Apache Hadoop vs Microsoft Parallel Data Warehouse comparison

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Executive Summary

We performed a comparison between Apache Hadoop and Microsoft Parallel Data Warehouse based on real PeerSpot user reviews.

Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Apache Hadoop vs. Microsoft Parallel Data Warehouse Report (Updated: November 2022).
657,849 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The scalability of Apache Hadoop is very good.""Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial.""The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable.""Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability.""We selected Apache Hadoop because it is not dependent on third-party vendors.""I liked that Apache Hadoop was powerful, had a lot of tools, and the fact that it was free and community-developed.""Hadoop is extensible — it's elastic.""One valuable feature is that we can download data."

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"I like Data Warehouse's data integrity features. Data integrity is what databases are made for as opposed to spreadsheets.""The solution has been reliable.""We have complete control over our data."

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Cons
"The price could be better. I think we would use it more, but the company didn't want to pay for it. Hortonworks doesn't exist anymore, and Cloudera killed the free version of Hadoop.""Real-time data processing is weak. This solution is very difficult to run and implement.""The integration with Apache Hadoop with lots of different techniques within your business can be a challenge.""It requires a great deal of learning curve to understand. The overall Hadoop ecosystem has a large number of sub-products. There is ZooKeeper, and there are a whole lot of other things that are connected. In many cases, their functionalities are overlapping, and for a newcomer or our clients, it is very difficult to decide which of them to buy and which of them they don't really need. They require a consulting organization for it, which is good for organizations such as ours because that's what we do, but it is not easy for the end customers to gain so much knowledge and optimally use it.""Hadoop's security could be better.""From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective.""I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness.""I think more of the solution needs to be focused around the panel processing and retrieval of data."

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"I would like the ability to do more real-time type updates instead of batch-oriented updates.""We'd like to see it be a bit more compatible with other solutions.""The query is slow if we don't optimize it."

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Pricing and Cost Advice
  • "The price of Apache Hadoop could be less expensive."
  • "If my company can use the cloud version of Apache Hadoop, particularly the cloud storage feature, it would be easier and would cost less because an on-premises deployment has a higher cost during storage, for example, though I don't know exactly how much Apache Hadoop costs."
  • "We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable."
  • "The price could be better. Hortonworks no longer exists, and Cloudera killed the free version of Hadoop."
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    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer:Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial.
    Top Answer:I am not sure about the price, but in terms of usability and utility of the software as a whole, I would rate it a three and a half to four out of five.
    Top Answer:I would like the ability to do more real-time type updates instead of batch-oriented updates.
    Top Answer:I rate Microsoft Parallel Data Warehouse eight out of 10. I would recommend it.
    Top Answer:I like Data Warehouse's data integrity features. Data integrity is what databases are made for as opposed to spreadsheets.
    Ranking
    6th
    out of 32 in Data Warehouse
    Views
    6,081
    Comparisons
    5,301
    Reviews
    9
    Average Words per Review
    502
    Rating
    8.0
    14th
    out of 32 in Data Warehouse
    Views
    2,024
    Comparisons
    1,614
    Reviews
    3
    Average Words per Review
    334
    Rating
    7.3
    Comparisons
    Also Known As
    Microsoft PDW, SQL Server Data Warehouse, Microsoft SQL Server Parallel Data Warehouse, MS Parallel Data Warehouse
    Learn More
    Overview
    The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

    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.

    Offer
    Learn more about Apache Hadoop
    Learn more about Microsoft Parallel Data Warehouse
    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
    Top Industries
    REVIEWERS
    Financial Services Firm46%
    Comms Service Provider31%
    Hospitality Company8%
    Consumer Goods Company8%
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company18%
    Comms Service Provider10%
    Manufacturing Company6%
    REVIEWERS
    Computer Software Company21%
    Pharma/Biotech Company14%
    Hospitality Company14%
    Comms Service Provider7%
    VISITORS READING REVIEWS
    Computer Software Company21%
    Financial Services Firm12%
    Comms Service Provider11%
    Insurance Company9%
    Company Size
    REVIEWERS
    Small Business32%
    Midsize Enterprise29%
    Large Enterprise39%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise71%
    REVIEWERS
    Small Business31%
    Midsize Enterprise12%
    Large Enterprise58%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise15%
    Large Enterprise66%
    Buyer's Guide
    Apache Hadoop vs. Microsoft Parallel Data Warehouse
    November 2022
    Find out what your peers are saying about Apache Hadoop vs. Microsoft Parallel Data Warehouse and other solutions. Updated: November 2022.
    657,849 professionals have used our research since 2012.

    Apache Hadoop is ranked 6th in Data Warehouse with 9 reviews while Microsoft Parallel Data Warehouse is ranked 14th in Data Warehouse with 3 reviews. Apache Hadoop is rated 8.0, while Microsoft Parallel Data Warehouse is rated 7.4. The top reviewer of Apache Hadoop writes "Has good processing power and speed and is capable of handling large volumes of data and doing online analysis". On the other hand, the top reviewer of Microsoft Parallel Data Warehouse writes "The admission tools, BI tools, and data warehousing tools are great". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Snowflake, Azure Data Factory, Oracle Exadata and Teradata, whereas Microsoft Parallel Data Warehouse is most compared with Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake, SAP BW4HANA and Vertica. See our Apache Hadoop vs. Microsoft Parallel Data Warehouse report.

    See our list of best Data Warehouse vendors and best Cloud Data Warehouse vendors.

    We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.