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.

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. 

Positive

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.

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.