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November 30, 2020
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)
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.
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 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.
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.
August 14, 2020
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
We were able to get up and running very quickly. This is really Matillion's strength. Once we got past the peculiarities of setting up our environment, we could connect to data sources right away and ingest data into our data lake. Putting on more pieces after that was easy.
August 13, 2020
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.
We tested Matillion for about two weeks. By the time we were done testing, we were writing transformations. At that point, we were extremely comfortable with the tool, and we successfully migrated all of our existing code over to Matillion in a few weeks. The learning curve for Matillion is shallow.
Our entry level Data Engineers don't need any hand holding with this tool. A very quick demonstration is generally all that is required. After that, the tool is intuitive enough that even a beginner can use it.
Our entry level Data Engineers don't need any hand holding with this tool. A very quick demonstration is generally all that is required. After that, the tool is intuitive enough that even a beginner can use it.
October 06, 2020
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.
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.
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.
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.
October 01, 2020
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.
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.
October 01, 2020
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.
Setup took about 3 or 4 days. And I was able to begin transformation process right away. All in all, less than a week!
October 01, 2020
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.
On Google Cloud platform you just start a VM directly from Marketplace, it took us 5 minutes to start the instance.
September 08, 2020
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.
Almost everything is up and running, one needs to figure out Lambda and SNS stuff to maintain scheduled tasks.
September 06, 2020
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.
A couple of weeks were enough to get me comfortable. A couple of months were enough to make me feel like I understood it intuitively.
September 03, 2020
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.
After just two months we had complex processes running on a production environment. And now after six months, we became really proficient users of Matillion which are also taking care of optimization improvements and not just a development.
August 31, 2020
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.
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.
It takes less than two hours for Matillion to be up and fully operational. If creating a backup instance, all code and settings can be downloaded as json files and then reuploaded in the new instance.
August 29, 2020
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)
We got Matillion from the AWS marketplace, and we got up and running within less than a week. Starting the server was simple, then we had to do a bit of configuration and integrations with our servers adding the credentials, git repos etc. The experience was really good as Matillion blends perfectly within the AWS ecosystem.
August 26, 2020
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
It took me about a few days to study those advanced features, due to not much community support, but I still was able to get support from Matillion. For those standard features, you can run it maybe within a few hours.
August 16, 2020
We utilize Matillion to transform data from multiple source systems into a cloud-based database. Our department is the sole user of Matillion and is managed exclusively by our team. Matillion allows our organization to consolidate our various data sources into a singular environment where our dashboard tools access data to provide valuable insights into the various units within our organization.
- Connection to numerous data sources
- Validation of objects and components
- Ease of use to schedule run-times
- Variable driven code development
- Documentation examples
- Speed of processing
- Requires upfront investment in design of system processing.
- Assistance with upgrades
Matillion was already implemented at our company and any time requirements to learn the tool was based on my ability to translate my previous ETL coding experience into Matillion's structure. Our team inherited an existing ETL process built-in Matillion and were able to add/modify/delete new objects and components within several weeks of having access to Matillion. It was necessary to understand the business rules prior to making any changes, but the tool itself is very intuitive.
August 14, 2020
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.
October 01, 2020
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.
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.
January 05, 2021
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)
Within 2 days
October 05, 2020
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.
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.
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.
Estimate depends on the complexity required. Would estimate anywhere between 3-9 months.
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.
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.
October 01, 2020
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.
About 1 week which included hands on and looking at online documentation. Part of the delay was just getting access to internal company data.
October 01, 2020
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.
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.
Took 1 month to make it useful in production, and 9 months in, I'm still learning features and quirks of Matillion.
September 16, 2020
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
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.
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.
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.
Matillion Scorecard Summary
Feature Scorecard Summary
What is Matillion?
Matillion is data transformation for cloud data warehouses. According to the vendor, only Matillion is purpose-built for Amazon Redshift, Snowflake, and Google BigQuery enabling businesses to achieve new levels of simplicity, speed, scale, and savings.
Quickly develop custom Transformation jobs by combining Filters, Joins, Aggregates, Calculators, Ranks, as well as more complex transformations such as Rankings, window calculations, and change-detection by dragging and dropping these onto a canvas GUI. Use Orchestration features to automate your ETL workflows with scheduling, notifications and alerting, and control flow.
Once your jobs are built, run them with confidence with component level validation and data sampling. For any custom or unique needs, Matillion also has Bash and Python Script components giving you the ultimate extensibility and flexibility.
Shared Job templates, Dynamic and Grid variables, and Version control help your development teams share resources across your Matillion instance.
Enterprise customers benefit from additional auditing and permissions, automatic job documentation, data lineage and more. Learn more about enterprise features here.
Each product comes with a set of warehouse specific features that help you get the most out of the platform. See our Product Feature pages on matillion.com for more information.
Categories: Data Integration
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Matillion Pricing
- Has featureFree Trial Available?Yes
- Does not have featureFree or Freemium Version Available?No
- Has featurePremium Consulting/Integration Services Available?Yes
- Entry-level set up fee?No
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
For the latest information on pricing, visit https://try.matillion.com/matillion-a-general-page-demo/
Matillion Support Options
| Free Version | Paid Version | |
|---|---|---|
| Forum/Community | ||
| FAQ/Knowledgebase | ||
| Video Tutorials / Webinar |
Matillion Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |
| Supported Countries: | Global |
| Supported Languages: | English |