as social networks. It is less appropriate for dealing with semi-structured or unstructured data.
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Score 6 out of 10
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We're using Matillion to mainly load data from three places into our RedShift clusters. The first one is from a s3, the second is syncing data from our production DB.
  • Managing Schedule
  • Intuitive UI
  • Easily integrates with the rest of AWS
  • Create different versions is easy
  • The deployment process is quite manual; need to export and import, create a new version. Would be nice if there's a repo for continuous integration
  • The Python script module is very limited. We try to use it to parse data on a file with 500 records, and it constantly crashes. It does not have the capabilities to run Python programs
  • In the Mongo module, the field must exist in the source system. Working with NoSQL DB, some fields might not exist just yet, and essentially we'll have to create everything downstream once the field appears in the source system.
For organizations that have limited resource with AWS Infrastructure/Lambda knowledge, this is a great tool that works right out of the box. This tool is great for batch jobs and easily works well with AWS. However, for most processes, it could be replaced easily with a simple Lambda.
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Score 9 out of 10
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Matillion is being used to assist our data pipeline ETL, and allows our business analysts to rapidly make adds, moves, and changes to our data ingress and reporting needs via an easy to use UI.
  • Fast - easy to use
  • Flexible - data ingress and back-end data stores
  • Cost-effective - easy to start small and scale up
  • More data stores beyond Redshift, Snowflake, BQ
  • More connectors for Redshift, Snowflake, BQ
  • UI updates to reduce clicks and time to configure
Matillion is well suited for quickly piping in multiple data feeds, and orchestrating ETL. Adds, moves, and changes are simple once they're set-up.
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Score 8 out of 10
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Matillion is being used by my department, and we use it as our main ETL tool daily.
  • Makes it easy to design ETL pipelines because of the "drag-and-drop" components.
  • Handles workloads nicely and seamlessly,
  • Has a wide variety of input and output sources
  • Theming or color selections (overall UI, folder and job icons, etc.) would be a nice to have feature.
  • The ability to "inspect" environment variables during the transformation/orchestration building process (similar to debugging in programming).
  • When environment variables are mentioned inside the components' parameters setting, it "invalidates" the job but the jobs would still run on executions. This might be caused by the default values of environment variables being NULL.
Matillion is a great tool that can be used by users with technical skills ranging from average to expert (aka, it's pretty simple to build ETL jobs for your average users with basic knowledge regarding databases and data handling).
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Score 10 out of 10
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We are using Matillion throughout our organization to run and automate all data pipelines into our data warehouse. We integrate various sources from API's, Python Scripts, SQL Scripts, and other databases, into a single data warehouse.
  • Easy to use.
  • Ability to leverage several technologies (SQL, Python, Bash, AWS).
  • Pre-built connectors to simply provide connections between several commonly used technologies.
  • Their customer support is extremely prompt and good at helping out.
  • Alerting needs to be done via AWS SNS, not pre-built in the platform.
  • There's an inability to track data lineage (where did a column of data from a downstream table come from?)
Matillion is one of the best ETL tools I've ever used. I hope they continue to do well, and I will continue to recommend it to friends.
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Score 10 out of 10
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We use Matillion to support our recurring data requests and cleaning processes on large data sets. Approximately half of our department currently uses it.
  • The GUI is very intuitive, making it easy for new users while also having more complex functions available to experienced users.
  • There is a good mix of defined components and customization options, giving users flexibility for both their skill level and the task at hand.
  • Matillion includes a chron scheduler and s3 export options which streamline the process, enabling all portions of the ETL process to take place within the same utility.
  • Areas for improvement include local variable updates, e.g. a last run date.
  • More python library support would greatly broaden the potential uses.
  • The S3 export function could use some adjustments in making clear defaults, particularly in regards to snowflake file types.
Matillion is well suited to recurring, SQL-based data pulls that happen on a regular basis. It is also easy to modify existing flows via variables for new tasks, as well as leveraging Python to update variables such as dates and date-based table names. It is not appropriate for live data return. "Select" is not supported in that data outputs must be sent to a table.
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May 23, 2019

Matillion Review

Score 7 out of 10
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We use it to transport data to our data warehouse.
  • Reading from multiple databases.
  • Writing to a data warehouse.
  • Performing data transformations.
  • Reading from other kinds of data storage in addition to relational databases.
  • Interface for API profile builder could be more user-friendly, especially for new users.
  • Could use better documentation & examples for API Profiles syntax.
  • No built-in version-control management.
  • No way to add integration tests for jobs for QA purposes.
  • If you have a lot of jobs currently running at the same time, then you cannot easily manage them, and they're relegated to the "Jobs" panel in the lower-right corner. It would be nicer to have an interface that allowed you to manage a large amount of currently running jobs (sortable columns, inline searching/filtering for currently running jobs, etc.). Maybe even have a larger view than just 25% of the window.
  • Difficult to track/identify changes made by collaborators (having a VCS/Git integration would improve this).
Matillion is good for reading and transforming data to a data warehouse, and has a variety of transformation commands. The flowchart UI makes most workflow composition relatively easy.
One thing to note is that it lacks any built-in version-control management, so you can't really save the state of your Matillion instance configuration very easily in your version control system (e.g. using Git).
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Score 10 out of 10
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Matillion is our ELT-Tool to load and analyze our web-log data with Redshift.
  • Very nice and intuitive user-interface
  • Easy drag and drop of components with a good documentation of each component
  • Good integration of different data sources
  • Detailed task history with a good overview of the current workflow with the defined parameters - easy error handling and detection
  • Better parallel workflows
  • More configuration opportunities for data sources without Python
If you are using Redshift or Snowflake it is the best ETL-tool.
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Score 8 out of 10
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It is currently used by Business Intelligence to produce the information fed to visual tools for the whole company. It is used to gather data from multiple sources, clean and normalize it.
  • Make complex joins between tables easy.
  • Connect to multiple data sources.
  • Make data processing accessible to non-IT users.
  • More connectors to other APIs makes integration easier.
  • Have a development-production environment, so tests can be performed without pushing them to production and therefore not affect daily jobs.
  • Better preview visualization for data.
Well suited to process data that is already passed through a basic ETL of the main data sources. Less appropriate when a lot of development is required.
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Feature Scorecard Summary

Connect to traditional data sources (58)
8.2
Connecto to Big Data and NoSQL (41)
7.6
Simple transformations (59)
8.5
Complex transformations (59)
7.4
Data model creation (34)
7.6
Metadata management (41)
6.6
Business rules and workflow (50)
7.5
Collaboration (52)
6.9
Testing and debugging (51)
6.8
feature 1 (4)
8.2
Integration with data quality tools (23)
6.6
Integration with MDM tools (21)
6.3

About 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
Does not have featureData model creation
Does not have featureMetadata management
Has featureBusiness rules and workflow
Has featureCollaboration
Has featureTesting and debugging
Does not have featurefeature 1
Data Governance Features
Does not have featureIntegration with data quality tools
Does not have featureIntegration with MDM tools

Matillion Screenshots

Matillion Videos (6)

Matillion Integrations

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