in my opinion, Meh
We are moving away from Matillion to MWAA Airflow with dbt.
Maintaining source control in github is important to us.
We have used Matillion …
Maintaining source control in github is important to us.
We have used Matillion …
Overview
What is Matillion?
Matillion is a productivity platform for data teams.Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed…
Recent Reviews
How Matillion Differs From Its Competitors
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Popular Features
- Simple transformations (124)8.181%
- Connect to traditional data sources (122)7.777%
- Complex transformations (123)6.464%
- Testing and debugging (109)5.555%
Reviewer Pros & Cons
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What is Matillion?
Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality…
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- No setup fee
For the latest information on pricing, visithttps://www.matillion.com/pricing
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- Free/Freemium Version
- Premium Consulting/Integration Services
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- FAQs
What is Matillion?
Matillion is a productivity platform for data teams.
Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed and scale that matches the business’s data ambitions.
The vendor states enterprises including Cisco, DocuSign, Pacific Life, Slack, and TUI use Matillion to move, transform, and orchestrate their data for a wide range of use cases from insights and operational analytics, to data science, machine learning, and AI.
Native integration with popular cloud data platforms such as Snowflake, Databricks, Amazon Redshift and Google BigQuery lets data teams at every skill level automate management, refinement, and data delivery for every data integration need.
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
Matillion Integrations
Matillion Competitors
Matillion Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
| Supported Countries | Global |
| Supported Languages | English |
Frequently Asked Questions
Talend Data Integration, Informatica Intelligent Cloud Integration Services, and Fivetran are common alternatives for Matillion.
Reviewers rate Data model creation and Metadata management highest, with a score of 9.1.
The most common users of Matillion are from Mid-sized Companies (51-1,000 employees).
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Reviews and Ratings
(204)Attribute Ratings
Reviews
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October 05, 2020
Easy to use and recommended for existing ETL developers
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.
October 05, 2020
Simple and Powerful
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.
October 05, 2020
I'd bet my job on Matillion
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.
October 01, 2020
Get up and running with your data fast!
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.
October 01, 2020
Matillion ETL - excellent cloud based data tool
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.
October 01, 2020
Matillion: flexible and complex
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.
October 01, 2020
Fell in love with Matillion in less than a week!
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.
October 01, 2020
Great product that does what it says on the tin
We started using Matillion a couple years ago across all areas of our business. We use this tool to gather data from various data sources (SQL, Google Sheets, API calls, etc.), and transforming it and eventually outputting to our data warehouse for data visualisation.
- Data transformation.
- Pulling data from various sources.
- Speed of data transformations.
- Automatic jobs backup.
September 17, 2020
Good stepping stone for data infrastructure
Matillion is used by the data engineering team in order to build most of our data warehouse.
- Development speed
- Number of connectors
- Load/unload speed
- GIT integration
- CI/CD integrations
- Failure messaging
September 08, 2020
My experience with Matillion over the past couple of years!!
Here at ReportGarden, we use Matillion to fetch data from multiple databases and construct a compact and easily referable database using various data blending techniques with the help of various blocks like Python script, SQL, joins, etc. We use the combination of SNS and Lambda to make sure tasks run seamlessly every day and to stop EC2 instances once tasks are done, which is of great use in saving unnecessary costs.
- UI is very simple to understand and documentation complements it very well.
- Able to mail logs for processed and errors in daily tasks.
- Having a detailed log of daily tasks maintained in the UI is a good addition.
- Slightly over priced for a startup.
- I feel support is a bit slow.
- Not all the libraries are installed in Python, would be easier if we could install them from the user side.
We use it as our ETL tool across the entire organization including marketing, finance, customer service, and support. We pull data from our proprietary product use as well as Salesforce to merge data about our product use, customer service, employee time, billing and finance information, and process handling in our AWS cloud core.
- Hides the SQL code
- Creates an intuitive UI for data flows
- Interfaces with many third-party platforms
- Moving data through a flow can be very tedious.
- Errors are often obtuse and hard to pinpoint.
- They used to auto-complete typing table names, but they removed that, making point number one all that much worse.
September 03, 2020
Matillion: the last puzzle piece in modern analytical solutions
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.
September 01, 2020
Matillion - the swiss knife of ETL
We use Matillion as the main ingestion tool within the business intelligence department.
We use it to ingest data coming from different legacy systems, outputting data in the cloud datalake solution.
It's really easy to learn, configure and spend less time ingesting the data and more time getting insights from the data.
- Python integration.
- Easy to set up, back up and restore.
- Scalable, works for any and all file types.
- Python libraries.
- Bigquery metadata.
August 31, 2020
Matillion for ETL and Data Collection
Matillion is being used as an on-premise ETL by our Data and Analytics team. Matillion has been a great tool for our organization from an ETL standpoint. It is scalable and flexible enough to accomplish any task we need to do. Matillion support is also spot on, timely, and responsive. Will work through any problem with you--whether it be you or them. They will see it through.
- Great UI
- Support
- Data Collection via API
- Developing in Python is primitive
August 29, 2020
Robust ELT with Matillion for Redshift
We use Matillion for Redshift as to orchestrate data pipelines as our main data integration tool. We are pleased by the usability and reliability along with multi-user interface, git integration, and lots of plugins. Matillion has helped us to have strong data pipelines that we can monitor to run hundreds of jobs a day. Support is also proactive. It's overall a great ELT tool.
- Visual flow of ETL
- Integrations with GIT
- Multi-user editing
- Shared components portability
- Having other databases other than Redshift as targets (such as MySQL)
Matillion is used as the primary ETL/ELT for data integrations in AWS cloud. We needed a tool that has diverse integration capabilities, rich features, is simple to use and manage, costs less, is easy to learn, and requires minimal maintenance. With a small IT team, we cannot afford to compromise in any of the aspects, as the company’s entire dataload depends on it.
- Matillion connection to AWS S3 and Snowflake are the vital parts for our architecture and Matillion is extremely simple to configure and robust in handling the connections. The interface is very intuitive to configure.
- Python and Jython interface provides an extremely useful capability to implement our design outside of available components.
- The scheduler is a nice feature and very helpful to schedule workloads. It has all the major capabilities of good scheduler software, except for event triggers.
- Has the interface to do query connections to Salesforce and similar software, but does not have a solid OAUTH interface, or it may be solid, but misses help documentation.
- Help and support are really awesome. But materials do not cover a few petty things that can support beginners to get it going.
- Examples in documentation and workflows could be better.
- A component to write direct SQL processing code in orchestration jobs will be a great way to do a few trivial steps.
- Shared jobs are a great way to create a framework for processing. But once embedded, if a rollback change is done on a shared job, say remove a job variable, it breaks the orchestration jobs using it. There should be an easy way to update the job, similar to when shared jobs are enhanced and orchestration jobs inherit seamlessly.
August 26, 2020
Simple and powerful data aggregation tool
I use Matillion for one project. I use Matillion to solve my data loading issue because Matillion supports many data sources. I use this to aggregate all the data sources to my data warehouse. Before using Matillion, I needed to write my own data loader, which was very time consuming and created a lot of stability issues. By using Matillion, I can load my data within an hour to my warehouse.
- Quick to set up
- Tailor-made for data warehouses (Bigquery, Snowflake, Redshift)
- Graphical UI to connect all the modules
- Easy to learn
- Customer response time needs improving
- SAAS model instead of charging hourly
- Lack of documentation
- Versioning logs not updated
August 17, 2020
My experience as a newbie to Matillion
We utilize Matillion to transform data from multiple source systems into a cloud-based database. Our department is the sole user of Matillion and is managed exclusively by our team. Matillion allows our organization to consolidate our various data sources into a singular environment where our dashboard tools access data to provide valuable insights into the various units within our organization.
- Connection to numerous data sources
- Validation of objects and components
- Ease of use to schedule run-times
- Variable driven code development
- Documentation examples
- Speed of processing
- Requires upfront investment in design of system processing.
- Assistance with upgrades
August 14, 2020
Matillion is fast, fairly flexible, and easy to use
It's being used by our BI team, to ingest data into our Data Warehouse and distribute primarily to clients via Tableau, GSheets, emails, SFTP, etc. We also use it internally. Almost all our usage is for loading data although we use some transformation components when bringing in semi-structured data. Our team supports the entire organization.
- Move large amounts of data quickly from various sources into Storage/Data Warehouse. This is important for any ETL tool.
- The built-in API connectors are very useful and are usually granular enough to save us loads of development time.
- Matillion usually responds to support tickets in a day or two.
- A couple of times, we've had components stop working. We were basically told to keep checking the release notes for fixes. It would be nice if they prioritized maintaining existing components over developing new ones.
- It can be hard for multiple users to work on the same job.
- Most of their documentation is good, but sometimes their support pages link to missing docs.
We use it to take data from many sources and ingest them into our data lake. We then use Matillion to orchestrate transformation jobs on that data to eventually land it in our data warehouse.
- Variety of connectors
- Graphical interface
- Source control integration
- Some connectors have significant limitations (web services, NetSuite)
- Runs out of memory easily
- Logging not easily exportable
August 13, 2020
Matillion Is Easy Data Pipelines!
Matillion is being used by Data Engineering, which is a part of the Purple Analytics department. No other departments are using it. Matillion helps us automate our data pipelines. It gives us a platform on which we can schedule Python scripts, and provides an easy workspace where we can design and implement our workflows. The Matillion server is easy to maintain, which was a problem before we switched from our previous platform. Matillion makes it easy to perform ETL operations on our data, whether it starts in our data warehouse or not. It gives us plenty of flexibility in how we design our jobs. And it's super easy to use.
- The workspace is drag and drop, which makes it intuitive and easy to use.
- Server creation and management is robust. We haven't had to worry too much about it once we got it created.
- The job scheduler is very simple and intuitive.
- Matillion allows you to run Python, which grants almost unlimited flexibility, even without using any other components.
- The Python script component needs a friendlier window in which to edit your scripts. The script is not searchable, and tabbing is frustrating.
- The High Availability server configuration was not working for us. It was allowing duplicate jobs to run, and causing a lot of confusion in the scheduler. Love the idea, but the implementation fell short.
- Better alerting around queued jobs would be nice. Sometimes jobs start queuing and nothing runs. Usually this is the result of a badly written job, but it would be nice to get alerts.
March 05, 2020
Good at its core, but not a commercial dev tool
We are using Matillion in our global IT department to process incoming revenue data from hundreds of sources around the world.
- Processing large data
- Easy to use UI
- Simplified the building of complicated SQL
- GIT implementation is shocking
- Multi-developers is almost impossible to manage
- Variable handling is time consuming
- No telephone support
June 28, 2019
Excellent Entry-Level ETL/ELT Resource
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
June 18, 2019
Lean & Mean Data Integration with Matillion
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
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.