Fivetran vs. Maia by Matillion vs. SAP Data Quality Management

[Fivetran is] very well suited when you are using popular and common data sources, such as the major ad platforms, and SaaS platforms such as Salesforce. If the majority of your data sources are custom internal applications or databases, may be less value as you aren't leveraging the delivered connectors.

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

When reporting, we use accurate data with no duplications since they are addressed by SAP DQM, we get the right target audience by analyzing marketing data, and also helps us to understand the current situation of our firm by comparing metrics.

Incentivized

• Simplified ETL from a wide range of data sources
• Stable and painless data pipeline
• Granular control over what parts of the data source are loaded

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

• SAP Data Quality integrates with numerous data sources, all with quality control metrics for effective analysis.
• SAP Data Quality deals with modernization and addressing validation procedures.
• Permanently eliminates the available data redundancy and duplicates.

Incentivized

• Doesn't include support for a Kinesis stream as a data source so couldn't be used for some use cases under consideration
• Doesn't support the use of "BEFORE DELETE" triggers
• No support for serverless Aurora

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

• On data mining, some fundamental features are either not strong, or unsustainable.
• Besides, the chatbot from SAP Data Quality sometimes fails to respond, a huge challenge.
• Finally, SAP Data Quality lacks the performance monitor, which should be routine and concrete.

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

Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

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

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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

As with other SAP products, there is excellent support from SAP on this new tool.

Incentivized

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

Incentivized

Fivetran came well with the connectors' availability and updates with the source changes. We had an idea on data requirements in our case which helped us to work out on cost implication and take a decision for Fivetran as a data provider for our organization. These were 2 places where Fivetran out-performed, other vendors.

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.

The client was on a SAP platform for most of their applications and also is planning to implement SAP Hana very soon. This tool was fitting good for the requirement of the client to manage the quality of the data and hence adapt it in the system. Also, it can go well with the BO reporting.

Incentivized

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

• Saved a lot of manual development days (unable to quantify)
• Accelerated the time to add a new source to the data warehouse a lot

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

• Data quality from SAP Data Quality Management brings accuracy and authenticity to the entire process.
• Further, SAP Data Quality Management allows clear validation of the customer address, making it effective to manage every process and demand plan.
• SAP Data Quality Management has the potential of improving the market penetration, powerful business proceedings.

Incentivized