In a project where we migrated from on-prem solutions to the cloud, we used Matillion and Snowflake, which streamlined the process significantly.
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Helps us monitor real-time data, but the scalability needs improvement
How has it helped my organization?
What is most valuable?
Matillion ETL's ability to conduct end-to-end data migration is valuable. We can create jobs, monitor them, and manage workflows effectively.
What needs improvement?
The product's scalability needs improvement. Perhaps adding more connectors would be beneficial.
What do I think about the stability of the solution?
The product is quite stable and can handle complex data integration tasks well.
What do I think about the scalability of the solution?
I rate the platform scalability around six or seven. Depending on the specific architecture, parallel processing needs, and data types involved, it could be optimized.
How are customer service and support?
I haven't contacted the technical support team, but the online documentation and community resources are quite sufficient.
How was the initial setup?
A team of two to three ETL architects and data engineers is required to work on deployment.
What other advice do I have?
We can monitor and manage real-time data pipelines, analyze task logs, and automate data pipelines wherever possible. We also apply parameterization to improve efficiency using the product.
It adapts well to changing data volumes and types. I would recommend it, especially for cloud data integration.
I rate it a seven or eight.
Improves data analytics pipeline and is easy to install
What is our primary use case?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Matillion ETL for fifteen years.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
The product's initial setup phase was easy. Matillion ETL is readily available from Snowflake's marketplace.
What other advice do I have?
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.
From simple to scaled up jobs
What do you like best about the product?
It is very easy to get started with the concepts used in the application is simple and easy to understand. But, under the hood it is very powerful and a workhorse that keep running the jobs day after day.
What do you dislike about the product?
That it is not fully cloud, i.e. it requires some maintenance and manual updating.
What problems is the product solving and how is that benefiting you?
Being the core tool for our data pipelines for our core data (fact and dimension tables) in our analytics products.
Professional ELT Data Integration Tool
What do you like best about the product?
Rich components for developing Data Integration ETL job. Easy and fast implementation.
What do you dislike about the product?
We have experienced several ELT job performance issues that caused Matillion server no response or ETL jobs hanging issues due to Jython Interpreter issue
What problems is the product solving and how is that benefiting you?
Matillion support provided us the solution for resolving Matillion server performance issue, and Matillion new version upgrade support.
Matillion: Empowering Data Wizards with Drag-and-Drop Magic
What do you like best about the product?
Matillion’s intuitive flow visualization makes orchestrating complex data pipelines feel like painting with digital brushes. The drag-and-drop interface transforms intricate ETL processes into a delightful canvas where connections flow seamlessly.
Matillion’s connectors are like well-fitted puzzle pieces. They effortlessly link disparate data sources, allowing you to focus on insights rather than plumbing. Whether it’s cloud services, databases, or APIs, Matillion’s connectors are the bridge to your data dreams.
In a nutshell, Matillion combines creativity and convenience, turning data wrangling into a captivating symphony. 🎨🔗✨
Matillion’s connectors are like well-fitted puzzle pieces. They effortlessly link disparate data sources, allowing you to focus on insights rather than plumbing. Whether it’s cloud services, databases, or APIs, Matillion’s connectors are the bridge to your data dreams.
In a nutshell, Matillion combines creativity and convenience, turning data wrangling into a captivating symphony. 🎨🔗✨
What do you dislike about the product?
Implementing code versioning within Matillion can be a bit like untangling a digital yarn ball. The lack of straightforward integration with popular version control systems can leave users yearning for a simpler path to track changes and collaborate seamlessly.
What problems is the product solving and how is that benefiting you?
ingestion and transformation of raw brons data towards the gold layer.
Matillion ETL - Cloud ETL Tool integrated with Snowflake
What do you like best about the product?
Very Intuitive
Ease of Integration
Ease of Implementation
Ease of Integration
Ease of Implementation
What do you dislike about the product?
Missing features for GIT Integration
Some bugs when coding related to character sets
Some bugs when coding related to character sets
What problems is the product solving and how is that benefiting you?
ETL for Enterprise Data Warehouse
ETL Tool for Operational business processes
ETL Tool for Operational business processes
Offers good user interface and easy to navigate
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
One of the features that's in development is data privacy in the cloud, along with further SAP integration.
For connectivity to SAP systems.
For how long have I used the solution?
We have been using it for two years. We work with the latest version.
What do I think about the stability of the solution?
I would rate the stability a nine out of ten. It is a very stable solution.
What do I think about the scalability of the 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.
How are customer service and support?
The customer service and support are very responsive.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
We are system integrators. We handle the implementations for our customers.
We have around 25 engineers on our team.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
Overall, I would rate the solution a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Easy to use ELT tool for staging and transforming you data
What do you like best about the product?
Matillion makes it easy to drag and drop component and generate SQL statements to transform your data. There are lots of built in connectors so it is not too difficult to add new sources to your data warehouse.
The new performance monitor is great for checking on jobs across projects and understand how your server is performing.
Consistent updates keep the tool ever improving
The new performance monitor is great for checking on jobs across projects and understand how your server is performing.
Consistent updates keep the tool ever improving
What do you dislike about the product?
The way Matillion works with transformation job it can be easy to end up with very poor performing queries that need to be reworked.
There could be better logging, especially when using transaction control and updates are run sequentially instead of in parallel.
Better API access for pulling job history and average run times.
Git integraton is kind of clunky for larger teams
There could be better logging, especially when using transaction control and updates are run sequentially instead of in parallel.
Better API access for pulling job history and average run times.
Git integraton is kind of clunky for larger teams
What problems is the product solving and how is that benefiting you?
Helping us expand our data warehouse and quickly add new sources
Powerfull ELT cloud native tool
What do you like best about the product?
Matillion enables fast delivery of complex data pipeline. The interface makes it possible to create value without coding at all
What do you dislike about the product?
Matillion ETL VM , as it is pay per use, must be managed to be started and stopped to avoid cost when not needed.
What problems is the product solving and how is that benefiting you?
Matillion can be used to retrieve any data from pretty much any sources (database, files, Rest Apis...) in any format.
Matillion is a great ETL tool for cloud data warehouses
What do you like best about the product?
Capability to connect with all the top data warehouses like bigquery, redshift, etc.
The tool is developer friendly, it also provides the capabilities to use python runtime, http end points etc.
The tool is developer friendly, it also provides the capabilities to use python runtime, http end points etc.
What do you dislike about the product?
Re usability of template is a bit challenging. I tried to create a data quality reusable template, but it requires a lot of efforts to make it right.
What problems is the product solving and how is that benefiting you?
Complete data integration in gcp is done via Matillion. Reading from gcs to writing to big query and applying transformations.
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