Filter Ratings and Reviews
Filter 103 vetted Matillion reviews and ratings
Reviews (1-25 of 60)
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
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.
June 11, 2019
Matillion Review
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.
May 27, 2019
2 Years with Matillion
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.
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.
May 23, 2019
Ease of Use and Reusability for Excellent ROI
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)
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.
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.
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 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.
May 24, 2019
Matillion has met the needs of our data warehousing and ETL better than we had exptected
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.
May 23, 2019
Great product and great customer support
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
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.
May 22, 2019
Matillion For All
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
May 22, 2019
Feedback on Matillion ELT
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.
May 18, 2019
Matillion: ELT vs ETL
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.
June 07, 2019
Matillion ELT for Snowflake
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).
May 27, 2019
Matillion for Snowflake - Excellent (cloud-only) solution for ELT pipelines and complex transformation needs
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
June 17, 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
June 13, 2019
Easily set up your ETL/ELT pipeline with Matillion
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.
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 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.
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?)
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.
May 23, 2019
Matillion Review
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 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
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.
Matillion Scorecard Summary
Feature Scorecard Summary
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
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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 |