Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed and scale that matches the business’s data ambitions. The vendor states enterprises including Cisco,
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Qlik Replicate
Score 8.4 out of 10
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Qlik Replicate enables organizations to accelerate data real-time replication, ingestion and streaming via change data capture, across a wide range of heterogeneous databases, data warehouses and data lake platforms.
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Pricing
Matillion
Qlik Replicate
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Matillion
Qlik Replicate
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
Required
Additional Details
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
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."
Qlik Replicate works very well with relational data platforms, both on premise and in the cloud, for example Oracle, SQL Server, PostgreSQL, MySQL and others, it also works very well with DB2. If the data source is MongoDB, it is more complicated and currently there is no possibility of sending data to MongoDB.
Replicate is extremely stable and does not generate a lot of alerts/failures/issues that take up time to troubleshoot.
It is very easy to add new source tables to a Replicate task so that we're always in sync with new data available from the CRM.
It's nice that Qlik Replicate also allows you to create a job to stop and then restart your tasks during maintenance windows that occur on both the source and target systems.
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.
The base Replicate web GUI is lacking. If you have dozens or more tasks, it's hard to get a sense of how they're performing. Enterprise Manager solves all of these problems but is a separate install.
The support portal is extremely difficult to navigate. It's hard to track down exactly what you're looking for.
It would be helpful to have better documentation and example queries for the tables in the Enterprise Manager analytics database.
Destination databases that don't support common DDL commands behave unpredictably. And the replication of schema changes isn't consistent.
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.
The availability of the replicated data in disparate environments has is now crucial. Replacing a product like Qlik Replicate would require significant time, investments, and work. In addition, Qlik Replicate is reasonably reliable with few failures.
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.
We now have greater business flexibility and scalability, and our big data integration projects have a quick rate of growth, which has been profitable for us. Independent of the sources involved, maintaining data consistency between sources is easy. One of my favorite features is the way it lets owners of the source system start and stop processes from updating their system windows.
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.
The issue I've had is that Qlik does an awful job of keeping their customers informed when new versions of the software are available. We found that we were using a version that was no longer supported and could never get help. When it came time to get us upgraded so that we were on a current version, no one knew how to help get us to where we needed to be. We had to purchased professional services time and even then I was basically on my own to get everything built out and set up. Qlik needs to be more proactive with communicating about new releases and how to get your version upgraded in the most secure, safe way possible.
Follow the directions from the Qlik documentation. They are pretty straight forward and easy enough to follow. If you follow these, then you are not likely to have issues on implementation.
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.
Great tool for data replication solution for Oracle/SQLServers/etc. Real easy to get it set up and start realizing business value. Getting the PoC accomplished in a short window. Product costing and easy to start small and scale as needed. It helped cover most of our ask compared to other solutions.
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.
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!
Prior to using Qlik Replicate, we used an ETL solution to copy data from the Oracle ERP system to the Microsoft SQL Server BI system at a 15-minute interval. It was very tedious to maintain. Qlik Replicate is much easier to use and we replicate data near real-time now.
