Azure Data Factory vs. IBM DataStage

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
IBM DataStage
Score 8.6 out of 10
N/A
IBM® DataStage® is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak® for Data offers automated integration capabilities in a hybrid or multicloud environment.N/A
Pricing
Azure Data FactoryIBM DataStage
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Azure Data FactoryIBM DataStage
Free Trial
NoYes
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 FactoryIBM DataStage
Considered Both Products
Azure Data Factory
Chose Azure Data Factory
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.
IBM DataStage

No answer on this topic

Top Pros
Top Cons
Features
Azure Data FactoryIBM DataStage
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
IBM DataStage
9.1
9 Ratings
10% above category average
Connect to traditional data sources9.27 Ratings9.59 Ratings
Connecto to Big Data and NoSQL9.07 Ratings8.88 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
IBM DataStage
9.5
9 Ratings
12% above category average
Simple transformations9.27 Ratings9.89 Ratings
Complex transformations7.77 Ratings9.39 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Azure Data Factory
7.8
7 Ratings
5% below category average
IBM DataStage
9.0
9 Ratings
10% above category average
Data model creation8.45 Ratings9.36 Ratings
Metadata management7.66 Ratings8.78 Ratings
Business rules and workflow7.67 Ratings8.18 Ratings
Collaboration7.26 Ratings9.09 Ratings
Testing and debugging7.67 Ratings9.59 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
IBM DataStage
8.9
8 Ratings
8% above category average
Integration with data quality tools7.57 Ratings8.88 Ratings
Integration with MDM tools8.07 Ratings9.08 Ratings
Best Alternatives
Azure Data FactoryIBM DataStage
Small Businesses
Skyvia
Skyvia
Score 9.6 out of 10
Skyvia
Skyvia
Score 9.6 out of 10
Medium-sized Companies
InfoSphere
InfoSphere
Score 8.2 out of 10
InfoSphere
InfoSphere
Score 8.2 out of 10
Enterprises
InfoSphere
InfoSphere
Score 8.2 out of 10
InfoSphere
InfoSphere
Score 8.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Azure Data FactoryIBM DataStage
Likelihood to Recommend
9.3
(7 ratings)
8.8
(9 ratings)
Usability
-
(0 ratings)
9.0
(2 ratings)
Performance
-
(0 ratings)
9.0
(1 ratings)
Support Rating
7.0
(1 ratings)
9.6
(3 ratings)
User Testimonials
Azure Data FactoryIBM DataStage
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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IBM
Excellent Cloud data mapping tool and easy creating multiple project data analytics in real-time and the report distribution are excellent via this IBM product. Easy tool to provide data visualization and the integration is effective and helpful to migrating huge amounts of data across other platforms and different websites insights gathering.
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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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IBM
  • Data movement
  • Seamless integration of scripts and etl jobs
  • Descriptive logging
  • Ability to work with myriad of data assets
  • Direct integration for Governance catalog
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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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IBM
  • Connector Stages to Snowflake on the cloud. We had some issues initially but since then had been corrected.
  • Accessing tool from a browser (zero foot-print). Currently we need to either install locally or connect to a server to do ETL work.
  • Diversify ways of authenticating users.
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Usability
Microsoft
No answers on this topic
IBM
Because it is robust, and it is being continuously improved. DS is one of the most used and recognized tools in the market. Large companies have implemented it in the first instance to develop their DW, but finding the advantages it has, they could use it for other types of projects such as migrations, application feeding, etc.
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Performance
Microsoft
No answers on this topic
IBM
It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records. It is necessary to have people with experience to be able to determine which algorithm to use and understand why.
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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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IBM
I believe that IBM generally has one of the worst and most complex assistance systems (physical and online) that exists.
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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.
Read full review
IBM
It's obvious since they both are from the same vendors and it makes it easier and can get better rates for licensing. Also, sales rapes are very helpful in case of escalations and critical issues.
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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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IBM
  • Reduce development time by 65% compared with hand coding.
  • Reduces ETL process maintenance times.
  • Better data governance for technical and non-technical people.
  • Improve time to market for initiatives that require data integration.
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ScreenShots