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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How Matillion Differs From Its Competitors
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Popular Features
- Simple transformations (124)8.181%
- Connect to traditional data sources (122)7.777%
- Complex transformations (123)6.464%
- Testing and debugging (109)5.555%
Reviewer Pros & Cons
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What is Matillion?
Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality…
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- No setup fee
For the latest information on pricing, visithttps://www.matillion.com/pricing
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- Free Trial
- Free/Freemium Version
- 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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Reviews and Ratings
(204)Attribute Ratings
Reviews
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March 29, 2024
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 in the past for:
Replication - Copy from postgres, load to s3, perform transformation in redshift
Running python scripts
S3 data transfers - bucket to bucket
Maintaining source control in github is important to us.
We have used Matillion in the past for:
Replication - Copy from postgres, load to s3, perform transformation in redshift
Running python scripts
S3 data transfers - bucket to bucket
- graphical user interface
- Moving around widgets
- options for out of box operations
- connections
- source control maintenance (sync w/ github)
- poor logging, in my experience, can't see clearly what error is if something fails
- in my experience, difficult to connect with outside tooling
March 25, 2024
Experience the ETL with Matillion
We use Matillion ETL (extract, transform, and load) with Bigquery to transform customer data to a standard format. There are 100+ sources and file formats are different. We use Matillion to transform these files and apply business logic to create and store it in a standard format which can be used by downstream and we deal with terra bytes of data on a daily basis.
- The jobs logging UI is very unique and helps in easy debugging
- It has a proper hierarchical structure. One can easily organise projects and related pipelines
- Access control and sharing necessary access is easy and quick
- I have seen good performance even with complex pipelines
- More features should be available with Git integration such as passing environment variables, schedules from git
- Need improvement in parallelism of job runs
- Sometimes cancelling a job gets stuck which can be improved
March 19, 2024
Matillion - Decent
We use Matillion to schedule and run our ETL jobs. This helps our company to have accurate and timely data in order to make data-driven decisions. Matillion runs and organizes almost all of our data.
- Scheduling ETL jobs
- Third party API connection components
- Not enough third party API integrations
- No version control easily usable
Matillion ETL is used to extract, transform and load data into the Data Warehouse in Snowflake from data sources like Oracle databases and external CSV files. Matillion is used both for managing, scheduling and controlling the ETL processes as well as the data loading and transformations.
- Executing Snowflake scripts
- Oracle Database Connection and Data Retrieval
- Low Code programming by setting properties of the different components
- Parameter Passing between jobs
- New components for supporting other programming languages like R
- Upgrade Send Email component with more features
- Upgrade GIT Integration features
March 15, 2024
Good easy to learn ELT tool
- Extracts Nested JSON
- Had good DB support for multiple products.
- Runs in Azure where your Cloud is (may be).
- Upgrade process is sometimes quirky with no updates listed when there are clearly newer versions.
- Support is sometimes long winded and multiple people have to get involved.
- Docs could be expanded as not enough installed base to make forums or Google results useful in many cases.
We needed a way of empowering people with data in a no-code way. Data is a bottleneck and everyone needs it, but how do we give them this data and enable them to use it effectively? We launched ‘Data as a Product’ - A package of tools and resources that exposes raw data to people and allows them to manipulate it with ease. Matillion is the key tool in this package that allows people to manipulate data at the speed of thought with no code. Matillion’s no-code design, intuitive interface, and collaboration centric architecture allowed us to get people set up and transforming data in a matter of minutes with very little support from the Data team.
- We leveraged Matillion’s no-code principals to make data manipulation easy for our internal customers. People who don't know how to use SQL no longer need to. Everything in Matillion is self-explained with no or little coding.
- We connected Matillion to our data warehouse to allow people to read raw data, transform it, then write results back to their sandbox databases. The drag and drop component design allowed customers to create complex data models at the speed of thought without any risk to production data.
- With sharing capabilities between projects enabled, everyone was able to help each other when questions arose which instilled a strong sense of collaboration and community.
- The new DPC version of Matillion uses Git principals like Commit, Push, Merge etc. This is perfectly fine for engineers, but for our use-case it means our customers will need to understand a basic level of Git. It would be great if they had an auto-commit-push setting which does it all for them.
February 07, 2024
Matillion Review
We use Matillion for loading data from various sources into Snowflake Data Lake. We have data in various source systems such as SQL servers, many SAAS applications with proprietary databases, Quickbooks, files, and so forth. It is important to have data from all of these sources in the data lake for integrated reporting for the business.
- ELT - Extract Load and Transform
- Mostly a direct copy of data into the destination is handled very well
- Managing secrets and all connections being handled using parameters/variables
- Some connections are not straightforward to set up
- Upgrades can get a bit complicated and require a backup instance to be created
- Ability to email data files
February 07, 2024
Replication with Matillion Saves Time
We replicate data between PostgreSQL and Snowflake for most of our core business operations. We were using various home-grown techniques for doing that which took a very long time to complete due to the growing size of the data. We turned to Matillion for help and started first with their original CDC product that was part of the ETL server. That could not handle the volume that we pushed, so we switched to their developing CDC product in Data Loader.
- Provides seamless, end-to-end replication
- Works tirelessly with the customer if there are any issues
- The customer service team needs to improve interaction with the customer
- Provide documentation to current customers on new features that are added so that we don't have to find out either ourselves or have to search through the FAQ pages
January 29, 2024
Great ETL for the wider organisation.
As a fast prototyping POC tool for end users of data and in production etl.
- Easy to learn.
- Easy to show complex calcs to stakeholders.
- Data lineage.
- Billing model.
- Git integration.
- Metadata management.
September 09, 2023
Matillion Helps Build Out Your Datalake
We get a lot of different files from different partners/vendors. We also have multiple databases that all need to be centralized. We use Matillion to read in the data from these different files and databases into a DataLake. That datalake then serves the rest of our company from reporting to billing.
- File Imports
- Large Scale Data Manipulation
- Database Synchronization
- File Manipulation After Processing
- Built-in Error Reporting
- Versioning
Matillion is widely used as an ETL tool. It helps to transform and load gigabytes of data in a quick and efficient way.
- Really good user interface
- No complexity for anyone without much technical experience to get familiar with the tool
- Efficient in loading and transforming data
- Prevention of application crashes during huge volume of data load
- Improvement of button and default home page of UI
- Font size too small for few tabs
April 04, 2023
Great tool. Does a lot of the hard work for you. And lets the engineers focus on the data.
ETL tool that integrates with all major data warehouses as well as other internal data sources. The platform is easy to use. Support is detailed and timely. Upgrades can be performed in a blue green manner. And the product has robust backup solutions. All in all a competent and easy to integrate platform that does a great deal of the heavy lifting for you.
- ETL
- Data manipulation
- Integrates with data warehouses
- Containerisation. Hopefully this will arrive soon.
- Billing model. New hub model is much better but the previous market place model wasn't very flexible.
- External AD solution would be a big win.
September 02, 2022
Matillion a great, easy to implement resource
We use Matillion for all our redshift data warehouse ETL needs.
- 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.
We are using Matillion to centralize our data to a new data warehouse. Our primary use case is pulling data from a relational database (MySQL). I've been able to implement three different flavors of data sourcing, the two primary types being a full table load and incremental data loading of changed data into Slowly Changing Dimension and Fact tables in the DW. The performance of Matillion combined with Snowflake is astoundingly fast. We are also able to hit APIs to Zendesk and Hubspot easily to round out our integration with other SaaS vendors in our stack.
- 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.
Score 10 out of 10
Vetted Review
Verified User
Matillion is helping resolve use-cases related to transparent orchestration workflows. These workflows often involve custom Python, SQL, staging locations, and sending data to external systems such as Salesforce and cross-channel marketing platforms all in one workflow. When FiveTran & DBT don't meet our exact requirements and use-case, we use Matillion to help resolve these use-cases in an easily understandable workflow that technical users, new engineers, or non-technical users can understand/troubleshoot.
- 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
July 08, 2021
Ease and complete tool, I would suggest it!
We have been using Matillion for a specific project in the SupplyChain area. The ETL works well and it was really easy to implement. We didn't use many objects, but we still were able to implement everything we needed, such us datamarts and file exports. We also tested the connection with SAP and we were happy to see it worked correctly. Something I think could be useful is a tool to move jobs between different environments: export/import upload. Json is ok but doesn't allow us to manage to version, and moreover, if you are moving many files, you need to manually substitute the original job with the new one.
- 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 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.
June 29, 2021
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.
November 30, 2020
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)
November 27, 2020
Good at what it does.
Being used by the financial services department for ETL processes. It does the job of cleaning and transforming our data for internal and external reports.
- 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.
October 01, 2020
Matillion ETL - excellent cloud based data tool
Matillion is our ETL tool to populate our data warehouse running in Google BigQuery. In conjunction with Google Data Studio, we have a daily process used across the whole business with up to date financial, commercial and project data. It is managed by the Technology Team but the whole business sees the results.
- User friendly.
- Build complex workflows visually.
- Support is good.
- Easier email integration to mail out results.
- Version control of jobs.
- Ability to use external APIs to push data not just pull.
September 01, 2020
Matillion - the swiss knife of ETL
We use Matillion as the main ingestion tool within the business intelligence department.
We use it to ingest data coming from different legacy systems, outputting data in the cloud datalake solution.
It's really easy to learn, configure and spend less time ingesting the data and more time getting insights from the data.
- Python integration.
- Easy to set up, back up and restore.
- Scalable, works for any and all file types.
- Python libraries.
- Bigquery metadata.
August 26, 2020
Simple and powerful data aggregation tool
I use Matillion for one project. I use Matillion to solve my data loading issue because Matillion supports many data sources. I use this to aggregate all the data sources to my data warehouse. Before using Matillion, I needed to write my own data loader, which was very time consuming and created a lot of stability issues. By using Matillion, I can load my data within an hour to my warehouse.
- Quick to set up
- Tailor-made for data warehouses (Bigquery, Snowflake, Redshift)
- Graphical UI to connect all the modules
- Easy to learn
- Customer response time needs improving
- SAAS model instead of charging hourly
- Lack of documentation
- Versioning logs not updated
August 17, 2020
My experience as a newbie to Matillion
We utilize Matillion to transform data from multiple source systems into a cloud-based database. Our department is the sole user of Matillion and is managed exclusively by our team. Matillion allows our organization to consolidate our various data sources into a singular environment where our dashboard tools access data to provide valuable insights into the various units within our organization.
- Connection to numerous data sources
- Validation of objects and components
- Ease of use to schedule run-times
- Variable driven code development
- Documentation examples
- Speed of processing
- Requires upfront investment in design of system processing.
- Assistance with upgrades
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