Matillion Data Productivity Cloud Reviews

Ease of use and a great way to present the ETL process to non-technical observers.

It has enabled building a data warehouse within three months from the ground up to support WMS reporting.

It would be great to have the ability to move windows around to dock in different locations/external windows.

This would involve an ability to physically pin, or move the windows within Matillion (for Redshift).

I know that they can be resized to some extent – but it would be great to collapse the windows, or move them -- similar to moving a tab from a browser so that it's separate from the main GUI.

We have been using this solution for six months.

We have not encountered any stability issues.

We have not encountered any scalability issues.

Matillion's support is great. They are fast to respond, always helpful, and they have great documentation. This includes articles, training videos, and regular software update building on customer feature requests. I would give the support a rating of 10/10.

I am not aware of any previous solution.

The setup was straightforward. The user interface is very intuitive and purchasing from the marketplace was simple.

If you are unsure, just send Matillion a quick email, and they’ll get back in touch to help.

We did not evaluate any other options.

Just go ahead and take advantage of the initial free period for evaluation. The on-demand price is also very competitive.

It is helping to make Makerbot a data-driven company.

The most valuable features are the components for SFDC, RDS, Marketo, Facebook, and Google AdWords; built-in verification; and scheduling, restarting & logging.

On a Redshift project, before Matillion was released, two people literally spent over one month using Sqoop to pull very wide data tables from Safesforce.com to Redshift. On a new project using Matillion, it took me 10 minutes to set up and begin importing data from Safesforce.com.

Built-in verification and sampling is a fabulous feature for ETL engineers. Anywhere along the transformation-pipeline, one can check, see and sample the data. This saves days & weeks of effort and leads to a far more agile project.

More frequent releases are needed, due to API changes from Google, Marketo, and Facebook. They frequently release upgrades to their API and consequently frequently deprecate the older version when only a few months old. The only way to use the Matillion components for these APIs successfully is for the Matillion release process to step up to the plate and have far more frequent "minor" API releases (as opposed to "major" product releases).

Even having these automated might not be a bad idea. Some customers willing to pay might open up a new revenue stream for "platinum" service, to take the headache out of this very valuable set of marketing components in Matillion.

I only encountered stability issues when accidentally performing EC2-intensive Python jobs (i.e., not Redshift-intensive SQL). These can kill the Matillion EC2 instance.

I have not encountered any scalability issues.

Customer Service:

Customer service is excellent.

Technical Support:

Technical support is above-and-beyond.

SnapLogic and Informatica: too slow; for MPP, they are just glorified and expensive Python schedulers.

Python scripts: high maintenance.

Initial setup was straightforward.

I implemented it myself.

We achieved ROI in <1 year.

The first two weeks are free; pay by the hour for smallest instance for next 2-3 months; after that, take out yearly discounted rate from AWS Marketplace for instance/devs in team.

We also evaluated SnapLogic, Informatica, Talend, and Hadoop.

The mindset of the traditional ETL tools is to off-load transformation to another server/DB. This totally misses the point of MPP and especially of Redshift. Load the data into Redshift early and then transform it inside Redshift ("ELT" not "ETL"). Matillion orchestrates the loading and transformation "pipelines", then gets out of the way whilst Redshift does what it is good at (i.e. the "grunt work").

The tool's primary use case is implementing Snowflake, including building a data warehouse atop our source systems and facilitating data exchange with customers and suppliers utilizing Snowflake.

The tool's middle-dimensional structure significantly simplifies obtaining the right data at the appropriate level. This feature makes deploying our applications easier since we utilize a single source without publishing data from various sources. Consequently, presenting data becomes more straightforward, and customer interactions involve no copying or pasting, streamlining the exchange process.

With Matillion ETL, we read data from different warehouse source systems. We create a unified version of the data in the data warehouse using the ETL functionality. Mapping tables address the challenges of different source systems, ensuring the data is at the correct level of detail. It is very strong and comes with much functionality, including monitoring. 

The tool's lineage is very weak. 

I have been using the product for three and a half years. 

The tool is stable. 

I haven't contacted the technical team yet. 

Matillion ETL's deployment can be done in-house. 

Matillion ETL is expensive. 

The tool seems to reduce complexity rather than boost performance. The overall process remains somewhat intricate. I don't think we've observed any performance improvements using my team. I rate the overall product a seven out of ten. 

In a project where we migrated from on-prem solutions to the cloud, we used Matillion and Snowflake, which streamlined the process significantly.

Matillion ETL's ability to conduct end-to-end data migration is valuable. We can create jobs, monitor them, and manage workflows effectively.

The product's scalability needs improvement. Perhaps adding more connectors would be beneficial.

The product is quite stable and can handle complex data integration tasks well.

I rate the platform scalability around six or seven. Depending on the specific architecture, parallel processing needs, and data types involved, it could be optimized.

I haven't contacted the technical support team, but the online documentation and community resources are quite sufficient.

A team of two to three ETL architects and data engineers is required to work on deployment. 

We can monitor and manage real-time data pipelines, analyze task logs, and automate data pipelines wherever possible. We also apply parameterization to improve efficiency using the product.

It adapts well to changing data volumes and types. I would recommend it, especially for cloud data integration.

I rate it a seven or eight.