Matillion ETL Reviews

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. 

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 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.

Bringing data from different sources onto our Snowflake data warehouse.

• It has helped us avoid a lot of red tape due to compliance. 
• It has helped us to get onto the cloud quickly.

• It is pretty easy to learn.
• No scripting is required.
• Being cloud native.

It is not an end-to-end platform for ETL. It brings in the data. To complete the pipeline, they might want to include some connectors which would put the data into different platforms. This would be helpful.

We are working with different platforms. Most of the connectors that we are looking for are included, but sometimes scripting is required. The good thing is we can reach out to support and have them create the custom connector for us. This can probably be improved.

We do not put much stress on it. We run it as a batch right now and spin it up on demand. So, we run it for three hours, then we shut it down. It is not real-time, and there is not a lot of streaming as of now, but it is running in the batch.

The size of our environment is not big. We started six months back. Right now, we only use one node, which is moving the data onto our data warehouse in Snowflake. This node is also very small at this time. 

Eventually, we will grow big and quickly, because we just had our three drugs approved. Therefore, a lot of data is going to come over. We will be moving this over to our data warehouse, which will need to increase significantly.

The technical support treats us well. They already have a support portal, and they are responsive, which helps.

We haven't used any of the on-premise solutions. What we used before was SQL Server Integration Service (SSIS), and when we were moving to the cloud, we wanted to use something that was cloud native (AWS).

The integration and configuration of this product in our AWS environment was excellent.

We are integrating it with SQL Server and the data coming in is from the on-premise file server. That's what we are connect with right now. Eventually, we will be pulling the data from Google Cloud and Twitter.

We are still in the process of moving the data over, so there are no data points as of now.

You probably don't even need to reach out to the company sometimes to purchase it. You can go to the AWS Marketplace. It's very easy to spin up, and the configuration is also easy. It spins up in your own AWS account. The only way you can get the product now is via the AWS Marketplace.

Their pricing is simple. They charge you by the hour. Whatever EC2 instance time that you have been running it for, that's what it's going to charge you for. The licensing is not based off of the per user or per server. They are going by the type of instance you spin up and how long you've been running it, so easy peasy.

The other products that we considered were SSIS and Informatica.

We chose Matillion because of the flexibility of the data and the company does not store data on its platform. As a life sciences company, which is HIPAA compliant, we didn't want to move the data over to the platform, so that's why we selected Matillion. It also has additional components that we did not have to pay for, as those features are included with the data. We can scale horizontally with it.

Give it a shot. See how easy it is to get started with the product, because the scripting which is required is minimal. Anybody who is familiar with the SQL Server platform and with SQL scripting can easily pick it up and run with it,

Overall, it is a really good product.

We use it for archiving and storing data.

"It has made our lives much easier," This is what my teammates who were using the other stuff before have said. Then, they moved it to Snowflake. Now, it is much easier and faster to use than before.

It is cost-effective. Based on our use case, it's efficient and cheap. It saves a lot of money and our upfront costs are less.

• Performance can be improved for efficiency, and it can be made faster. 
• Latency could be reduced. Sometimes, it takes longer to fetch data out of it. There are network issues because we experience a little lag.
• While the UI is good, it could be improved in its efficiency and made easier to use.
• It can be used by different consumers. So, I would recommend to the company to promote more, because people don't know much about it. If they promote more, they can sell it. They need more marketing.

It is pretty stable, but you can't blindly rely on it. On a scale of one to ten, I rate it around an eight.

It can scale to a great extent. It can handle the load that we are putting on it, which is about 5TBs.

The technical support was good at troubleshooting. The issue that was sent to them was resolved in a couple of days, so it was resolved quickly. 

The integration and configuration was pretty easy. It was completed in about two months.

It saves a lot of time.

I have heard from my manager and other higher ups, "This product is cheaper than other things on the market," and they have done the research.

Anybody can help to configure and train on it, learning how to use it. This will help speed up the process.

We use the AWS version only.

It is helping to make Makerbot a data-driven company.

The most valuable features are the components for SFDC, RDS, Marketo, Facebook, and Google AdWords; built-in verification; and scheduling, restarting & logging.

On a Redshift project, before Matillion was released, two people literally spent over one month using Sqoop to pull very wide data tables from Safesforce.com to Redshift. On a new project using Matillion, it took me 10 minutes to set up and begin importing data from Safesforce.com.

Built-in verification and sampling is a fabulous feature for ETL engineers. Anywhere along the transformation-pipeline, one can check, see and sample the data. This saves days & weeks of effort and leads to a far more agile project.

More frequent releases are needed, due to API changes from Google, Marketo, and Facebook. They frequently release upgrades to their API and consequently frequently deprecate the older version when only a few months old. The only way to use the Matillion components for these APIs successfully is for the Matillion release process to step up to the plate and have far more frequent "minor" API releases (as opposed to "major" product releases).

Even having these automated might not be a bad idea. Some customers willing to pay might open up a new revenue stream for "platinum" service, to take the headache out of this very valuable set of marketing components in Matillion.

I only encountered stability issues when accidentally performing EC2-intensive Python jobs (i.e., not Redshift-intensive SQL). These can kill the Matillion EC2 instance.

I have not encountered any scalability issues.

Customer Service:

Customer service is excellent.

Technical Support:

Technical support is above-and-beyond.

SnapLogic and Informatica: too slow; for MPP, they are just glorified and expensive Python schedulers.

Python scripts: high maintenance.

Initial setup was straightforward.

I implemented it myself.

We achieved ROI in <1 year.

The first two weeks are free; pay by the hour for smallest instance for next 2-3 months; after that, take out yearly discounted rate from AWS Marketplace for instance/devs in team.

We also evaluated SnapLogic, Informatica, Talend, and Hadoop.

The mindset of the traditional ETL tools is to off-load transformation to another server/DB. This totally misses the point of MPP and especially of Redshift. Load the data into Redshift early and then transform it inside Redshift ("ELT" not "ETL"). Matillion orchestrates the loading and transformation "pipelines", then gets out of the way whilst Redshift does what it is good at (i.e. the "grunt work").

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.

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.