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BigQuery 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
7.4
Organizations experienced improved performance and cost savings after adopting BigQuery, achieving a 75% cost reduction and efficient data management.
Sentiment score
6.9
Most are satisfied with ROI, acknowledging its benefits, though improvements are possible, as it efficiently enhances backend operations.
 

Customer Service

Sentiment score
7.2
Customers generally find BigQuery support helpful, but integration challenges and resource availability need improvement despite positive responsiveness.
Sentiment score
6.8
Microsoft Parallel Data Warehouse support is responsive and expert, though users sometimes need online resources for faster solutions.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
 

Scalability Issues

Sentiment score
8.0
BigQuery offers impressive scalability and efficiency for large data, but may be costly and present integration challenges for smaller users.
Sentiment score
7.2
Microsoft Parallel Data Warehouse excels in scalability, integration, and expandability, though improvements are needed for large data sets.
It is a 10 out of 10 in terms of scalability.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
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
8.5
BigQuery is highly stable and reliable for cloud data analytics, efficiently handling large volumes with minor 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.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
 

Room For Improvement

BigQuery users face challenges with migration, integration, cost, scaling, user interfaces, and call for better machine learning capabilities.
Microsoft Parallel Data Warehouse presents complexity, compatibility challenges, performance issues, high costs, and requires improved in-memory analysis and updates.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
It would be better to release patches less frequently, maybe once a month or once every two months.
When there are many users or many expensive queries, it can be very slow.
The ETL designing process could be optimized for better efficiency.
 

Setup Cost

BigQuery offers flexible, pay-as-you-go pricing based on data usage, with low storage costs and adaptable enterprise plans.
Microsoft Parallel Data Warehouse's pricing varies by needs; Azure integration can be cost-effective, but technical support costs extra.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
Microsoft Parallel Data Warehouse is very expensive.
 

Valuable Features

BigQuery excels in scalability, performance, cost-efficiency, and integration with Google products, making it ideal for complex data analyses.
Microsoft Parallel Data Warehouse offers performance, integration, flexibility, and cost-effectiveness for large data management and business intelligence.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
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.
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
 

Categories and Ranking

BigQuery
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
41
Ranking in other categories
Cloud Data Warehouse (3rd)
Microsoft Parallel Data War...
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
38
Ranking in other categories
Data Warehouse (10th)
 

Featured Reviews

Luís Silva - PeerSpot reviewer
Handles large data sets efficiently and offers flexible data management capabilities
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.
Ashok Bhadra - PeerSpot reviewer
Provides flexible data handling and integrates easily but requires less frequent patch releases
Microsoft Parallel Data Warehouse keeps giving updates and new features. In my first consultancy, I transitioned a mortgage company from Oracle OBIEE to Microsoft Parallel Data Warehouse to greatly reduce the mortgage approval time. 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. It extracts data from the ERP system, and we are doing extensive data analytics. The system handles target marketing for a company which does 85 to 90% of its business in wholesale. We have a variety of clients from mom-and-pop stores to big box stores, and we have intermediaries. We sell through approximately 13 retail channels including Etsy, our retail website, Faire, Wayfair, and Walmart. Each channel requires different types of inventory updates and packaging, which is managed automatically. A significant amount of development is done on Microsoft Parallel Data Warehouse where all these processes are automatically fed out and updated. We also conduct inventory analysis and aging analysis. We identify seasonal buyers, track their purchasing habits, and auto-trigger campaigns for them, sometimes offering discounts on various items. We've found that combining top-selling items with non-selling items can lead to increased sales, as people often buy additional items when they find one on sale.
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Top Industries

By visitors reading reviews
Computer Software Company
17%
Financial Services Firm
15%
Manufacturing Company
11%
Retailer
8%
Computer Software Company
29%
Insurance Company
11%
Financial Services Firm
8%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about BigQuery?
The initial setup process is easy.
What is your experience regarding pricing and costs for BigQuery?
I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...
What needs improvement with BigQuery?
I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery compon...
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?
The patch releases are a concern. For a customer with tons of servers, frequent patches lead to required reboots, which interrupts business. It would be better to release patches less frequently, m...
 

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

Information Not Available
Auckland Transport, Erste Bank Group, Urban Software Institute, NJVC, Sheraton Hotels and Resorts, Tata Steel Europe
Find out what your peers are saying about BigQuery vs. Microsoft Parallel Data Warehouse and other solutions. Updated: July 2025.
862,624 professionals have used our research since 2012.