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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
6.4
Azure Data Factory offers significant ROI, efficiency, and cost savings, with users highlighting benefits in data integration and migration.
Sentiment score
4.9
Users find Microsoft Parallel Data Warehouse effective in managing data, integrating tools, with ROI potential despite indirect tracking.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
Data Engineer at Vthinktechnologies
 

Customer Service

Sentiment score
6.3
Azure Data Factory support is generally satisfactory, with responsive assistance and strong community resources enhancing user satisfaction.
Sentiment score
6.8
Microsoft Parallel Data Warehouse support is generally positive with responsive service, though some suggest enhancements in speed and Azure expertise.
The technical support from Microsoft is rated an eight out of ten.
Chief Analytics Officer at Idiro Analytics
The technical support is responsive and helpful
Sr. Technical Architect at Hexaware Technologies Limited
The technical support for Azure Data Factory is generally acceptable.
Solution Architect at Mercedes-Benz AG
They are responsive and get back to us.
Service Desk Administrator at a real estate/law firm with 1,001-5,000 employees
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.
CEO at Smart Data-Driven Solutions
 

Scalability Issues

Sentiment score
7.4
Azure Data Factory offers scalable cloud-based solutions for diverse operations, despite some third-party integration limitations and use case challenges.
Sentiment score
7.3
Microsoft Parallel Data Warehouse is scalable with SQL benefits, but may lag behind Snowflake in large data handling.
Azure Data Factory is highly scalable.
Chief Analytics Officer at Idiro Analytics
We go from a couple of users to tons of users all the time, and it scales and handles things really well.
Service Desk Administrator at a real estate/law firm with 1,001-5,000 employees
I give the scalability an eight out of ten, indicating it scales well for our needs.
Architecture at a manufacturing company with 10,001+ employees
As a consultant, we hire additional programmers when we need to scale up certain major projects.
Associate Director at Sequentis
 

Stability Issues

Sentiment score
7.8
Azure Data Factory is considered highly stable and reliable, though minor issues can occur, mostly in development environments.
Sentiment score
8.1
Microsoft Parallel Data Warehouse is stable, reliable, handles large volumes well, with occasional speed issues on vast datasets.
The solution has a high level of stability, roughly a nine out of ten.
Chief Analytics Officer at Idiro Analytics
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Architecture at a manufacturing company with 10,001+ employees
 

Room For Improvement

Azure Data Factory needs better integration, UI, documentation, data handling, pricing transparency, real-time processing, connectivity, and scheduling.
Microsoft Parallel Data Warehouse needs better tool integration, scalability, compatibility, frequent updates, competitive pricing, and enhanced error messaging.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Chief Analytics Officer at Idiro Analytics
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Sr. Technical Architect at Hexaware Technologies Limited
There is a problem with the integration with third-party solutions, particularly with SAP.
Solution Architect at Mercedes-Benz AG
It would be better to release patches less frequently, maybe once a month or once every two months.
Associate Director at Sequentis
Addressing the cost would be the number one area for improvement.
CEO at Smart Data-Driven Solutions
When there are many users or many expensive queries, it can be very slow.
Computer engineer at a engineering company with 5,001-10,000 employees
 

Setup Cost

Azure Data Factory's pricing is pay-as-you-go, with costs based on usage, offering competitive and cost-effective solutions.
Microsoft Parallel Data Warehouse offers competitive pricing, suitable for large enterprises, but can be costly for high-performance needs.
The pricing is cost-effective.
Chief Analytics Officer at Idiro Analytics
It is considered cost-effective.
Sr. Technical Architect at Hexaware Technologies Limited
Microsoft Parallel Data Warehouse is very expensive.
Architecture at a manufacturing company with 10,001+ employees
 

Valuable Features

Azure Data Factory offers scalable ETL processes with easy integration, user-friendly interface, and strong orchestration, security, and automation features.
Microsoft Parallel Data Warehouse boosts data loads, integrates with Power BI, and offers scalable BI with minimal costs.
It connects to different sources out-of-the-box, making integration much easier.
Sr. Technical Architect at Hexaware Technologies Limited
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
Data Engineer at Vthinktechnologies
Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.
Director at a computer software company with 1,001-5,000 employees
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
BI/Data Warehouse Analyst at a healthcare company with 501-1,000 employees
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
Architecture at a manufacturing company with 10,001+ employees
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.
Associate Director at Sequentis
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
93
Ranking in other categories
Data Integration (3rd), Cloud Data Warehouse (2nd)
Microsoft Parallel Data War...
Average Rating
7.8
Reviews Sentiment
6.6
Number of Reviews
40
Ranking in other categories
Data Warehouse (12th)
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
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
CEO at Smart Data-Driven Solutions
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
11%
Manufacturing Company
9%
Government
7%
Insurance Company
13%
University
9%
Performing Arts
9%
Manufacturing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise57
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise6
Large Enterprise22
 

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: December 2025.
881,082 professionals have used our research since 2012.