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.
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 …
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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- Connect to traditional data sources (91)9.292%
- Testing and debugging (82)9.292%
- Simple transformations (92)9.090%
- Complex transformations (91)7.878%
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- Tech Details
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.
- Supported: Connect to traditional data sources
- Supported: Connecto to Big Data and NoSQL
- Supported: Simple transformations
- Supported: Complex transformations
- Supported: Business rules and workflow
- Supported: Collaboration
- Supported: Testing and debugging
|Deployment Types||Software as a Service (SaaS), Cloud, or Web-Based|
- I is really simple and good.
- Drags and Drops.
- I think the price could be a bit cheaper.
- 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.
- 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.
- 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.
- 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
- 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'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.
- 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.
- 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 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.
- Just drag and drop, good to do.
- Very simple to use.
- Good features.
- Support responds a bit late.
- Breaks jobs down
- Graphics Interface
- Has lots of AWS documentation but not as much Azure.
- Automation and scheduling
- Security and authorization
- Ease of use
- API Calls using python
- More community support and forums
- Supports a wide variety of digital platform connectors, which could be helpful for any industry working to automate any of their reporting needs.
- Support for AWS technologies adds to greater advantage.
- Takes up unique functionality provided by the database into account which is very helpful.
- Also provides direct SQL query feed-in option for any migration of existing solution.
- With the added functionality available in Matillion, the understanding to use complex features becomes challenging for a new development team.
- Updates are regularly provided by Matillion support team but then they fail to specify the release document, new features or updates carried out in each update.
- Validation failure in scheduled jobs is sometimes encountered without any reason or proper RCA.
- Complex user management flow.
Training on Matillion and getting the development team on-board was quite a challenge. Would require immense experience to explore the core functionality of Matillion. Matillion was running with-in a week time after acquiring a license but for the development team to work efficiently it took a couple of weeks.
- Components available to do work with any source
- Ability to connect to sources without preconfigured component with extensability
- Able to kick off jobs from SNS, SQS
- There are so many options that the learning curve could be long for a newbie
- Can only parallelize the load in 16 partitions so it can't make use of parallelism of Redshift
- Menu items for admins may not always work and would have to resort to shell scripting (offered)
- It 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)
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.
- 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.
- 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
- 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.
- 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.
- 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.
- Clean interface.
- 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.
- 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.
- 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.
Less suited - Pushing output other than CSV or writing to tables.
- 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.
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.
Took 1 month to make it useful in production, and 9 months in, I'm still learning features and quirks of Matillion.
- 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.
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.