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Apache Hadoop vs Microsoft Parallel Data Warehouse comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

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
6.5
Apache Hadoop offers cost-effective storage and processing, with varying returns based on analytics sophistication and workload optimization.
Sentiment score
6.9
Organizations value ROI from Microsoft Parallel Data Warehouse but see opportunities for improvement, citing efficient data management and integration.
 

Customer Service

Sentiment score
6.5
Apache Hadoop's support varies, with high satisfaction from vendor packages, responsive teams, and helpful documentation and community.
Sentiment score
6.8
Customer service for Microsoft Parallel Data Warehouse is positive, with knowledgeable support but some desire faster, Azure-savvy assistance.
 

Scalability Issues

Sentiment score
7.6
Apache Hadoop offers scalable data management for large-scale deployments, efficiently supports diverse users and adapts across industries.
Sentiment score
7.1
Microsoft Parallel Data Warehouse is generally scalable, though limitations exist compared to Snowflake and BigQuery, especially with large data.
 

Stability Issues

Sentiment score
7.4
Apache Hadoop is stable, especially newer versions, with occasional issues in setup, memory, and online data ingestion.
Sentiment score
7.9
Microsoft Parallel Data Warehouse is stable, reliable, handles large datasets well, and is appreciated for quick issue resolution.
 

Room For Improvement

Apache Hadoop requires enhanced compatibility, improved usability, real-time processing, better security, modern interfaces, and cost-effective solutions to boost adoption.
Microsoft Parallel Data Warehouse needs improved integration, performance, scalability, error messaging, and real-time update capabilities to meet modern data demands.
When there are many users or many expensive queries, it can be very slow.
 

Setup Cost

Apache Hadoop is cost-effective for large-scale deployments, but smaller enterprises face higher expenses despite potential cloud cost savings.
Microsoft Parallel Data Warehouse is cost-effective for large organizations with Azure, but may be expensive for on-premises setups.
 

Valuable Features

Apache Hadoop offers cost-efficient, scalable data processing with HDFS, supporting large datasets and seamless integration with tools like Spark.
Microsoft Parallel Data Warehouse offers enhanced performance, integration with Microsoft tools, and supports big data management for business intelligence.
The interface is very user-friendly.
 

Categories and Ranking

Apache Hadoop
Ranking in Data Warehouse
6th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
39
Ranking in other categories
No ranking in other categories
Microsoft Parallel Data War...
Ranking in Data Warehouse
10th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
35
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Data Warehouse category, the mindshare of Apache Hadoop is 5.2%, down from 6.2% compared to the previous year. The mindshare of Microsoft Parallel Data Warehouse is 1.0%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Warehouse
 

Featured Reviews

Sushil Arya - PeerSpot reviewer
Provides ease of integration with the IT workflow of a business
When working with Kafka, I saw that the data came in an incremental order. The incremental data processing part is still not very effective in Apache Hadoop. If the data is already there, it can be processed very effectively, especially if the data is coming in every second. If you want to know the location of some data every second, then such data is not processed effectively in Apache Hadoop. I can say that one of the features where improvements are required revolves around the licensing cost of the tool. If the tool can build some licensing structures in a pay-per-use manner, organizations can get the look and feel of Apache Hadoop. Apache Hadoop can offer a licensing structure of the product that can be seen as similar to how AWS operates. Apache Hadoop can look into the capability of processing incremental data. The tool's setup process can be a scope of improvement. Also, it is not very simple because while doing the setup, we need to do all the server settings, including port listing and firewall configurations. If we look at other products on the market, then they can be made simpler. There are certain shortcomings when it comes to the product's technical support part, making it an area where improvements are required. The time frame for the resolution is an area that needs to be improved. The overall communication part of the technical support team also needs improvement.
StevenLai - PeerSpot reviewer
Strong scalable solution with streamlined metadata warehousing
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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Top Industries

By visitors reading reviews
Financial Services Firm
34%
Computer Software Company
10%
University
7%
Energy/Utilities Company
6%
Computer Software Company
28%
Financial Services Firm
19%
Insurance Company
8%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Hadoop?
It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.
What is your experience regarding pricing and costs for Apache Hadoop?
The product is open-source, but some associated licensing fees depend on the subscription level. While it might be free for students, organizations typically need to pay for their subscriptions. Th...
What needs improvement with Apache Hadoop?
Hadoop lacks OLAP capabilities. I recommend adding a Delta Lake feature to make the data compatible with ACID properties. Also, video and audio streaming import issues could be improved to ensure p...
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 are many areas for improvement. A major issue is with table statistics. Sometimes the statistics are not refreshed correctly, which causes issues for us. When we update a table, it should tri...
 

Also Known As

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

Learn More

 

Overview

 

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
Auckland Transport, Erste Bank Group, Urban Software Institute, NJVC, Sheraton Hotels and Resorts, Tata Steel Europe
Find out what your peers are saying about Apache Hadoop vs. Microsoft Parallel Data Warehouse and other solutions. Updated: November 2024.
824,067 professionals have used our research since 2012.