Actian AI Analytics Platform vs. Maia by Matillion vs. SAS Data Management

This is a tool geared for smaller to mid-sized business that has disparate sources of data from different platforms in varying incarnations. It’s a great ETL tool to solve the problems a scenario like that causes, but you can also achieve that with good BI Tools like Qlik Sense. So be careful that you really need an ETL tool, as opposed to an end-use tool with a built-in ETL component. If you are going ELT and have a lot of data an not a lot of corporate resources, this is a better option than Microsoft or Informatica

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Great: Need to query simpler APIs, or utilize well known services such as GSheets etc.? Matillion has got some of the best and easiest to use connectors out there. Not so great: Do you need have a competent CI/CD flow that you will be able to update / compare from Matillion as well as other sources at the same time? Good luck, you will need to be extra careful, as you might have to have a deeper dive into your servers Terminal each time you have a git conflict.

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When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.

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• Quick response for queries involving multi-million rows
• Low cost

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• The user interface of your data pipelines makes it easier for people who aren’t as techy as data engineers to observe what's going on.
• Customer support is quick, not always as efficient as you would want it to be, but still.
• Nice documentation available.

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• SAS/Access is great for manipulating large and complex databases.
• SAS/Access makes it easy to format reports and graphics from your data.
• Data Management and data storage using the Hadoop environment in SAS/Access allows for rapid analysis and simple programming language for all your data needs.

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• As I said before, more training or greater visibility to training tools/options would be a plus. It’s easy to publish YouTube videos these days, I think they should make more of them.
• Differentiation would help, there’s not a lot out there to drive you to buy the product if you are well informed in the market. If you know the market, you steer towards the large or trendy products. It’s a good product, but lost in the noise of the field I think.
• Hitching the wagon to a major software brand (like Mule did to Salesforce) would help grow the user base, and thus increase the activity in the support community. More users also translates into product champions.

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• Matillion is brilliant at importing data -- it would be amazing to have more ways to export data, from emailed exports to API pushes.
• Any Python that takes more than a few lines of code requires an external server to run it. It would be great to have more integration (perhaps in a connected virtual environment) to easily integrate customized code.
• Troubleshooting server logs requires quite a bit of technical expertise. More human readable detailed error handling would be greatly appreciated.

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• Requires third-party drivers to connect to common data sources like SFDC, MS SQL, Postgres.
• Debugging errors from the logs is a complicated process.
• E-mail alert system is very primitive and needs customization to make it more modern,
• Cannot send SMS alerts for jobs.

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With the current experience of Matillion, we are likely to renew with the current feature option but will also look for improvement in various areas including scalability and dependability. 1. Connectors: It offers various connectors option but isn't full proof which we will be looking forward as we grow. 2. Scalability: As usage increase, we want Matillion system to be more stable.

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We are happy with the software and its functionality. As a SAS-shop, DataFlux is a logical choice for complex data integration.

We are able to bring on new resources and teach them how to use Matillion without having to invest a significant amount of time. We prefer looking for resources with any type of ETL skill-set and feel that they can learn Matillion without problem. In addition, the prebuilt objects cover more than 95% of our use cases and we do not have to build much from scratch.

The main negative point is the use of a non-standard language for customizations, as well as the poor integration with non-SAS systems. However, there is no doubt that it is a high-performance and powerful product capable of responding optimally to certain requirements.

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It worked as expected.

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Overall, I've found Matillion to be responsive and considerate. I feel like they value us as a customer even when I know they have customers who spend more on the product than we do. That speaks to a motive higher than money. They want to make a good product and a good experience for their customers. If I have any complaint, it's that support sometimes feels community-oriented. It isn't always immediately clear to me that my support requests are going to a support engineer and not to the community at large. Usually, though, after a bit of conversation, it's clear that Matillion is watching and responding. And responses are generally quick in coming.

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With SAS, you pay a license fee annually to use this product. Support is incredible. You get what you pay for, whether it's SAS forums on the SAS support site, technical support tickets via email or phone calls, or example documentation. It's not open source. It's documented thoroughly, and it works.

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We were able to control on access and built various enviroment for implementation

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Oracle>DB2>MS SQL Server>GreenPlum>Vectorwise

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Fivetran offers a managed service and pre-configured schemas/models for data loading, which means much less administrative work for initial setup and ongoing maintenance. But it comes at a much higher price tag. So, knowing where your sweet spot is in the build vs. buy spectrum is essential to deciding which tool fits better. For the transformation part, dbt is purely (SQL-) code-based. So, it is mainly whether your developers prefer a GUI or code-based approach.

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Because of ease of using SAS DI and data processing speed. There were lots of issues with AWS Redshift on cloud environment in terms of making connections with the data sources and while fetching the data we need to write complex queries.

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We're using Matillion on EC2 instances, and we have about 20 projects for our clients in the same instance. Sometimes, we're struggling to manage schedules for all projects because thread management is not visible, and we can't see the process at the instance level.

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• We had to move out of VectorWise after using the database for 2 years. Hence no positive impacts.

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• Matillion has been the backbone of my company's analytical functionalities for 10+ years, so it has a good ROI.
• The price is ok for what our company built with it, but it starts to be less competitive if the tool is not used at its fullest.

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• We have more users who can connect to the many different data sources.
• Our users do have existing SAS programming knowledge and that can carry over.
• Business functions are starting to rely on SAS Data Integration Studio work product shortly after introduction.

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