Matillion Data Productivity Cloud Reviews

For the ETL, we are using Matillion Data Productivity Cloud.

We have skilled resources for Matillion Data Productivity Cloud, which is why we are using it. The infrastructure is provided by the customers, and we implement based on their requirements. When customers have a Matillion Data Productivity Cloud license and ask us to use it, we implement it accordingly.

They are helpful for our data integration.

The predefined connectors eliminate the need to write code for connectivity. If you have a predefined connector, it is easy to use with plug and play functionality.

The processing time and ease of use are significant benefits.

As everyone is moving into AI integration, it will definitely help.

When creating workflows, they can propose solutions directly.

The main areas for improvement are AI features and scalability.

We have been using Matillion Data Productivity Cloud for the last five to six years.

The deployment process is easy.

Stability-wise, it is performing well.

Scalability in Matillion Data Productivity Cloud has some limitations. Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required.

It could definitely be more scalable.

The support is good.

Positive

Previously we worked with Microsoft SSIS. SSIS is an older technology, while Matillion Data Productivity Cloud is more advanced. Each solution has their own advantages and disadvantages.

We have experienced both time savings and money savings.

The pricing is managed by the tooling team. The pricing is moderate, neither expensive nor cheap.

We are working with JMeter but not with other Apache products.

We cannot use open-source solutions due to compliance issues.

We work with AWS, primarily using Redshift and Reltio.

We use both Azure and AWS public cloud services.

I would rate Matillion Data Productivity Cloud a seven out of ten.

Overall, it has been a good experience.

The overall review rating is 9 out of 10.

I primarily use Matillion Data Productivity Cloud for ingest functions. In one client's case, we switched from typical AWS tools and changed all of the blueprints and architecture to use Matillion Data Productivity Cloud for ingesting information from GCP and reading SQL data.

Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations. Its integration with existing environments, utilizing security measures like VPN and VPCs, makes it a secure choice. However, its features are not notably different from other products in the market.

There are problems with GCP connectivity. Specifically, connections to BigQuery for extracting information are complex, and the optimization of the extraction process requires improvements. I raised this issue with Matillion's support, but the solution is pending with their research team.

I have been working with Matillion Data Productivity Cloud for two years.

Matillion Data Productivity Cloud is stable and functions reliably as a product.

The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity. However, the limitation is the number of parallel jobs due to the direct relation to the cores on the machine.

Matillion's technical support is excellent. They communicate effectively and respond quickly to all inquiries.

The initial setup is straightforward. We completed the foundations process using Infrastructure as Code (IaC), which standardizes and simplifies the deployment process.

In terms of ROI, one client found that using Snowflake tasks was more cost-effective than Matillion's credits cost. Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.

While pricing can be an issue compared to other solutions, Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.

In many cases, clients consider buying a product from the same hyperscaler for an end-to-end solution, such as Microsoft Fabric.

While Matillion Data Productivity Cloud can be valuable, it is challenging to recommend due to its pricing and limited clientele in regions like Spain. Overall, I would rate Matillion as a six out of ten.

I use Matillion ETL for wrangling or transforming data from sources like S3 and Databricks.

The most valuable feature of Matillion ETL is the UI experience in which you can drag and drop most of the transformation.

Sometimes, we have issues with the solution's stability and need to restart it for three weeks or more. There is some room for improvement in job clusters for Databricks.

We have been using Matillion ETL for three years.

Sometimes, we have issues with the solution's stability and need to restart it for three weeks or more.

I rate Matillion ETL an eight out of ten for stability.

Around 100 users are using the solution in our organization. There is a possibility of creating multiple nodes for the solution. However, you need to manage the separate nodes and upgrade the Linux, which is difficult.

I rate the solution an eight out of ten for scalability.

The solution's technical support is great. They respond within a few hours and close the case as soon as possible.

Positive

I previously used Informatica PowerCenter and Informatica Cloud. We switched to Matillion ETL because it was cheaper.

Matillion ETL has a pay-as-you-go pricing model of a few dollars per hour of runtime.

I am using the latest version of the solution. The solution's connectors saved us the most time during data transformations.

Overall, I rate the solution a nine out of ten.

We use it primarily for transferring data into our cloud data warehouse and conducting research. We rely on Matillion ETL for our data integration and transformation needs, finding its user-friendly interface and robust capabilities highly effective. Essentially, we've built a cloud-based data platform using Matillion ETL to seamlessly extract data from various sources, perform necessary transformations, and store it in our cloud environment.

In our finance-focused projects, we predominantly utilize Matillion for data warehousing tasks, particularly in the realms of finance and micro-lending. While our projects may not involve big data volumes, they often entail handling intricate datasets that require sophisticated processing.

The cloud-native design of Matillion has significantly impacted our video data workflows, providing a unified view across multiple data sources and serving as a centralized and secure platform. It offers a seamless experience, consolidating various functionalities into one interface, which simplifies our tasks considerably. Moving forward, we're eager to explore their SaaS offering, particularly for Redshift, once it becomes generally available. This scalable solution eliminates concerns about version upgrades, maintenance, and patching, offering a hassle-free experience.

Its versatility is impressive, as it allows us to seamlessly transition between different cloud platforms without being locked into a specific provider.

It is an incredibly user-friendly and intuitive tool, making the learning curve quite smooth. Its simplicity is such that one only needs a basic understanding of ETL processes to become proficient. With a plethora of connectors available, it seamlessly integrates with various data sources. Moreover, its native integration with AWS enhances its efficiency, utilizing lightweight instances and pushing processing tasks to registered instances, resulting in swift performance.

There's room for improvement in how it handles data streaming capabilities. Our main challenge currently is that Matillion runs on an EC2 instance, limiting us to running only two processes simultaneously at the entry level. This constraint means we can run about sixteen jobs concurrently at the moment. However, once we transition to the SaaS offering, scalability will no longer be an issue. With the SaaS solution, we'll have the flexibility to run as many jobs as needed, making it a natural next step in our progression.

I have been working with it for six years.

It exhibits a high level of stability and robustness. I would rate it nine out of ten.

In our small business unit, we currently have around four users, with two of them utilizing Matillion within our organization. Considering our growing needs, we're contemplating transitioning to an enterprise SaaS solution where we would share the same instance. Currently, each user is billed individually, but consolidating to a shared instance seems more efficient. Scalability is excellent when using the SaaS solution, easily reaching a rating of ten out of ten. Each data pipeline request is encapsulated within a Docker container and spun off, allowing for instant scalability. Overall, I would rate it a nine out of ten in terms of performance and scalability.

The technical support is excellent, and consistently helpful whenever we need assistance. We frequently engage with them, and the documentation is top-notch. Additionally, there's ample support available on YouTube covering various features. We heavily rely on their documentation site for guidance and reference. I would rate it eight out of ten.

Positive

Previously, we relied on an on-premise solution, which often posed performance and capacity challenges due to server limitations. However, this approach involved upfront purchases and heavy licensing fees, which was a different model altogether. What I appreciate about Matillion is that it's available on the AWS Marketplace, allowing for easy installation and billing directly through our Amazon bill. This integration streamlines the process, as there's no need to manage separate vendor payments for ETL services.

It seamlessly integrates with CodeConnect, which is the AWS Git repository, but it also supports integration with any Git repository, whether it's GitHub, GitLab, or another platform. Regardless of your choice of change control tool, Matillion can accommodate it. It simplifies the process by converting JSON payloads into a usable format, allowing for easy promotion from one environment to another. There's no maintenance required from our end; it's all handled automatically by the vendor. We receive weekly patches and periodic version updates, ensuring the solution stays up-to-date without any intervention from our side.

We pay $5.40 per EC2 running hour, and we can reduce costs by stopping and starting the EC2 instances strategically. For instance, in our production environment, we run it for sixteen hours a day, while in our data environment, it's around ten to eleven hours a day, Monday to Friday. This approach allows us to save significantly on costs since we're billed per EC2 running hour. The absence of licensing commitments makes it easy to experiment with the tool, and if we decide it's not suitable, we can simply stop the ETL instance and cease incurring charges.

Overall, I would rate it nine out of ten.

The solution is used for data ingestion from multiple sources to Snowflake and extract and load.

The solution's most valuable feature is the CDC (Change Data Capture) component.

Depending on the use case, the solution's pricing could be improved. Matillion ETL should include more enhanced capabilities for extracting data from the SAP systems.

I have been using Matillion ETL for more than one year.

Matillion ETL is a highly stable solution.

I rate the solution’s stability a nine out of ten.

Matillion ETL is a highly scalable solution because it is hosted on a cloud provider. So, that part is automatically taken care of. More than 100 users are using the solution in our organization.

I rate the solution’s scalability a nine out of ten.

The solution's technical support team is highly responsive.

Positive

Matillion ETL is a self-managed solution. Matillion deploys the tool. It's a SaaS offering that requires no extra deployment effort from our side. The solution can be deployed in a couple of minutes. Matillion's support team spun up the environment for us.

Since the solution is on the cloud and self-managed, your operational costs and admin efforts will decrease. The solution has saved around 15% to 20% of our operational costs.

The solution's pricing is not based on the licensing cost but on the running hours when the Matillion instance is up and running. Its pricing model is different from the traditional pricing models of Talend or Informatica. The solution was not expensive for my use case.

I rate the solution’s pricing an eight out of ten.

The solution's orchestration capabilities are pretty good. It does have an integration with DBT. I would recommend the solution to other users because it has a very clear roadmap with a good support system. The solution’s maintenance is done through an in-house team of three to four engineers.

Overall, I rate the solution an eight out of ten.

Matillion EDR is used for data loading. It extracts data from various sources, stages it in a data warehouse environment, and then performs orchestration and transformation jobs to automate processes across different layers of the data warehouse.

It serves as a development environment to build data pipelines. This is part of the entire data integration process, encompassing extract, transform, load (ETL) actions. It's used to run transformations on ETL data, preparing it for consumption.

We had a customer with on-premise systems. We extracted data from these systems and staged it in S3 or Azure Blob Storage. Then, Matillion picked it up for processing. The key advantage here was the speed of development.

The new version with the Productivity Cloud is very simple. It's easy to use, navigate, and understand.

It also offers automated scalability in terms of handling large data volumes. It scales up automatically in the background, so you don't have to worry about infrastructure to do that.

One of the features that's in development is data privacy in the cloud, along with further SAP integration.

For connectivity to SAP systems.

We have been using it for two years. We work with the latest version. 

I would rate the stability a nine out of ten. It is a very stable solution. 

It scales automatically in the background. Obviously, we don't need to take care of any infrastructure for scaling. It scales based on the volume and processing required. 

You can tweak it if you want, but it adjusts the scales as needed. So, for smaller workloads, there's less consumption, but for large workloads, it scales to run within a specific SLA.

In South Africa, we've got six large enterprise customers. I would rate the scalability a ten out of ten. 

The customer service and support are very responsive. 

Positive

Our experience is mainly around using Snowflake Data Cloud with Matillion. And the two of them combined, offer superior performance and price point benefits. 

Using them together is very efficient because Matillion's processing and Snowflake's own scalability and consumption based on pushing down code work well together. They are very efficient.

We've used Azure Data Factory (ADF) for integration. So that's an alternative, depending on customer choice for the integration. And then on AWS, a few of the other services, like Terraform, and S3 storage, and that's about it.

It's on a case-by-case basis. If they've chosen ADF as a technology, then we implement using that choice.

There is nothing complex in the process. There is a tenant set up by Matillion in a few minutes, and then you can start working.

We are system integrators. We handle the implementations for our customers. 

We have around 25 engineers on our team. 

The pricing depends on what edition the customer opts for.  For example, a standard edition and then business critical of different editions. Each of those has a different cost per unit, which is Italian cost. It is like a utility model model. For example, the standard edition is priced at $2.00 per credit. And you are only charged when you use it. You're not charged when it's idle.

So, the customer only pays for the runtime, not for idle time.

So, the unit cost includes everything, even any additional costs.

Overall, I would rate the solution a nine out of ten. 

We use the solution to make data transfers between our source systems and Snowflake. It's our data analytics architecture.

The product has a good user interface. I don’t have to write too many codes. I can design or create general data transfer and technical jobs through simple texts. If I want to repeat a job, I can do it without creating multiple pipelines. I can create multiple jobs with a single pipeline. It is a fantastic feature of the product.

The product must enhance its near-real-time data capture feature. Also, if they enhance CDC functionality, we can replace multiple components with one component. Otherwise, we must purchase a different component for CDC functionality and another component for data transformation. This added feature will help the user do everything with a single component.

I have been using the solution for six months.

The tool is stable.

Six technical developers use the solution in our organization.

We did not contact support for technical issues. We contacted them for administration, deployment, and purchasing. Support was excellent.

Positive

The initial setup was easy. The solution is deployed on the cloud on a virtual machine.

The tool is neither cheap nor expensive. It is moderately expensive. The product must improve its pricing.

Glue is much cheaper than Matillion.

If the product enhances the CDC functionality, I would rate it a ten out of ten. I would recommend people to use the solution. Overall, I rate the solution a nine out of ten.

My primary use case involves handling standard ETL tasks. I work on processing both our company's data and third-party data sources. While I focus on these standard ETL tasks, my colleagues excel in more advanced pipeline work.

The most valuable feature of Matillion ETL is its user-friendly graphical interface.

As someone new to the data industry and with limited experience in ETL tools, I'm not familiar with other options. My background was as a university professor until about a year ago, so I'm still getting acquainted with this field. I found some of the more complex aspects of ETL challenging, but I grasped the concepts fairly quickly.

I have been using Matillion ETL for one year.

I would rate the stability a nine out of ten. It is quite stable.

The scalability of the solution is excellent. I would give it a nine out of ten. 
Matillion is our main ETL tool, and it is the one our consultants recommend. Currently, about 30 people at our company use it exclusively for all our ETL tasks.

My advice to new users would be to start by going through the tutorials and working with example data. These tutorials are quite helpful in getting you familiar with the tool. Additionally, try working with data that you understand well, such as data from your previous work or a familiar dataset. This way, you can focus on learning how to use the tool without having to figure out complex data problems simultaneously. Overall, I would rate Matillion ETL a nine out of ten. It works very well.

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.

Positive

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.