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…
Recent Reviews
How Matillion Differs From Its Competitors
Awards
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Popular Features
- Simple transformations (124)8.181%
- Connect to traditional data sources (122)7.676%
- Complex transformations (123)6.464%
- Testing and debugging (109)5.454%
Reviewer Pros & Cons
Pricing
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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…
Entry-level set up fee?
- 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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Product Details
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- FAQs
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 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
October 06, 2020
Beautiful UI and UX to compete with any ETL, but at a cost!
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.
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.
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
March 05, 2020
Good at its core, but not a commercial dev tool
We are using Matillion in our global IT department to process incoming revenue data from hundreds of sources around the world.
- Processing large data
- Easy to use UI
- Simplified the building of complicated SQL
- GIT implementation is shocking
- Multi-developers is almost impossible to manage
- Variable handling is time consuming
- No telephone support
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.
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 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
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.
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
April 18, 2019
Turned a Scrappy Startup into a Polished Engine
We are using Matillion as a way to automate our manual ETL processes for getting a wide variety of data loaded into Snowflake on AWS. It is a core piece of our Data Engineering and works for us as it generates SQL (no lock-in) and works great for a startup given the low-cost profile (pay-as-you-go).
- Transformation Jobs as a way to build up complicated SQL
- Variety of data source connectors - we didn't think we'd need them but turns out we did
- The ability to call out to bash or python as needed
- Security (Auth/Azn) - auditing is really good
- Difficult to structure job flow sometimes (unconditional vs failure-to-exit - which takes place first)
- Management of Job variables and environments