Matillion, simple but powerful
May 03, 2019

Matillion, simple but powerful

Krishna Naidu | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Matillion

Matillion is used by the Data Engineering team to load data into Amazon Redshift and to implement data transformations that produce the reporting layer of the data warehouse. The reporting layer is used across the organisation from everything from customer analytics to financial reporting.
  • Loading data into Redshift using bulk load utilities.
  • ELT makes use of Redshift's MPP architecture.
  • Very good UI, and intuitive to use.
  • Choice of Python for custom code is good as it's an easy language.
  • Connectors to streaming solutions like Kafka would be a good addition.
  • Some legacy component connectors are not available.
  • Reduced setup, and software update costs.
  • Reduced training costs due to intuitive UI and lots of good documentation and how to videos.
The UI is one of the best for ETL tools. The use of Python for custom code is also smart as it's an easy language to pick up.

Lastly, excellent documentation, much better than the competition. You can see a lot of time has been invested in the documentation and how to videos.
Set up was done in less than an hour. This is a huge saving. We ingested about 130 data sets and about developed about 60 transformation steps in 5 months with a team of 4.
Does really well for batch ingestion. I do not see a viable path for use as a streaming platform as out organisation matures and when the appetite for near real time data grows.
Matillion is easier to set up and use. Works well for cloud data warehouses. Falls short when legacy application connectors are required, this is where Talend and Informatica have the upper hand.
Matillion is great for cloud data warehouses. It's easy to get set up and use straight away. It's easy to purchase and comes through in AWS billing.

Direct integration with Kafka would complete the solution for us.

Matillion Feature Ratings

Connect to traditional data sources
Connecto to Big Data and NoSQL
Simple transformations
Complex transformations
Data model creation
Metadata management
Business rules and workflow
Testing and debugging
Integration with data quality tools
Integration with MDM tools