Azure Data Factory vs. Informatica Cloud Data Quality

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
Informatica Cloud Data Quality
Score 8.5 out of 10
N/A
The vendor states that Informatica Data Quality empowers companies to take a holistic approach to managing data quality across the entire organization, and that with Informatica Data Quality, users are able to ensure the success of data-driven digital transformation initiatives and projects across users, types, and scale, while also automating mission-critical tasks.N/A
Pricing
Azure Data FactoryInformatica Cloud Data Quality
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Azure Data FactoryInformatica Cloud Data Quality
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Azure Data FactoryInformatica Cloud Data Quality
Top Pros
Top Cons
Features
Azure Data FactoryInformatica Cloud Data Quality
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
Informatica Cloud Data Quality
-
Ratings
Connect to traditional data sources9.27 Ratings00 Ratings
Connecto to Big Data and NoSQL9.07 Ratings00 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
Informatica Cloud Data Quality
-
Ratings
Simple transformations9.27 Ratings00 Ratings
Complex transformations7.77 Ratings00 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
Informatica Cloud Data Quality
-
Ratings
Data model creation8.45 Ratings00 Ratings
Metadata management7.56 Ratings00 Ratings
Business rules and workflow7.57 Ratings00 Ratings
Collaboration7.16 Ratings00 Ratings
Testing and debugging7.57 Ratings00 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
Informatica Cloud Data Quality
-
Ratings
Integration with data quality tools7.47 Ratings00 Ratings
Integration with MDM tools8.07 Ratings00 Ratings
Data Quality
Comparison of Data Quality features of Product A and Product B
Azure Data Factory
-
Ratings
Informatica Cloud Data Quality
8.9
6 Ratings
0% below category average
Data source connectivity00 Ratings9.36 Ratings
Data profiling00 Ratings9.26 Ratings
Master data management (MDM) integration00 Ratings8.96 Ratings
Data element standardization00 Ratings8.26 Ratings
Match and merge00 Ratings8.76 Ratings
Address verification00 Ratings9.06 Ratings
Best Alternatives
Azure Data FactoryInformatica Cloud Data Quality
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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
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Score 8.2 out of 10
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User Ratings
Azure Data FactoryInformatica Cloud Data Quality
Likelihood to Recommend
9.3
(7 ratings)
9.3
(20 ratings)
Likelihood to Renew
-
(0 ratings)
6.6
(14 ratings)
Usability
-
(0 ratings)
8.0
(1 ratings)
Availability
-
(0 ratings)
9.0
(2 ratings)
Performance
-
(0 ratings)
9.0
(1 ratings)
Support Rating
7.0
(1 ratings)
-
(0 ratings)
Online Training
-
(0 ratings)
10.0
(1 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Azure Data FactoryInformatica Cloud Data Quality
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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Informatica
Helps to increase productivity, optimize costs, and democratize data across multiple cloud environments with cloud ETL and ELT. Capacity to integrate data sources at scale and with ease. Has cloud data integration capabilities that cover diverse sets of patterns, use cases, and users ensuring we have well-architected and seamless automated data pipelines.
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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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Informatica
  • The matching algorithms in IDQ are very powerful if you understand the different types that they offer (e.g., Hamming Distance, Jaro, Bigram, etc..). We had to play around with it to see which best suit our own needs of identifying and eliminating duplicate customers. Setting up the whole process (e.g., creating the KeyGenerator Transformation, setting up the matching threshold, etc..) can be somewhat time consuming and a challenge if you don't first standardize your data.
  • The integration with PowerCenter is great if you have both. You can either import your mappings directly to PowerCenter or to an XML file. The only downside is that some of the transformations are unique to IDQ, so you are not really able to edit them once in PowerCenter.
  • The standardizer transformation was key in helping us standardize our customer data (e.g., names, addresses, etc..). It was helpful due to having create a reference table containing the standardized value and the associated unstandardized values. What was great was that if you used Informatica Analyst, a business analyst could login and correct any of the values.
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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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Informatica
  • Several partnerships diminishing the value of technologies
  • Unable to get list of objects from Repository (like sources & targets) that don't have any dependency
  • Scheduling: The built-in scheduling tool has many constraints such as handling Unix/VB scripts etc. Most enterprises use third party tools for this.
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Likelihood to Renew
Microsoft
No answers on this topic
Informatica
As pointed out earlier, due all the robust features IDQ has, our use f the product is successful and stable. IDQ is being used in multiple sources (from CRM application and in batch mode). As this is an iterative process, we are looking to improve our system efficiency using IDQ.
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Usability
Microsoft
No answers on this topic
Informatica
Easy to use not only for developers but also business users
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Reliability and Availability
Microsoft
No answers on this topic
Informatica
The application works well except an occasional error out while using the system. It usually gets fixed when restarting the Infa server
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Performance
Microsoft
No answers on this topic
Informatica
Performance works just fine. It was able to load 200+ business terms, 150+ DQ automation, etc. very 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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Informatica
No answers on this topic
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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Informatica
Informatica Data Quality has a wide range of cleansing features, that are detailed, professional, and accurate in scaling down the required database. Further, Informatica Data Quality ensures there is proper collaboration, and this fosters businesses to have the freedom of working closely with several programs. Finally, Informatica Data Quality design is authentic and allows personalization.
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Scalability
Microsoft
No answers on this topic
Informatica
Scalability works as expected and it is truly an enterprise system.
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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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Informatica
  • Integration with tools like PowerCenter helped faster delivery of product, and at the same time conversion
  • Reduce overall project cost due to bad data , bad quality, exceptions identified nearing go-live and post production
  • Employee efficiency is increased exponentially due to more automated, customized tool
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ScreenShots