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Microsoft Parallel Data Warehouse vs Snowflake Analytics 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
5.7
Most are satisfied with ROI, acknowledging its benefits, though improvements are possible, as it efficiently enhances backend operations.
Sentiment score
5.7
Snowflake Analytics offers potential 40-50% performance benefits and cost savings, though financial returns vary based on user needs.
 

Customer Service

Sentiment score
6.7
Microsoft Parallel Data Warehouse support is responsive and expert, though users sometimes need online resources for faster solutions.
Sentiment score
7.2
Snowflake Analytics receives praise for responsive support, though some suggest improvements in complex issue resolution and occasional delay handling.
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.
The Snowflake Analytics documentation is excellent.
Recently we had a two-day session where the Snowflake Analytics team provided a demo on Cortex AI and its features.
The technical support for Snowflake Analytics is excellent based on what I have heard from others.
 

Scalability Issues

Sentiment score
7.2
Microsoft Parallel Data Warehouse excels in scalability, integration, and expandability, though improvements are needed for large data sets.
Sentiment score
7.8
Snowflake Analytics efficiently manages large data volumes with dynamic cloud scaling, offering superior scalability and cost efficiency versus competitors.
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.
Storage is unlimited because they use S3 if it is AWS, so storage has no limit.
It supports both horizontal and vertical scaling effectively.
Maintaining security and data governance becomes easier with an entire data lake in place, and the scalability improves performance.
 

Stability Issues

Sentiment score
8.0
Microsoft Parallel Data Warehouse is praised for its stability, reliability, and quick issue resolution, despite time-consuming extensive dataset processing.
Sentiment score
8.4
Snowflake Analytics is highly rated for its stable and reliable performance, ensuring minimal disruptions and high availability.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Snowflake Analytics has been stable and reliable in my experience.
Snowflake Analytics is stable, scoring around eight point five to nine out of ten.
The Power BI team raised tickets for both Power BI and Snowflake Analytics, and their responses were very good.
 

Room For Improvement

Microsoft Parallel Data Warehouse presents complexity, compatibility challenges, performance issues, high costs, and requires improved in-memory analysis and updates.
Snowflake Analytics requires enhancements in ML, cloud, legacy integration, cost transparency, user experience, and performance with large datasets.
It would be better to release patches less frequently, maybe once a month or once every two months.
Addressing the cost would be the number one area for improvement.
When there are many users or many expensive queries, it can be very slow.
AIML-based SQL prompt and query generation could be an area for enhancement.
If it offered flexibility similar to Oracle and supported more heterogeneous data sources and database connectivity, it would be even better.
I would prefer Snowflake Analytics to improve their support response times, as sometimes the responses we receive are not very prompt and ticket assignments may not be timely.
 

Setup Cost

Microsoft Parallel Data Warehouse's pricing varies by needs; Azure integration can be cost-effective, but technical support costs extra.
Snowflake Analytics offers flexible usage-based pricing impacting cost, with views varying on expense and competitive pay-as-you-go benefits.
Microsoft Parallel Data Warehouse is very expensive.
Snowflake charges per query, which amounts to a very minor cost, such as $0.015 per query.
Snowflake is better and cheaper than Redshift and other cloud warehousing systems.
Snowflake Analytics is quite economical.
 

Valuable Features

Microsoft Parallel Data Warehouse offers performance, integration, flexibility, and cost-effectiveness for large data management and business intelligence.
Snowflake Analytics provides efficient, secure, and scalable data management, supporting seamless integration and cost-effective analytics with advanced features.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
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.
Running a considerable query on Microsoft SQL Server may take up to thirty minutes or an hour, while Snowflake executes the same query in less than three minutes.
Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.
It is a data offering where I can see data lineage, data governance, and data security.
 

Categories and Ranking

Microsoft Parallel Data War...
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
39
Ranking in other categories
Data Warehouse (10th)
Snowflake Analytics
Average Rating
8.4
Reviews Sentiment
7.1
Number of Reviews
42
Ranking in other categories
Web Analytics (2nd), Cloud Data Warehouse (10th)
 

Featured Reviews

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.
Garima Goel - PeerSpot reviewer
Have created secure cloud-based data lakes and improved real-time data processing using integrated AI features
There are many capabilities which Snowflake Analytics offers that I find valuable, such as the storage and compute engine that allows working with any cloud system such as AWS or Azure, alongside its efficiencies in storage computation and cost-effectiveness, which saves money compared to on-premise systems. We also have features such as pre-cached results, Time Travel, and fail-safe, which are very useful for restoring data if deleted accidentally, and the streams and data pipes that facilitate real-time ingestion are great features as well. Snowflake Analytics offers multiple new connectors, allowing me to connect it with Kafka, and with Snowpark, I can work with any programming language such as Python, Java, or Scala for data processing and analysis. The data sharing feature offered by Snowflake Analytics is good because it allows sharing specific sets of data to end customers or users from different Snowflake Analytics accounts without exposing the entire dataset for data security reasons. Snowflake Analytics' support for machine learning models and real-time insights has enhanced significantly. Originally, it wasn't strong in AI/ML, but now it has multiple models and forecasting capabilities, providing good competition to tools such as Databricks and Spark. In BI, I have worked majorly with Microsoft Power BI, and the integration with Snowflake Analytics is very easy. The way we integrate Snowflake Analytics with other on-premise systems just requires the warehouse details, username, passwords, and the account name, along with multiple options such as client ID and credentials for logging in and creating a session. The end-to-end encryption provided by Snowflake Analytics is very important because, in my previous firm, working in finance and investment management, data encryption is necessary due to the sensitive nature of customer data and the involvement of people's money. It's crucial to have encryption in transit and at rest, along with data masking features which Snowflake Analytics offers.
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Top Industries

By visitors reading reviews
Computer Software Company
22%
Insurance Company
13%
Financial Services Firm
8%
Manufacturing Company
6%
Computer Software Company
16%
Retailer
9%
Financial Services Firm
9%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise6
Large Enterprise21
By reviewers
Company SizeCount
Small Business10
Midsize Enterprise12
Large Enterprise21
 

Questions from the Community

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...
What is your experience regarding pricing and costs for Snowflake Analytics?
Snowflake Analytics is quite economical. It does not appear to incur significant extra expenses beyond the solution's initial cost. However, a complete pricing analysis is still in progress.
What needs improvement with Snowflake Analytics?
There are some minor issues encountered with Snowflake Analytics, such as problems when working with identity or row number generating different results or issues with referential integrity, which ...
 

Also Known As

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

Overview

 

Sample Customers

Auckland Transport, Erste Bank Group, Urban Software Institute, NJVC, Sheraton Hotels and Resorts, Tata Steel Europe
Lionsgate, Adobe, Sony, Capital One, Akamai, Deliveroo, Snagajob, Logitech, University of Notre Dame, Runkeeper
Find out what your peers are saying about Microsoft Parallel Data Warehouse vs. Snowflake Analytics and other solutions. Updated: September 2025.
869,566 professionals have used our research since 2012.