Matillion Data Productivity Cloud Reviews

We primarily use Matillion ETL to effectively manage data's movement, ingestion, and transformation through pipelines. We have specific use cases that involve different types of data, but they all fall under the general bracket of data movement.

We've got customers that have used Matillion to stand up entire and data platforms. We have customers that use it on a daily basis to perform heavy data movements and pipelines. We have a whole load of different case studies for different customers for different technologies. For example, Optio used Matillion to actually build a full new data platform and those pipelines. 

The most valuable feature is the ability to put together pipelines that push down all of the logic into Snowflake. So none of the actual execution or none of the data you need to travel through. But we can do it in a GUI-based system, which is a lot easier to hand over to end-users. Most end-users, in my opinion, have an easier understanding of GUI-based tools than code-based tools. And Matillion just has a whole load of features that assist with that, they get integration allowing you to have the exact same as slow but apply it to different environments or push code changes through two different environments is really useful. And their ability to leverage different forms of iterators over variables control tables, etcetera, so that you can orchestrate a whole variety of things in one go instead of having to kind of train them up.

So the main thing I would like to see improved in Matillion are two things. Firstly, their ability to process concurrent workloads. Right now, the concurrency reaches a stalling point if too many things are added, and it gets stuck waiting for each one to finish. 

Secondly, Matillion needs an improvement in its backend integration and the way that it pushes things through. It is already good, but it could be cleaner. I will say that I think both of those issues are being addressed in the new platforms that are coming out.  Matillion Unlimited Scale is the new answer to concurrent workloads, and Matillion Data Productivity Cloud is their new software-as-a-service version of a Matillion ETL provider, including a deeper git integration. So my concerns are being addressed, but those are the two things that stand out to me the most right now.

I have been using Matillion ETL for four years. 

It is a stable solution. However, the stability depends on how you host it. If you choose to deploy it on a robust virtual machine with ample RAM and CPU, it will be more stable. On the other hand, if you put it on a smaller virtual machine, it might be less stable. 

From an architecture standpoint, Matillion is as stable as any other virtual machine. However, the software has some memory leakage issues that may affect its stability. I suggest restarting your Matillion instance regularly to clear out the memory. It's also a good practice to shut down the instance when not in use. We have some customers who keep the instance on for only 20 hours a day and have a process that automatically shuts it off and starts it again four hours later.

It is a scalable solution, but with the new versions coming out, they call it "unlimited scale." So, the latest version of Matillion is a lot more scalable. The current version, in my opinion, is a bit more limited because it's on a virtual machine, and everything executes on that one virtual machine. If you have more users, you may need to deploy additional virtual machines, which can be quite cumbersome and require keeping them in sync. However, the new unlimited-scale approach means having one designer and one front-end. 

All the workloads are delegated out to Elastic containers, so it's very easy to log in to the configuration for the containers, change the number of containers that will be active, and scale out that way.

The maintenance part can be a bit of a challenge. Matillion has come quite far recently. It used to be that in order to perform an upgrade, you had to set up a completely new machine and migrate everything from the old one to the new one. That was a lot more of a headache. These days, they do support in-place upgrades. So, you can just click through a few app admin options, perform an update, and it will update the instance. However, if you go that route, you might still need to perform a full migration over the span of a year or so by setting up a new one before the full migration. It is because the more the underlying processes change, the more those in-place updates eventually fall behind, especially in terms of keeping the VM itself up to date. In-place updates only really update the Matillion application running on the VM and not the underlying libraries, etcetera. 

Over time, I have escalated quite a few things to the customer service and support team. They're good. They respond not just to our partners or us, but we've seen customers raise questions themselves, and they usually get a response from Matillion within a day. 

Matillion is always happy to jump on a Zoom call or something to try and resolve the issue. Also, they have an active ideas platform. So, if you go to the Matillion forum, there's an ideal area. They are quite good at taking the most uploaded ideas and implementing them into the tool going forward. I think there's a nice combination of the support dealing with existing issues and the developers trying to improve based on the community's input.

Only one person is required to deploy the solution. The process is relatively straightforward and takes around one and a half hours. 

The deployment model for Matillion ETL depends on the customer since we are a consultancy with several customers in different capacities. However, the majority of our customers use the cloud-hosted equivalent and host it in their own cloud environment. We don't have any customers who host it on-premises, although it is possible. Customers typically host it on their platform.

Additionally, we do have access to Matillion's own as-a-service beta, but it is only in a private preview environment. We only use it for general testing and experimentation.

For the deployment process in the current world, you would log in to Resilient and create an account on Matillion Hub. That is the process that allows you to get started with Matillion Hub. Matillion often helps you with this process if you are a Matillion partner. You go through Matillion Hub, put in your details, and then you can decide which platform you want to deploy your virtual machine to. You can choose between Azure, AWS, and GCP.

When you choose a platform, it will give you a template report bespoke to that environment. For example, if you're deploying to AWS, it will give you an ARM template. If you do it on Azure, it will provide you with a kind of shared instance template or a VM template. Then you just need to configure a few options, and it will deploy the instance to stand up for you. That's the current process.

In the new world, when Matillion launches its Data Productivity Cloud, things will change. It's delivered as a service, so I imagine the deployment will be much simpler. But my understanding is that it will still leverage your own custom zone containers to process the workloads. So the process will be very similar to what's currently used to deploy the virtual machines, but it will be used to deploy the elastic containers to which the workload will be pushed out.

I have seen ROI. For example, one of our customers in the UK has used Matillion quite extensively. They had a challenge where they were a group of different smaller companies, and they wanted all their engineers to work collaboratively on a single platform. That's where Snowflake and Matillion came in. They have one instance that's managing to serve all of these different sub-companies and sub-engineers, and they are easily recuperating the cost of that in order to provide data. But it is worth noting that they're not a profit-based company; they are a public health service, so it's more that they are saving money as opposed to making it.

The current pricing is based on consumption. So when you spin up a virtual machine, the size and type of the machine will determine the hosting cost by the provider, like Azure. 

Before considering the licensing cost of Matillion itself, you need to consider the cost of hosting the virtual machine, which is an additional cost. Matillion charges one credit per hour for each virtual CPU that the VM is using. So if you choose an 8-VCPU virtual machine and run it for 24 hours a day, the cost can add up quickly. The price per credit varies depending on your tier, but I think it's around $3.50 per credit for the top tier of Matillion and $2 per credit for the lowest tier. If you Google Matillion pricing, you can quickly find the dollar amounts. But you can manage these costs by shutting down your instance when you're not using it with an automated process.

In my opinion, none of the tools in this particular space right now are perfect, including Matillion. However, Matillion seems to be the best of the bunch. Its UI looks a bit dated at this point, but I find it reliable and relatively easy for users to get going with. 

There's not a big learning curve in Matillion. You can log in, and within about half an hour or an hour, you can know how the general platform works and how to get going. The main benefit I've seen that I haven't seen in some of the other tools is the ability to easily change which environment you're working with, such as swapping between dev, test, prod, or GitPrime and executing a flow in that location quite easily.

The main thing to consider before using Matillion is the specific reason you want to use a tool like it. If you need a combination of data ingestion and transformation, then Matillion can be a great option. 

Additionally, if you expect to load large volumes of data that will be used across multiple avenues, Matillion is a good choice. On the other hand, if you only need data ingestion and with more manageable volumes or ingesting streams, it might be better to look at a tool like Fivetran. However, they charge you based on the volume of rows or records you ingest. So, if you ingest large volumes, your costs can rapidly increase and overtake your maintenance costs.

Overall, I would rate Matillion ETL an eight out of ten. 

While loading data into Snowflake, I encountered an issue with the key due to the file's large size and a record count in the billions. Loading the data with a Python script was taking a long time, so I decided to explore other options. This is when I discovered Matillion ETL, which I had not heard of before. I learned more about it and used some of its features, including the Material Data Loader, to load the data into Snowflake. Using Matillion ETL, I was able to load around 770 million records in just five to ten minutes. This was a successful use case, and I have also used Matillion ETL for loading data from Amazon S3 to Snowflake and for other data-loading tasks, including connectivity to on-premise servers and different cloud platforms.

I have used on-premise and cloud deployments of this solution.

The most valuable feature of Matillion ETL is the ETL. The solution is open-source which provides advantages, such as good performance and high efficiency. Additionally, it supports three data types which eliminates predefining the data, and we can write script models in Python.

When using the SQL loader type there were not a lot of pre-processing features for the data. For example, if there is a table with twenty columns, but we only want to load ten columns. In that case, we can use a security script to select the specific columns needed. However, if we want to perform extensive pre-processing of the data, I faced some challenges with Matillion ETL. I did not encounter many challenges, but my overall experience is limited as I only have three years of experience.

The solution could improve by adding support for instructed data types.

I have been using Matillion ETL for approximately two years.

Matillion ETL is a stable solution.

We have a large number of people using the solution for our projects.

The scalability of Matillion ETL is good.

I have not used the support from Matillion ETL. My team was able to solve our problems.

Setting up Matillion ETL was straightforward. When connecting to Amazon AWS, all I had to do was pass the connection string, and it would connect easily. The same was true for on-premise servers. Therefore, from a connection standpoint, it was simple to set up.

The time it takes for the implementation depends on the size of the record data. If the data is large then the process will take longer. However, the time it takes is only a few minutes.

The price of Matillion ETL is reasonable.

The decision to use Matillion ETL depends on the specific requirements. If the requirements can be met without Matillion ETL in a short amount of time, then using it would be unnecessary. However, if dealing with large data sets and frequent data migrations to and from the cloud, then Matillion ETL would be a suitable choice.

I have a lot of experience in this field of data and I was able to achieve results with Matillion ETL that I was not able to with the traditional approach. The solution is helpful for large amounts of data.

I rate Matillion ETL an eight out of ten.

I am not specifically into ETL or data pipelines only since I am basically a data architect who is more into designing cloud-based data solutions. My focus is more on understanding the user's use cases and then designing a solution.

When it comes to Snowflake and Matillion ETL, both offer a lot of compatibility and ease of use. The improvement area could be possible if the tool provides better integration capabilities with other ecosystems, including governance tools or data cataloging tools, as it is currently an area where the solution is lacking.

I have been using Matillion ETL for fifteen years.

Matillion ETL runs the codes in Snowflake, which itself is a highly scalable product, and ultimately provides greater scalability due to the combination of both the products.

I did not contact the solution's technical support team because, most of the time, our company could resolve the issues related to the product with the information available online.

I am not able to recall the products that I have used in the past and be able to make a comparison between those tools against Matillion ETL. I have used Fivetran and DBT tools at some point in time, but I am not able to record when I use them. I know that despite using some other products in the past, my company ended up choosing and recommending Matillion ETL since its features did fit into the requirements of my organization.

The product's initial setup phase was easy. Matillion ETL is readily available from Snowflake's marketplace.

Matillion ETL and DBT help our company by making it easy for us to build data pipelines, particularly when Snowflake was used as a data platform.

Matillion ETL helps my company automate the data pipeline and allows us to optimize data workflows.

In the context of the cloud-based data platforms, the good aspect of Matillion ETL stems from the fact that it actually runs code in the platform, which is basically like a push-down approach wherein, though our company builds the pipeline using the tool, the actual execution of those pipelines happens within Snowflake.

The product supports our company's growing data needs.

The product is integrated with Snowflake. Through a straightforward process, Matillion ETL can be degraded with Snowflake.

The integration capabilities of the tool improved our company's overall data analytics pipeline.

I am satisfied with the product interface.

The product has had an impact on my company's team productivity. It is an easy-to-use tool with a simple UI and serves as a no-code ETL solution.

I recommend the tool to those who specifically use Snowflake in their companies.

I rate the tool an eight out of ten.

We use the solution for the ETL pipelines.

I believe the biggest advantage of using Matillion ETL is its speed of development. We don't have to deal with all the details; we can operate on the component level and do a lot with minimal effort. This is the biggest advantage; for example, we can look for particular components, configure them with some metadata, and simply run it. This simplicity and speed of development make it easy to do things quickly. Additionally, the graphical interface makes it easier to visualize something and find the details we are looking for, rather than going through a number of SQL codes and trying to find the issue. Therefore, the speed of development is the biggest advantage of Matillion.

The positive aspect of this solution is that it provides a graphical interface for jobs. Another advantage of Matillion is its extensibility; if something is missing, it can be easily adjusted or custom components can be written. What I really appreciate about Matillion is that it allows us to schedule jobs, so we can track and monitor their execution on a daily basis. I also like how the solution is organized in Matillion; everything is clear and visible, and we can easily access any details we are interested in. Additionally, there is a useful feature for reporting errors, so we don't have to worry about error handling within the jobs themselves. This feature is very convenient as it saves a lot of time and effort.

Matillion ETL has great Git integration that is perfect and convenient to use.

Unlike Snowflake which automatically takes care of upgrading to the latest version and includes additional features, with Matillion ETL we need to do this ourselves. Matillion upgrades the tool quite often, but we need to manually apply it in our environment. This manual process can be done in a few minutes, but it has room for improvement.

Recently, I needed to develop a component that runs queries on Athena, one of the AWS services. Matillion ETL does not have this functionality out of the box, so having an additional component to handle this would be quite convenient. The tool is quite flexible, and there is no source that cannot be easily integrated. The developers are constantly adding new functionality from release to release, responding to market needs. The only thing I was missing at some point was a component for Athena queries.

I have been using the solution for five years.

The solution has been available for a few years now. Initially, there were some issues, but the support was excellent. If something wasn't working, we could quickly get help to resolve the issue. After a few years of using the solution, it has become very stable. We don't have any problems with Marillion ETL; I haven't experienced any surprises. Matillion ETL is very reliable; whatever we develop works.

The solution is scalable due to its cloud environment. This is the beauty of the cloud; if we require a machine with more power, CPU, and memory, we can do it on the fly. We can simply go to the configuration and change the underlying machine, which requires a quick reboot. The new instance is then set up. This is more of a cloud-related feature than a Matillion ETL feature, but it is very easy to scale. If more power is needed, it can be done quickly and easily. It is also important to note that Matillion is usually connected to a database engine, such as Snowflake, AWS Redshift, Azure Synapse, or Databricks. Most of the processing happens on the database side. However, if there is external work such as loading data from S3 or moving data, there is some load on Matillion ETL. But the majority of the work is done on the database side because it is an ELT-like tool. The data is loaded onto the database and then the transformation happens in most cases. It is up to us how we develop it, but usually, the majority of the power is consumed on the database side.

I have had the opportunity to collaborate with the technical support team a few times and have found them to be extremely helpful. They are very responsive, knowledgeable, and adept at understanding the intricacies of our issue. In my experience, the cooperation has been perfect.

Positive

At the time I joined the company I now work for, two years ago, Matillion ETL had already been introduced. We wanted to switch to something nicer and decided to move to the cloud with AWS Redshift. We also wanted to use a graphical interface for development, rather than lots of code snippets, as it would be faster and easier to maintain. With the graphical approach, it is much easier to spot and go to the root of any issues.

The deployment time depends on the configuration, but with the cloud, everything goes smoothly and we can complete it within two hours.

I believe the cost is customer dependent, so depending on the instance we have and what we can have on-premise, we can have an installation in the cloud, or we can use the hub option. This is what Matillion ETL provides directly. A rough estimation of the cost is around 20,000 dollars a month, however, this is dependent on the machine used and how Matillion ETL is used. If there is only an hour or two of processing per day, it is better to use the hub approach as we only pay for the hours used. If there is ongoing processing, then an installation in the cloud is usually the better option. Ultimately, the cost is dependent on the individual case.

I give the solution a nine out of ten. This is one of the best solutions. Matillion ETL is closely integrated with the cloud environment, which is quite common, and thanks to that, we can take advantage of services available in AWS, GCP, and Azure. I have used a few solutions so far and this is one of the best. Everything works as expected, the tool is very intuitive, the monitoring is very well-developed, and the Git integration is great. From a developer or architect's point of view, it is quite intuitive and nice to use. Matillion ETL is one of the best.

We currently have 15 developers using the solution in our organization.

Depending on the specific case and requirements; when deciding, I would take into consideration what other options are available. From an end-user perspective, I really like Matillion ETL; it is comfortable to work with and easy to set up and maintain. The solution is not perfect, but there are no other similar solutions. The support is also very good and the integration with Git is quite nice, so it is quite flexible. Even if something is not supported out of the box, we can customize components or include Python code. It also depends on the amount of data to be processed, what kind of data it is, and the underlying database engine. There are cases where Matillion ETL makes sense, but there may be cases where it is not recommended. The solution is quite flexible, with the Snowflake dedicated version or Databricks, so we can decide which underlying data warehouse to use. If the company is GCP-related, they may not want to use Redshift, but there is an option for Bitquery. If developers prefer to use Spark, the Databricks version of Matillion ETL would be a nice option. I have been using the solution for a while and I feel comfortable with it. Compared to other tools like DataStage and Informatica, I can say a lot of good things about Matillion ETL, so it is quite convenient for us.

We have some very unique use cases for the solution. We are interfacing between the on-premises database and on-cloud database– Oracle and Snowflake. It is a very complex process wherein we had to ask for help from Matillion’s engineers to build it out. We're looking up our on-premises database servers for information to build our cloud database. Once we get everything copied in Snowflake, we go back to Oracle on-premises. We have created this bridge to use till we switch to AWS full-time. But right now, we don't have that in the book. So the best way to move forward is to make sure we're taking a solution that could bridge that gap, and that's Matillion.

Matillion’s ability to bridge the gap between on-premises and on-cloud helps us prepare for the future while building our platform. If you plan to switch to AWS from on-premises, using Matillion allows you to keep everything in sync as you port the data. They also have a CRM sync function that allows you to make changes to the on-premises database and automatically sync it up with the material inside the cloud system.

We have a lot of user-created excel spreadsheets. Matillion works very dynamically when it comes to fetching out data changes. It uses the metadata in the Excel document to map everything to your Snowflake which no other ETL can do. It cuts down the redundancy of remapping everything whenever somebody makes a change, especially while dealing with client-created files.

It's been able to do everything we require. It could fetch Excel files, SLS files, etc. It's pretty dynamic. I would say I am happy with their features.

Matillion’s on-premises capabilities don’t allow you to build something customized. I will give an example of tables to explain it. If we want to do a lookup, we have to copy the whole table with three million rows in it, every time. It is not cost-effective for me. We have these three million rows ported over in our S3 bucket. We have to pay for that and Snowflake as well. So, we have told them to build up a custom solution allowing us to bring over the data we care about, using the records and the drivers. But, that wasn’t out-of-the-box. So, the default way to work is to port everything over from on-premises to your AWS environment. For me, it is not cost-effective in the long run.

We have been using the solution for about a year along with training sessions.

We are yet to have the solution run in production, but it has been very stable in our testing environment. I would say it is extremely stable.

I would rate its scalability ten out of ten. It is the reason why we work with Palo Alto. Currently, we have five of us using the solution. We eventually plan to increase the usage.

We have used Informatica, SSIS, and Alteryx, previously. The primary reason to switch was the scalability. We thought Matillion would bridge the gap between on-premises and on-cloud as we were moving there.

The initial setup was more complex as my team had tons of overhead due to security concerns.

Our in-house team did the implementation. We primarily had one person working on it. You can count overheads for security, management approvals, and everything it requires, but the actual work is done by only one person.

It's not the cheapest. But, in case you are a large-scale organization, it is not going to break the bank for sure.

Before switching to cloud databases, you need to understand the cost of Snowflake and AWS. You need to understand the costs of porting the data over and replicating it. If you are going for Snowflake, I would 100% advise you to go for it. It helps you get where you need to be. I would rate the product ten out of ten.

We are using Matillion ETL for extracting and integrating the data from different applications, such as SQL, and other data sources.

The most valuable feature of Matillion ETL is its ease of use. If you have had some experience with other solutions, such as Snowflake, the use of this solution will be simple.

The cost of the solution is high and could be reduced.

I have been using Matillion ETL for approximately six months.

Matillion ETL is a highly stable solution. We are using a stable version of the solution.

I rate the stability of Matillion ETL a nine out of ten.

The solution has limited scalability. However, for concurrent tasks, the solution has been scalable enough for our needs.

I rate the scalability of Matillion ETL an eight out of ten.

The support from the vendor could be better. The speed of the support could improve.

I rate the support of Matillion ETL a six out of ten.

Neutral

I have used Snowflake. However, I am beginning the using these types of tools.

The implementation of Matillion ETL is straightforward. The length of time it takes for the deployment of the solution typically can be completed in a few hours.

We did the deployment of the solution with some assistance.

The price of Matillion ETL is expensive.

For those who want a cloud-based analytics platform, I would recommend this solution.

I rate Matillion ETL an eight out of ten.

It is cloud native and designed to run on cloud warehouses. There is compatibility with many of the cloud data warehouses, as well as Snowflake, and any SQL data warehouse. It is not compatible with other ETL products.

The interface is very intuitive.

I am looking forward to seeing the expansion of the source range for their data loader product. However, I think they have done a very good job of incorporating a lot of different data sources.

I have been working with Matillion ETL for four years now.

Matillion ETL is one hundred percent stable.

I have found that Matillion ETL is infinitely scalable.

Technical support is excellent.

The initial setup was very easy.

I think it is cost conscious. It used to be very cheap and they have more recently bumped up the pricing, so it is competitive now. I would not call it cheap anymore, but it is certainly competitive.

I would rate Matillion ETL a nine out of ten it is a very good product.

I am using Matillion ETL for Snowflake. We are doing a migration project from SAP BODS  
to Matillion for Snowflake. We are migrating all the BODS data flows, workflows, to Matillion jobs.

The customer wanted to move ahead from the Oracle database and SAP BODS. They wanted to move into the cloud for their various data integration and as part of their digitalization. The customer wanted to move to a cloud-based solution. They preferred Matillion ETL for Snowflake.

The loading of data is the most valuable feature of Matillion ETL.

There are certain functions that are available in other ETL tools which are still not present in Matillion ETL. It would be good to have more features.

I have used Matillion ETL within the past 12 months.

The performance of Matillion ETL is good.

The solution is scalable.

Companies can receive a return on investment by using it.

I would recommend Matillion ETL for any cloud-based operations. 

I rate Matillion ETL an eight out of ten.