Excellent ELT product that helps deliver rapid value and clear, easy to maintain transformations
May 15, 2019

Excellent ELT product that helps deliver rapid value and clear, easy to maintain transformations

Anonymous | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User

Overall Satisfaction with Matillion

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.
  • More visibility of data flows and where business rules are applied.
  • Rapid development and improvements for quick returns.
  • Somewhat fragile ingestion pipeline (single EC2 failure or corruption if updating) means some monitoring and the manual backup is required.
Very intuitive to build and follow jobs. Excellent documentation and online support make it great for quick turn-around projects. Self-documenting jobs help with understanding. When building the SQL transformations, the ability to debug, sample, and filter out the returned results at any point in the job makes it so easy to test and understand what is going on.
We were able to successfully deploy everything into production in less than 4 weeks. This includes setting up the Matillion (and Snowflake) environments, connecting to on-prem databases, pulling stage data, building the data warehouse transformations in Matillion, and exporting the results into reporting tables. We also had enough time to implement integration straight back to Excel, S3 and full production monitoring with SNS.
Scalability of jobs in parallel is okay. But you have little over how many jobs will run simultaneously as it is basically controlled by instance size. Also as your jobs get bigger, or longer, you have less ability to trigger additional vertical scaling. This is particularly noticeable when doing significant python scripting or file management.
Generally, Matillion is much easier to get up and running, quicker to deliver value and returns on your ELT process, and simpler to maintain. The speed to get data connections and start pulling data is so quick that you can focus on the transformations and business rules required. However, once in a larger pipeline, with many different triggering requirements or similar, it lacks some of the flexibility and scalability.
Same as the pros and cons. Matillion is great for quick integration to may sources, provides an excellent interface to build, debug and test SQL transformations, and has an easy to use schedule. But it has limited on-prem functionality to manage file ingestion, external logs, and triggers. It also performs poorly with queries over large tables due to subquery reliance.

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