Microsoft Parallel Data Warehouse Reviews

Name: Microsoft Parallel Data Warehouse
Brand: Microsoft
Rating: 3.9 (35 reviews)

3.9 out of 5

35 reviews
86% willing to recommend

1,470 followers

What is Microsoft Parallel Data Warehouse?

The traditional structured relational data warehouse was never designed to handle the volume of exponential data growth, the variety of semi-structured and unstructured data types, or the velocity of real time data processing. Microsoft's SQL Server data warehouse solution integrates your traditional data warehouse with non-relational data and it can handle data of all sizes and types, with real-time performance.

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Microsoft Parallel Data Warehouse is the #10 ranked solution in top Data Warehouse solutions. PeerSpot users give Microsoft Parallel Data Warehouse an average rating of 7.8 out of 10. Based on the analysis of the 35 most recent Microsoft Parallel Data Warehouse reviews, the overall sentiment is positive, with a sentiment score of 6.8. Microsoft Parallel Data Warehouse is most commonly compared to Snowflake: Microsoft Parallel Data Warehouse vs Snowflake. Microsoft Parallel Data Warehouse is popular among the large enterprise segment, accounting for 59% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 28% of all views.

Buyer's Guide

Microsoft Parallel Data Warehouse

December 2024

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Helped 824,053 peers since 2012

Featured reviews

StevenLai

Sr. Data Engineer at Amrock

We use it to build our data warehouse and databases, and everything in the back end It helps streamline our metadata warehousing process. As it is our only type of data warehouse and database, it serves as our source, destination, and staging area. This product has many features which are useful…

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PALAK SURI

Senior Software Engineer at Eurofins

The initial setup was fine and straightforward. The migration process related to Microsoft Parallel Data Warehouse has been going on for a month in my company. We have multiple labs, of which it was possible to load the data in 47 labs. With one lab, it took almost a week to load the data since the data count was in millions, due to which it initially failed multiple times, creating a huge issue for us. It's all done right now, but we have faced issues in the past. In our company, we created new databases in the new SQL Server. We replicated the databases, and then we got the data from there.

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PeterMaier

Chief Financial Officer at a retailer with 5,001-10,000 employees

We primarily use Data Warehouse for our business intelligence platform Qlik, which sits on top of it. Data Warehouse is a middleware product. Our users work with Qlik, and our data architects are using SQL Server. It mostly feeds the data to the Qlik cloud product. Very few people are directly…

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Microsoft Parallel Data Warehouse mindshare

As of December 2024, the mindshare of Microsoft Parallel Data Warehouse in the Data Warehouse category stands at 1.0%, down from 1.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Warehouse

PeerAnalyst reports

Type	Title	Date
Category	Data Warehouse	Dec 22, 2024	Download
Product	Reviews, tips, and advice from real users	Dec 22, 2024	Download
Comparison	Microsoft Parallel Data Warehouse vs Snowflake	Dec 22, 2024	Download
Comparison	Microsoft Parallel Data Warehouse vs Oracle Exadata	Dec 22, 2024	Download
Comparison	Microsoft Parallel Data Warehouse vs Teradata	Dec 22, 2024	Download

Title	Rating	Mindshare	Recommending
Snowflake	4.2	17.1%	97%	98 interviews Add to research
Teradata	4.1	17.1%	87%	76 interviews Add to research

Valuable Features

Microsoft Parallel Data Warehouse enhances performance with fast data loading and processing, offering up to 100x speed improvements. Users appreciate its integration with BI tools like Power BI, intuitive interface, and ease of data modeling. The platform efficiently handles large data volumes and supports diverse database systems. Its cost-effectiveness and seamless integration with the Microsoft ecosystem make it appealing, alongside robust data integrity features and scalable infrastructure suitable for large organizations and complex data environments.

"The interface is very user-friendly."
"The interface is very user-friendly."
"It is very strong, scalable, and has tons of features."

Room for Improvement

Microsoft Parallel Data Warehouse requires significant SQL Server expertise and infrastructure planning. It is expensive and shows compatibility issues with various tools. Users report challenges in data integration, query performance, and scalability. The interface is not user-friendly, and error messages are vague. More frequent updates and better visualization features are desired. Integration with non-Microsoft tools needs to be enhanced. The performance lags behind competitors like Oracle, and more flexibility and support for larger datasets are required.

"When there are many users or many expensive queries, it can be very slow."
"When there are many users or many expensive queries, it can be very slow."
"A major issue is with table statistics. Sometimes the statistics are not refreshed correctly, which causes issues for us."

ROI

Microsoft Parallel Data Warehouse has delivered a substantial return on investment by efficiently managing large data volumes and integrating with existing tools. One team observed a full return on investment, emphasizing its role in back-end operations. Another group finds it a quick solution for clients. Some sections are satisfied with the ROI but see room for improvement. However, certain areas of the organization do not actively track ROI.

Pricing

Microsoft Parallel Data Warehouse pricing is generally competitive, often deemed well-priced compared to market offerings, especially for enterprises with large data needs. Costs often vary depending on data size and performance requirements, with some finding it expensive. Licensing agreements are sometimes facilitated through government partnerships. Organizations frequently manage costs through one-time or annual subscriptions, without needing extra support contracts. Integration with Azure is noted as cost-effective, though on-premises solutions may face higher costs.

"The solution is cost-effective."
"They offer an annual subscription. The pricing depends on the size of the environments."
"The tool could be expensive if we need to manage a lot of data."

Popular Use Cases

Organizations primarily use Microsoft Parallel Data Warehouse for storing and analyzing large volumes of data, including historical and real-time data. It supports enterprise data warehouse solutions, helps in building dashboards, integrates and transforms data from multiple sources, and serves as the backend for business intelligence platforms. It is used in sectors like finance, banking, insurance, and retail for tracking sales, shipping, transactions, and other key performance indicators, often utilizing tools like Power BI and Qlik for analytics.

Service and Support

Users report Microsoft Parallel Data Warehouse customer service and technical support as satisfactory. Some have direct Microsoft links, while others rely on online resources like Google and YouTube for assistance. Technical support, rated 7/10, is accessible and knowledgeable, though speed and Azure expertise could improve. A license is needed for support access. User feedback is generally positive, though there's a call for more knowledgeable customer service representatives.

Deployment

Many users found Microsoft Parallel Data Warehouse's initial setup straightforward and efficient, often completed within days or weeks. However, experiences varied, with some finding it complex, especially when migrating from SQL Server or across multiple regions. Effective collaboration with Microsoft's support team was beneficial. Challenges included data complexity and integration. Knowledge and resources significantly influenced the ease of deployment, ranging from a few days to several months, depending on data size and project requirements.

Scalability

Microsoft Parallel Data Warehouse is considered scalable by many users, though SQL expertise is often required for optimal use. Some experience no issues, successfully supporting hundreds of users across various environments, including large enterprises. However, others note limitations with large datasets and seek alternatives like Snowflake for better performance. Organizations find it easy to extend the system, yet a few mention room for scalability enhancement, particularly compared to other technologies.

Stability

Users find Microsoft Parallel Data Warehouse to be stable and reliable. While earlier versions had issues, it performs well even during outages. Stability is dependent on proper data setup and network conditions. It manages large data volumes effectively, though large data processing may slow down operations. Users report quick issue resolution, lack of crashes, and consistent performance. Some have rated its stability nine or ten out of ten, noting successful integration with various database systems.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Microsoft Parallel Data Warehouse Buyer's Guide for additional reliable information.

Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Computer Software Company

28%

Financial Services Firm

19%

Insurance Company

Government

Educational Organization

Retailer

University

Manufacturing Company

Healthcare Company

Wellness & Fitness Company

Real Estate/Law Firm

Construction Company

Energy/Utilities Company

Logistics Company

Pharma/Biotech Company

Recreational Facilities/Services Company

Wholesaler/Distributor

Sports Company

Transportation Company

Non Profit

Media Company

Outsourcing Company

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Learn more about Microsoft Parallel Data Warehouse

Microsoft Parallel Data Warehouse was previously known as Microsoft PDW, SQL Server Data Warehouse, Microsoft SQL Server Parallel Data Warehouse, MS Parallel Data Warehouse.