dbt vs. Devart Excel Add-ins vs. Maia by Matillion

dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.

Incentivized

One of the biggest benefits of Excel is its primary function: The ability to organize large amounts of data into orderly, logical spreadsheets and charts. With the data organized, it's a lot easier to analyze and digest, especially when used to create graphs and other visual data representations. Excel crunches numbers almost instantly, making batch calculations much easier than working things out yourself with a calculator. Depending on your understanding and skill with Excel, the formulas and equations are used to quickly compute both simple and complex equations using large amounts of data.

Incentivized

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.

Incentivized

• user experience makes it easy to work with SQL and version control
• customer success team and the dbt (data build tool) community help establish best practices
• thorough and clear documentation

• Devart Excel Add-in supports connectivity with various databases - which really helps to customize and massage data.
• Since data is available in cloud, it is easily accessible everywhere.
• It can also be integrated with SSIS components making it possible to resolve complex problems.

Incentivized

• 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.

Incentivized

• Slow load times of the dbt cloud environment (they're working on it via a new UI though)
• More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros

Incentivized

• The data pump feature breaks sometimes
• The profile usage monitoring is slow
• The event profiler is delayed

Incentivized

• Static and monolithic, it will show its limits when running multiple concurrent jobs.
• Github and versioning implementation is messy and broken. Don't use it.
• There's not way to see/query the system resources, just wait for a server to crash due to out of memory. An admin panel would be appreciated + some env variables with updated info.
• API implementation is cumbersome and limited.
• There's no concept of hub and worker engine, everything happens of the same server (designing workflows and executing them). Having separate light ETL engines to run job could be better. (sort of docker/kubernetes/lambda functions).
• Handling of variables is limited especially for returned values from sub components.
• Some components could return more metadata at the end of their execution instead of the standard one.
• Billing is badly designed not taking into account that the server is hosted by the client. Expensive.
• We had several issue with migration where starting a new instance was required and then migrating the content. It was painful and time consuming also have to deal with support and engineering team on Matillion side.
• CDC doesn't work as expected or it is not a mature product yet.

Incentivized

Matillion is easy to use and flexible to debug. Performance are good and support is giving us a good service level. There are still some technical points to be developed more (such as SAP extraction). but easy flows are really fast to be developed. We are also using a tool for migration from other tools, and it is useful as Matillion is producing XML code.

Incentivized

dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.

Incentivized

Easy tasks are really easy, and complex tasks are still possible. With prior knowledge of general data warehousing principles and experience with other data transformation tools, it's straightforward to get familiar with and use Matillion. I initially used minimal external support from a partner for some more complex tasks but very soon could work entirely independently with Matillion.

Incentivized

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.

Incentivized

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

Incentivized

Matillion is graphical versus dbt, which is SQL code-based (that, of course, is a matter of personal preference and not an objective advantage). The integrated testing, documentation generation, lineage, etc., were additional criteria that led us to choose dbt.

Incentivized

Microsoft Excel is the most used tool in the software universe. As such, asking Microsoft Excel to be the tool used post data acquisition makes a ton of sense. Almost everyone we hire has some idea on how to use this tool, which decreases the time spend training new employees.

Incentivized

We selected Matillion primarily because of it's ability to connect to numerous data sources and easily create transformation jobs. While Fivetran does a better job managing and examining deltas, it is not easy to use and is very non user friendly. SSIS was not a good fit for our team and required a significant amount of attention and server management that we did not want to invest in.

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.

Incentivized

• In 3 months we re-wrote the data warehouse (15-20 sources) in dbt with 3 developers.
• We are using it continually for the past year with no issues.
• Sorry, I don't have ROI numbers but the impact was huge.

Incentivized

• The data compare option saves our dev team a lot of time
• The documenter feature is nice to use for explaining table definitions
• The event profiler is great for usage metrics

Incentivized

• Time savings -- we could custom code nearly everything Matillion does, but it would take days/weeks instead of minutes/hours.
• There's a bit of a learning curve to truly unlock Matillion's potential, and that can be frustrating for some new users, but once you get over that curve, the possibilities are endless.
• It allows us to centralize the hundreds of way to bring data in, so that even if you have to troubleshoot what someone else wrote, it's easy to jump in and understand what is happening.

Incentivized