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Azure Data Factory vs Microsoft Parallel Data Warehouse comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

ROI

Sentiment score
6.8
Azure Data Factory offers cost-effective, efficient data consolidation for actionable insights, saving time and resources compared to manual processes.
Sentiment score
5.7
Most are satisfied with ROI, acknowledging its benefits, though improvements are possible, as it efficiently enhances backend operations.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
 

Customer Service

Sentiment score
6.4
Azure Data Factory support is generally satisfactory, with responsive assistance, though some users report delays or costly consulting.
Sentiment score
6.7
Microsoft Parallel Data Warehouse support is responsive and expert, though users sometimes need online resources for faster solutions.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
The technical support for Azure Data Factory is generally acceptable.
I would rate my experience with technical support around six on a scale of 1 to 10 because I have not had a particular experience with technical support.
 

Scalability Issues

Sentiment score
7.5
Azure Data Factory is highly scalable and flexible but has room for improvement with third-party integrations and large datasets.
Sentiment score
7.2
Microsoft Parallel Data Warehouse excels in scalability, integration, and expandability, though improvements are needed for large data sets.
Azure Data Factory is highly scalable.
I give the scalability an eight out of ten, indicating it scales well for our needs.
As a consultant, we hire additional programmers when we need to scale up certain major projects.
 

Stability Issues

Sentiment score
7.8
Azure Data Factory is stable and reliable, with occasional issues in responsiveness and large dataset handling.
Sentiment score
8.0
Microsoft Parallel Data Warehouse is praised for its stability, reliability, and quick issue resolution, despite time-consuming extensive dataset processing.
The solution has a high level of stability, roughly a nine out of ten.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
 

Room For Improvement

Azure Data Factory needs better integration, scheduling, support, AI features, and user interface improvements for efficient data management.
Microsoft Parallel Data Warehouse presents complexity, compatibility challenges, performance issues, high costs, and requires improved in-memory analysis and updates.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
There is a problem with the integration with third-party solutions, particularly with SAP.
Addressing the cost would be the number one area for improvement.
It would be better to release patches less frequently, maybe once a month or once every two months.
The ETL designing process could be optimized for better efficiency.
 

Setup Cost

Azure Data Factory offers competitive, flexible pricing based on usage, with costs integrating Azure services and varying significantly.
Microsoft Parallel Data Warehouse's pricing varies by needs; Azure integration can be cost-effective, but technical support costs extra.
The pricing is cost-effective.
It is considered cost-effective.
Microsoft Parallel Data Warehouse is very expensive.
 

Valuable Features

Azure Data Factory excels in data integration with user-friendly features, scalability, and over 100 connectors for seamless data movement.
Microsoft Parallel Data Warehouse offers performance, integration, flexibility, and cost-effectiveness for large data management and business intelligence.
The orchestration features in Azure Data Factory are definitely useful, as it is not only for Azure Data Factory; we can also include DataBricks and other services for integrating the data solution, making it a very beneficial feature.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
It connects to different sources out-of-the-box, making integration much easier.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
The biggest advantage of Microsoft Parallel Data Warehouse is the possibility to stop or pause the service because it can be very expensive.
There's a feature that allows users to set alerts on triggers within reports, enabling timely actions on pending applications and effectively reducing waiting time.
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (2nd)
Microsoft Parallel Data War...
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
39
Ranking in other categories
Data Warehouse (10th)
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Integrates diverse data sources and streamlines ETL processes effectively
Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.
HassanFatemi - PeerSpot reviewer
Has handled large volumes of data effectively but still needs cost flexibility
There could be improvements on the cost side of Microsoft Parallel Data Warehouse because it is still considered to be quite expensive by a lot of users, and many companies are not interested in solutions with parallel data warehousing due to this expense. Addressing the cost would be the number one area for improvement. Additionally, I have not worked recently with it, so I don't know if this feature already exists, but if it doesn't, having an elastic feature that adjusts the service's power dynamically based on the workload would be beneficial instead of fixing the power at a specific level.
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
6%
Computer Software Company
22%
Insurance Company
13%
Financial Services Firm
8%
Manufacturing Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise6
Large Enterprise21
 

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 Microsoft Parallel Data Warehouse?
Microsoft Parallel Data Warehouse provides good firewall processing in terms of response time.
What needs improvement with Microsoft Parallel Data Warehouse?
There could be improvements on the cost side of Microsoft Parallel Data Warehouse because it is still considered to be quite expensive by a lot of users, and many companies are not interested in so...
 

Also Known As

No data available
Microsoft PDW, SQL Server Data Warehouse, Microsoft SQL Server Parallel Data Warehouse, MS Parallel Data Warehouse
 

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
Auckland Transport, Erste Bank Group, Urban Software Institute, NJVC, Sheraton Hotels and Resorts, Tata Steel Europe
Find out what your peers are saying about Azure Data Factory vs. Microsoft Parallel Data Warehouse and other solutions. Updated: September 2025.
869,566 professionals have used our research since 2012.