Azure Data Factory vs IBM Cloud Pak for Data comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Number of Reviews
81
Ranking in other categories
Cloud Data Warehouse (3rd)
IBM Cloud Pak for Data
Ranking in Data Integration
16th
Average Rating
8.0
Number of Reviews
11
Ranking in other categories
Data Virtualization (3rd)
 

Market share comparison

As of June 2024, in the Data Integration category, the market share of Azure Data Factory is 9.6% and it decreased by 29.4% compared to the previous year. The market share of IBM Cloud Pak for Data is 1.4% and it decreased by 36.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
Unique Categories:
Cloud Data Warehouse
13.6%
Data Virtualization
25.0%
 

Featured Reviews

KR
Mar 6, 2024
Offers good integration with SQL pools and serverless architecture
We have multiple banking applications running on SSIS pipelines. We're in the process of upgrading them to a hybrid cloud architecture. For that, we use Azure Data Factory to move data from on-premises to the cloud – mainly for back-end database operations and ETL transformations. We primarily use it to load data from an on-premises SQL Server to either Blob storage or an Azure SQL data warehouse. For other integrations, especially those outside of Azure, we tend to use Informatica Cloud Services (ICS). For structured data loading, we use it However, we use Informatica for unstructured or semi-structured data. We also use Snowflake for ETL processes and sometimes for streaming. In my opinion, ADF isn't as suitable for streaming – for streaming, Snowflake streamlets or Informatica structured streaming are more reliable. ADF works well for batch processing, though.
EV
May 27, 2020
Good reporting, but resource utilization is high and technical support can be improved
We are a consultancy company that specializes in data integration and we advise clients on what solutions will best meet their requirements The most valuable features are data virtualization and reporting. Data virtualization has to do with integrating data from different databases.  The…

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"Data Factory's most valuable feature is Copy Activity."
"It is easy to deploy workflows and schedule jobs."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"The most valuable feature of this solution would be ease of use."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"I am one hundred percent happy with the stability."
"The most valuable features are data virtualization and reporting."
"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"Scalability-wise, I rate the solution a nine or ten out of ten."
"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."
"DataStage allows me to connect to different data sources."
"Its data preparation capabilities are highly valuable."
"The most valuable feature of IBM Cloud Pak for Data is the Modeler flows. The ability to develop models using a graphical approach and the capability to connect to various sources, as well as the data virtualization capabilities, allow me to easily access and utilize data that is dispersed across different sources."
 

Cons

"Some known bugs and issues with Azure Data Factory could be rectified."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"Data Factory's performance during heavy data processing isn't great."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"The deployment should be easier."
"The solution could have more connectors."
"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."
"One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."
"The tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."
"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."
"The technical support could be a little better."
"The solution's user experience is an area that has room for improvement."
"Cloud Pak would be improved with integration with cloud service providers like Cloudera."
 

Pricing and Cost Advice

"I would not say that this product is overly expensive."
"The price you pay is determined by how much you use it."
"Understanding the pricing model for Data Factory is quite complex."
"I would rate Data Factory's pricing nine out of ten."
"The pricing is a bit on the higher end."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"ADF is cheaper compared to AWS."
"It's not particularly expensive."
"The solution is expensive."
"IBM Cloud Pak for Data is expensive. If we include the training time and the machine learning, it's expensive. The cost of the execution is more reasonable."
"For the licensing of the solution, there is a yearly payment that needs to be made. Also, since it is expensive, cost-wise, I rate the solution an eight or nine out of ten."
"It's quite expensive."
"I think that this product is too expensive for smaller companies."
"I don't have the exact licensing cost for IBM Cloud Pak for Data, as my company is still finalizing requirements, including monthly, yearly, and three-year licensing fees. Still, on a scale of one to five, I'd rate it a three because, compared to other vendors, it's more complicated."
"Cloud Pak's cost is a little high."
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
13%
Manufacturing Company
8%
Healthcare Company
7%
Financial Services Firm
27%
Computer Software Company
11%
Manufacturing Company
8%
Government
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
What do you like most about IBM Cloud Pak for Data?
DataStage allows me to connect to different data sources.
What needs improvement with IBM Cloud Pak for Data?
The product must improve its performance. We see typical cloud-related issues in the solution. IBM can still focus more on keeping the performance up and keeping it 100% available all the time.
 

Also Known As

No data available
Cloud Pak for Data
 

Learn More

 

Overview

 

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Qatar Development Bank, GuideWell, Skanderborg Music Festival
Find out what your peers are saying about Azure Data Factory vs. IBM Cloud Pak for Data and other solutions. Updated: May 2024.
787,061 professionals have used our research since 2012.