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…
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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%
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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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- Premium Consulting/Integration Services
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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 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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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.
June 08, 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).
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.
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.
May 29, 2019
Matillion to go
Matillion is being used as a data pipeline tool, and we use it to pipe data from Postgres to Redshift, and also for piping other external APIs directly using Matillion connectors. It helps in removing and having a separate data end-team for maintaining jobs written in code basics, and it's easy to maintain.
- Fast data transfer.
- No coding required.
- Lots of connectors.
- Matillion support.
- Need Github connectors.
- More learning materials based on common use-cases.
May 28, 2019
A good solution for a one man data shop
It was a self-hosted SaaS ETL for us for all data warehousing, connecting all internal and external data sources.
- Easy to learn
- Visual interface
- Pretty reliable
- Version control
- Search
- Explaining dependencies
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?)
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 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
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.
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 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 24, 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).
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
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
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.
May 15, 2019
Excellent ELT product that helps deliver rapid value and clear, easy to maintain transformations
Used to re-platform on-prem SQL servers to Snowflake. Also used to create data warehouse transformations, marts, and reporting tables. We also used it to remap several Excel-based calculations into an automated pipeline with backup and recovery on S3 and Snowflake. As consultants, we recommended the product, provided initial implementation and ran training workshops tailored to the client's tasks and workflows.
- Excellent visual layout of transformation jobs.
- Easy debugging while building SQL transformation by allowing you to sample the data at any point along with the job.
- Good connection to many different sources.
- Good auditing of jobs, steps, and operations.
- Poor SQL query generation for performance. It only does subquery composition, so becomes very inefficient on large tables.
- Limit scheduling and triggering capabilities without creating separate apps to call via API.
- Lack of on-prem file support, such as moving a file once processed, checking last modified date, etc.
May 15, 2019
Small scale Matillion user
As a ETL tool to pull data from various data sources for which Matillion had data connectors into a central database.
It was used only by the Marketing department. Since Matillion had its own native connectors to certain platforms, it was easy to pull data from those sources. And since we didn't have a developer to write scripts for those connectors, someone less tech savvy could do it themselves by dragging & dropping. Also since Matillion was responsible to keep its native connectors up to date, a developer wasn't required to read and implement all new API documentation if there were certain changes.
It was used only by the Marketing department. Since Matillion had its own native connectors to certain platforms, it was easy to pull data from those sources. And since we didn't have a developer to write scripts for those connectors, someone less tech savvy could do it themselves by dragging & dropping. Also since Matillion was responsible to keep its native connectors up to date, a developer wasn't required to read and implement all new API documentation if there were certain changes.
- Native data connectors
- No fixed fees
- Good support
- Development timeline
- More data connectors to various ad platforms
- Need the instance live to do setup, which you then pay for
- Initial setup is very technical
- Ease of Administration and Management
- Better visibility over ETL processes
- Seamless solution with complete integration into Snowflake
- Authentication methods need to be broader
- UI for projects could be better
- Ability to manage it using both AD and local accounts.
It is used by the analytics department as a replication tool.
- Replication
- Ease of use
- Trainable
- APIs
- Customer representatives aren't the best at communication but solution architects are great.
- Pricing explaination
- Update documentation
May 11, 2019
Matillion ETL for Amazon Redshift
I met with Matillion team at AWS Summit 2018 at Sydney, Australia. At that time we were using legacy ETL for our Redshift Data Warehouse.
We were trying to adopt Continuous Integration/Continuous Delivery framework for our ETL and found it to be challenging for our environment. It was time to look for something new and say goodbye to our bellowed legacy ETL. So we had a conversation with Matillion team and quickly decided to do a POC. We've evaluated couple of other tools in the past but nothing came close to Matillion.
First of all, setup was a breeze. The migration was quick and painless, the system is very easy to work with. Almost more importantly, the Matillion team is very friendly and always willing to help, their support is outstanding! Matillion has dozens of native connectors out of the box, so we had no problem with integrations at all. In fact, migration to Matillion allowed us to drop couple of third party legacy ETL components required for AWS infrastructure.
Python scripts component allows to use Boto3 and hundreds of other libraries, so practically there is no limit of where you can use Matillion.
We were finally able to build full scale CI/CD pipeline. Our setup uses combination of JIRA, Bitbucket and Bamboo build server and it works like a charm, thanks to Matillion'™s REST API.
- Works very well with Redshift and integrates with other AWS Services, such as S3, SNS or SQS for example
- Has scripting components like Python using Boto and any other libraries. Additional libraries need s to be installed on Matillion EC2 instance
- Plenty of data sources out of the box, the rest can be pulled via API
- Automatic validation of database objects and components
- Easy to install
- Excellent integration with CI/CD
- Minor: Changes to the ETL can only be reviewed in Matillion GUI rather than true source code diff, i.e. Bitbucket