Azure Data Factory vs. Matillion

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Azure Data Factory
Score 8.3 out of 10
N/A
Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.N/A
Matillion
Score 7.5 out of 10
N/A
Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed and scale that matches the business’s data ambitions. The vendor states enterprises including Cisco,N/A
Pricing
Azure Data FactoryMatillion
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Azure Data FactoryMatillion
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsBilled directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
More Pricing Information
Community Pulse
Azure Data FactoryMatillion
Top Pros
Top Cons
Features
Azure Data FactoryMatillion
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Azure Data Factory
9.1
7 Ratings
10% above category average
Matillion
8.6
122 Ratings
4% above category average
Connect to traditional data sources9.27 Ratings8.7121 Ratings
Connecto to Big Data and NoSQL9.07 Ratings8.581 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Azure Data Factory
8.5
7 Ratings
1% above category average
Matillion
8.4
123 Ratings
0% above category average
Simple transformations9.27 Ratings9.3123 Ratings
Complex transformations7.77 Ratings7.4122 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Azure Data Factory
7.7
7 Ratings
6% below category average
Matillion
8.2
115 Ratings
0% above category average
Data model creation8.45 Ratings9.133 Ratings
Metadata management7.56 Ratings9.140 Ratings
Business rules and workflow7.57 Ratings7.9107 Ratings
Collaboration7.16 Ratings7.3107 Ratings
Testing and debugging7.57 Ratings7.3108 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Azure Data Factory
7.7
7 Ratings
6% below category average
Matillion
8.2
23 Ratings
0% below category average
Integration with data quality tools7.47 Ratings8.222 Ratings
Integration with MDM tools8.07 Ratings8.220 Ratings
Best Alternatives
Azure Data FactoryMatillion
Small Businesses
Skyvia
Skyvia
Score 9.6 out of 10
Skyvia
Skyvia
Score 9.6 out of 10
Medium-sized Companies
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.2 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.2 out of 10
Enterprises
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.2 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Azure Data FactoryMatillion
Likelihood to Recommend
9.3
(7 ratings)
8.9
(124 ratings)
Likelihood to Renew
-
(0 ratings)
8.1
(6 ratings)
Usability
-
(0 ratings)
8.6
(123 ratings)
Support Rating
7.0
(1 ratings)
7.4
(7 ratings)
Implementation Rating
-
(0 ratings)
8.2
(1 ratings)
Product Scalability
-
(0 ratings)
8.7
(116 ratings)
Vendor post-sale
-
(0 ratings)
9.1
(1 ratings)
Vendor pre-sale
-
(0 ratings)
9.1
(1 ratings)
User Testimonials
Azure Data FactoryMatillion
Likelihood to Recommend
Microsoft
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
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Matillion
It is very well suited for ETL on the cloud. Whenever there is something that can be accomplished with no code or little code, Matillion is a good tool. However, if your pipeline requires a lot of customizations, Matillion should be avoided.
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Pros
Microsoft
  • It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
  • We can use linked service in multiple pipeline/data load.
  • It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
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Matillion
  • We leveraged Matillion’s no-code principals to make data manipulation easy for our internal customers. People who don't know how to use SQL no longer need to. Everything in Matillion is self-explained with no or little coding.
  • We connected Matillion to our data warehouse to allow people to read raw data, transform it, then write results back to their sandbox databases. The drag and drop component design allowed customers to create complex data models at the speed of thought without any risk to production data.
  • With sharing capabilities between projects enabled, everyone was able to help each other when questions arose which instilled a strong sense of collaboration and community.
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Cons
Microsoft
  • Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
  • Does not yet have parity with SSIS as far as the transforms available.
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Matillion
  • I think a non-CLI approach to installing and uninstalling python libraries would be nice. I mean, it isn't difficult to install a python library via Linux or CLI, but I imagine most companies don't feel comfortable allowing Matillion users to go on a virtual server and installing it themselves. Requirement.txt file for installing libraries would be simple, and maybe that could also be used to uninstall libraries as well......or maybe the library gets automatically downloaded if it is imported into a python script but the library doesn't exist.
  • Python Component is lacking very much in terms of UI. It would be unrealistic for me to suggest Matillion build its own EDI, but it would be nice if a python component could connect to a local IDE. Right now, if you want to write any decent length python code, you are going to be stuck copying and pasting your code from your local IDE.
  • It is also very difficult to debug in Matillion because you can't set breakpoints. Local IDE integration can resolve that.
  • I would like to have more templates to copy from for certain simplistic scenarios. For instance, a template for a job that fails which sends an AWS SNS with the Job name, component it failed on, and the error message. It wasn't as simple as I thought it would be to figure out and having to use a shared job for such circumstances can be painful because you have to export a bunch of variables.
  • There should be a drop down list for Global Matillion variables as it is difficult to remember at times.
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Likelihood to Renew
Microsoft
No answers on this topic
Matillion
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.
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Usability
Microsoft
No answers on this topic
Matillion
It has been easy to train new employees who don't have previous experience with Matillion. It is quite self-explanatory. There are quite a few things that can be done with Python, however, we have not really looked into this feature much but likely will do in the future. Mostly, it is drag and drop of components and environments can be set up so easy to set up connections as well.
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Support Rating
Microsoft
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
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Matillion
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.
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Implementation Rating
Microsoft
No answers on this topic
Matillion
We were able to control on access and built various enviroment for implementation
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Alternatives Considered
Microsoft
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
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Matillion
Matillion ran circles around Stitch and Striim both in functionality, setup, and performance. There was no real comparison. Fivetran massively outperforms Matillion in pretty much every facet of the production from setup, maintenance, visibility, and usability. It already has the ability to connect any data source to a destination regardless of database type. Why we chose Matillion over Fivetran is that, for our current needs, Matillion provides us with the functionality that we need and a much more competitive price for a smaller company.
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Scalability
Microsoft
No answers on this topic
Matillion
I have been able to connect Matillion to AWS Aurora Databases, MySQL databases, Rest APIs, Files in AWS S3, etc. Being able to load all of that disparate data into one datalake has made data mining and reporting a lot simpler. I wish everything could be implemented as easily as Matillion.
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Return on Investment
Microsoft
  • It is very useful and make things easier
  • Debugging can improve
  • Its better suited than other products with the same objective
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Matillion
  • Saving us time reduces our need for headcount
  • Allows us to collaborate on data-eng pipelines in a transparent way with non-technical stakeholders, ensuring accuracy and continuity
  • Allows us a single platform to log and manage all of our pipelines to pin-point where something failed and why
  • Always has a solution to very common data engineering tasks, i.e., real-time data, connectors, pre-built workflows, etc.
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ScreenShots

Matillion Screenshots

Screenshot of Matillion's GUI, used to orchestrate jobs with control data flow functionality, automating the ETL process.Screenshot of where structured and semi-structured data can be prepared to create clean data sets that can be used with any BI/reporting/visualization tool of choice. Matillion reads and combines data across a target warehouse external storage, such as S3 or Blob.Screenshot of Matillion's self-validating components, sample and row counts. If a job does fail, the warehouse queue services available with Matillion can be used get an alert to a connected email or Slack account.Screenshot of the SQL component used to run custom scripts from within Matillion. With hundreds of pre-built connectors out of the box, Matillion can handle complex transformation needs.