Maia by Matillion is an autonomous data engineering platform designed to automate the lifecycle of data pipelines through AI-driven orchestration. The solution functions as an enterprise "digital workforce" that translates natural language requirements into production-ready DataPipelines, leveraging a Pushdown Architecture to execute transformations natively within cloud data warehouses.
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SSIS Data Flow Components
Score 8.1 out of 10
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Devart SSIS Data Flow Components are designed to simplify the ETL process within SQL Server Integration Services (SSIS) packages, allowing users to connect cloud applications and databases through their SSIS workflows without the need to write complex code. Export data from various sources to different file formats Import XML, CSV, and other files to cloud applications and databases Synchronize cloud applications and databases Migrate from one…
$549.95
one-time fee
Pricing
Maia by Matillion
SSIS Data Flow Components
Editions & Modules
No answers on this topic
SSIS Integration Database Bundle
$549.95
one-time fee
SSIS Integration Cloud Bundle
$749.95
one-time fee
SSIS Integration Universal Bundle
$999.95
one-time fee
Offerings
Pricing Offerings
Maia by Matillion
SSIS Data Flow Components
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
Purchase includes a perpetual product license and a 1 year of subscription which includes the product updates and premium support.
SQL Server Integration Services (SSIS) is built around the Microsoft ecosystem; we needed something that was either "ecosystem-agnostic" or focused on AWS, which Matillion is. SSIS has very limited ability to parameterize jobs/packages compared to Matillion, reducing the …
Overall from an ease of use and set up standpoint, it stacks up better than most of the other tools I've used. However, Matillion doesn't quite expand and scale up as well as the other solutions.
Matillion ease of use and feature set along with integration capabilities and built in integrations are definitely a step ahead than the other tools and it provides the ability for us ti integrate with refshift, Snowflake and on premise data sources and hence becomes a …
Matillion is much easier to use than the other tools that I've worked with in the past and has a more complete forward-thinking cloud strategy. Legacy ETL tools struggle to make use of modern cloud data warehouse patterns.
Being a fairly new player in the market, Matillion is coming up against some well-established names such as Pentaho and Talend. The primary benefit for our use case was the fact that Matillion is built specifically for Amazon Redshift. While Pentaho and Talend are more mature …
Great: Need to query simpler APIs, or utilize well known services such as GSheets etc.? Matillion has got some of the best and easiest to use connectors out there. Not so great: Do you need have a competent CI/CD flow that you will be able to update / compare from Matillion as well as other sources at the same time? Good luck, you will need to be extra careful, as you might have to have a deeper dive into your servers Terminal each time you have a git conflict.
Matillion is brilliant at importing data -- it would be amazing to have more ways to export data, from emailed exports to API pushes.
Any Python that takes more than a few lines of code requires an external server to run it. It would be great to have more integration (perhaps in a connected virtual environment) to easily integrate customized code.
Troubleshooting server logs requires quite a bit of technical expertise. More human readable detailed error handling would be greatly appreciated.
With the current experience of Matillion, we are likely to renew with the current feature option but will also look for improvement in various areas including scalability and dependability. 1. Connectors: It offers various connectors option but isn't full proof which we will be looking forward as we grow. 2. Scalability: As usage increase, we want Matillion system to be more stable.
We are able to bring on new resources and teach them how to use Matillion without having to invest a significant amount of time. We prefer looking for resources with any type of ETL skill-set and feel that they can learn Matillion without problem. In addition, the prebuilt objects cover more than 95% of our use cases and we do not have to build much from scratch.
Overall, I've found Matillion to be responsive and considerate. I feel like they value us as a customer even when I know they have customers who spend more on the product than we do. That speaks to a motive higher than money. They want to make a good product and a good experience for their customers. If I have any complaint, it's that support sometimes feels community-oriented. It isn't always immediately clear to me that my support requests are going to a support engineer and not to the community at large. Usually, though, after a bit of conversation, it's clear that Matillion is watching and responding. And responses are generally quick in coming.
Fivetran offers a managed service and pre-configured schemas/models for data loading, which means much less administrative work for initial setup and ongoing maintenance. But it comes at a much higher price tag. So, knowing where your sweet spot is in the build vs. buy spectrum is essential to deciding which tool fits better. For the transformation part, dbt is purely (SQL-) code-based. So, it is mainly whether your developers prefer a GUI or code-based approach.
We're using Matillion on EC2 instances, and we have about 20 projects for our clients in the same instance. Sometimes, we're struggling to manage schedules for all projects because thread management is not visible, and we can't see the process at the instance level.