Matillion vs. Oracle Data Integration Platform Cloud (DIPC)

Matillion is very easy to learn and develop quickly. It also allows complex orchestration on a visual platform. It has many ready-made components, and it is easy to develop new user components. Although Git integration needs some further development, it enables teamwork in the same project with the "Versions."

Incentivized

For daily transactional information and customer data needs it has been an extremely fast and reliable system. Several thousand users are able to connect many third party tools and run a large number of extracts while still experiencing great performance. For smaller systems in use with limited user accounts this system is less appropriate due to maintenance resources required.

Incentivized

• Matillion has a rich transformation library. It provides multiple functionalities, such as join, group by, pivot, various sources, and sinks.
• It provides the security capability as well. All the credentials can be securely stored in Matillion.
• Reusable templates can be built which reduces the redundancy.
• Time to production is very minimal.

Incentivized

• ODI uses ELT architecture, which enhances the load performance and is very scalable.
• ODI is platform independent. It supports all platforms, hardware and operating systems.
• It comes with pre-built integration, allowing immediate data replication, transformation, and data governance while ensuring data quality.

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

• Training and ramping up to learn all the Oracle specific intricacies.
• Security concerns with administering some of the tools to end users.

Incentivized

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.

Incentivized

Data modeling and SQL knowledge are still heavily recommended when using Matillion because designing a successful and maintainable data transformation requires modeling it with a developer mindset. The data transformation components are often a thin wrapper around SQL concepts, and they don't simplify life that much, IMO. An example is the window component, which cannot facilitate the complex concept of SQL windows just by exposing it to a different interface. On the other hand, low code ingestion is very good and usable, especially for GSheets, Salesforce, and others.

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 much easier to set up and easier to work for the team. Offers a lot more connections which are easier to set up. Environment variables make it easy to set up once and job creation is easy. We use Metadata tables to just loop through the list of tables that need to be loaded. So development is just a matter of seconds when there are new tables to be loaded.

Incentivized

We are longtime ODI on-premise users. So, it was easy for us to migrate to the ODI cloud rather than implementing another solution.

Incentivized

The current connectivity is set to provide information primarily in one direction; from simpler OLTP systems to big data warehouses. Allowing the big data warehouses (e.g., Snowflake) to be used as a source for transmission elsewhere would be a big extension to the product line and provide more flexibility in architecture.

Incentivized

• Our embedded data analysts (data analysts that sit in a team outside of the Data team) all now use Matillion to create proof of concepts (POCs). This allows them to debug logic at a component level and quickly explore ideas without investing lots of time and effort.
• Since the soft-announcement of ‘Data as a product’ (a beta launch) and demoing Matillion to some of our internal customers we’ve had a huge number of requests from people to get their hands on this new method of self serving data. We’ve yet to release the full product and make a company wide announcement, but early estimates show we can expect around 10-15% of the company to be onboarded and using Matillion as part of Data as a Product. Given the Data team only accounts for around 2% for the company's employees, that’s a huge increase in the number of people using and manipulating raw data!

Incentivized

• Ability to setup reliable disaster recovery.
• Upgrades and performance monitoring tools are readily available.

Incentivized