Compare Azure Data Factory vs. IBM InfoSphere DataStage
Azure Data Factory is ranked 14th in Data Integration Tools with 2 reviews while IBM InfoSphere DataStage is ranked 6th in Data Integration Tools with 6 reviews. Azure Data Factory is rated 9.6, while IBM InfoSphere DataStage is rated 8.4. The top reviewer of Azure Data Factory writes "A straightforward solution with a nice interface and ability to integrate with GitHub". On the other hand, the top reviewer of IBM InfoSphere DataStage writes "Powerful, reliable and the ability to run it in parallel mode makes it very fast". Azure Data Factory is most compared with Informatica Enterprise Data Catalog, Talend Open Studio and Dell Boomi AtomSphere, whereas IBM InfoSphere DataStage is most compared with SSIS, Informatica PowerCenter and Talend Open Studio. See our Azure Data Factory vs. IBM InfoSphere DataStage report.
Most Helpful Review | |||
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 Find out what your peers are saying about Azure Data Factory vs. IBM InfoSphere DataStage and other solutions. Updated: September 2019. 372,124 professionals have used our research since 2012. | |||
Quotes From Members
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Pros | |
The solution has a good interface and the integration with GitHub is very useful. This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added. | The data lineage report can be filtered for reporting. The reports are user-friendly and take less time to find what you need. DataStage works better with Linux operating systems when the application services are hosted on Linux system equipment, but it's powerful on Windows too. The most valuable feature is the ability to transfer information via notes. The product is a stable and powerful data management solution that can run in parallel mode for enhanced speed. The solution has improved the time it takes to perform tasks related to batch applications. Highly customizable: Allowing you to handle multiple data latencies (scheduled batch, on-demand, and real-time) in the same job. |
Cons | |
In the next release, it's important that some sort of scheduler for running tasks is added. The thing we missed most was data update, but this is now available as of two weeks ago. | We would be happy to see in next versions the ability to return several parameters from jobs. Now, jobs can return just one parameter. If they could return several parameters, that would be great. I really like this tool, but the administration should be on the same client application because a lot of administration features are not on the client-side, and they usually need to have administrative access. It's quite complicated to force IT teams to have separate administrative access from the developers. The documentation and in-application help for this solution need to be improved, especially for new features. The interface needs work to be more user-friendly. The solution should be more user-friendly. Working with some of the big data components is good, but I can see improvements are needed. |
Pricing and Cost Advice | |
Information Not Available | Small and medium-sized companies cannot afford to pay for this solution. Pricing varies based on use, and it is not as costly as some competing enterprise solutions. High-cost of ownership: They could take a page from open source software. |
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Ranking  | |
Views 2,858 Comparisons 2,197 Reviews 2 Average Words per Review 396 Avg. Rating 9.5 | Views 14,669 Comparisons 11,644 Reviews 6 Average Words per Review 712 Avg. Rating 8.3 |
Top Comparisons | |
 Compared 16% of the time.  Compared 13% of the time.  Compared 8% of the time. |  Compared 21% of the time. Compared 18% of the time. Compared 15% of the time. |
Learn | |
| Microsoft | IBM |
Overview | |
Create, schedule, and manage your data integration at scale with Azure Data Factory - a hybrid data integration (ETL) service. Work with data wherever it lives, in the cloud or on-premises, with enterprise-grade security. | IBM InfoSphere DataStage integrates data across multiple systems using a high performance parallel framework, and it supports extended metadata management and enterprise connectivity. The scalable platform provides more flexible integration of all types of data, including big data at rest (Hadoop-based) or in motion (stream-based), on distributed and mainframe platforms. |
Offer | |
Learn more about Azure Data Factory | Learn more about IBM InfoSphere DataStage |
Sample Customers | |
| Milliman, Pier 1 Imports, Rockwell Automation, Ziosk, Real Madrid | Dubai Statistics Center, Etisalat Egypt |
Top Industries | |
Software R&D Company45% Comms Service Provider7% Retailer7% Government5% | Software R&D Company37% Comms Service Provider11% Financial Services Firm9% Government9% |
Find out what your peers are saying about Azure Data Factory vs. IBM InfoSphere DataStage and other solutions. Updated: September 2019.
372,124 professionals have used our research since 2012.
See also Azure Data Factory Reviews, IBM InfoSphere DataStage Reviews, and our list of Best Data Integration Tools Companies.
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