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: March 2024).
763,955 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges.""As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R.""Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect.""The most valuable features are powerful tools for ingestion, as data is in multiple systems.""The most valuable feature is scalability and the possibility to work with major information and open source capability.""The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics.""​​Data ingestion: It has rapid speed, if Apache Accumulo is used.""I liked that Apache Hadoop was powerful, had a lot of tools, and the fact that it was free and community-developed."

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"The most valuable feature is the business intelligence (BI) part of it.""It handles high volumes of data very well.""The solution's integration is good.""I am very satisfied with the customer service/technical support.""We have complete control over our data.""Microsoft Parallel Data Warehouse integrates beautifully with other Microsoft ecosystem products.""The most valuable features are the performance and usability.""I like Data Warehouse's data integrity features. Data integrity is what databases are made for as opposed to spreadsheets."

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Cons
"Real-time data processing is weak. This solution is very difficult to run and implement.""We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.""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.""It would be good to have more advanced analytics tools.""The upgrade path should be improved because it is not as easy as it should be.""The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks.""The integration with Apache Hadoop with lots of different techniques within your business can be a challenge.""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."

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"This solution would be improved with an option for in-memory data analysis.""The reporting for certain types of data needs to be improved.""The query is slow if we don't optimize it.""They need to incorporate a machine learning engine.""I would like the ability to do more real-time type updates instead of batch-oriented updates.""In the future I would love to see a slightly better automation engine, just for the data integration layer, to make it slightly easier for end-users or junior developers to get involved in incremental updating.""​Concurrent queries are limited to 32, making it more of a data storage mechanism instead of an active DWH solution.""We find the cost of the solution to be a little high."

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Pricing and Cost Advice
  • "Do take into consider that data storage and compute capacity scale differently and hence purchasing a "boxed" / 'all-in-one" solution (software and hardware) might not be the best idea."
  • "​There are no licensing costs involved, hence money is saved on the software infrastructure​."
  • "This is a low cost and powerful solution."
  • "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."
  • "We just use the free version."
  • More Apache Hadoop Pricing and Cost Advice →

  • "I think the program is well-priced compared to the other offerings that are out in the market."
  • "Microsoft has an agreement with the government in our country, so our customers get their licensing costs from the Ministry. Whenever we work with any government, company, or government institute, which is mainly what we are doing, that license comes directly from the Ministry of Technology and Information."
  • "All the features that we use do not require any additional subscription or yearly fees."
  • "Technical support is an additional fee and is expensive."
  • "The solution's pricing is fairly decent for organizations with huge data sizes."
  • "The tool could be expensive if we need to manage a lot of data."
  • "They offer an annual subscription. The pricing depends on the size of the environments."
  • More Microsoft Parallel Data Warehouse Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:It's open-source, so it's very cost-effective.
    Top Answer:The main thing is the lack of community support. If you want to implement a new API or create a new file system, you won't find easy support. And then there's the server issue. You have to create and… more »
    Top Answer:Microsoft Parallel Data Warehouse provides good firewall processing in terms of response time.
    Top Answer:They offer an annual subscription. The pricing depends on the size of the environments.
    Top Answer:Sometimes, the product requires rolling back to its previous version during a software update. This particular area could be enhanced.
    Ranking
    5th
    out of 33 in Data Warehouse
    Views
    2,765
    Comparisons
    2,378
    Reviews
    10
    Average Words per Review
    539
    Rating
    8.0
    8th
    out of 33 in Data Warehouse
    Views
    638
    Comparisons
    516
    Reviews
    12
    Average Words per Review
    379
    Rating
    8.0
    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.

    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 Firm40%
    Comms Service Provider27%
    Hospitality Company7%
    Consumer Goods Company7%
    VISITORS READING REVIEWS
    Financial Services Firm27%
    Computer Software Company10%
    Comms Service Provider6%
    Educational Organization6%
    REVIEWERS
    Computer Software Company18%
    Healthcare Company18%
    Pharma/Biotech Company12%
    Hospitality Company12%
    VISITORS READING REVIEWS
    Computer Software Company21%
    Financial Services Firm18%
    Insurance Company7%
    University6%
    Company Size
    REVIEWERS
    Small Business33%
    Midsize Enterprise24%
    Large Enterprise42%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise10%
    Large Enterprise75%
    REVIEWERS
    Small Business34%
    Midsize Enterprise14%
    Large Enterprise51%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise18%
    Large Enterprise66%
    Buyer's Guide
    Apache Hadoop vs. Microsoft Parallel Data Warehouse
    March 2024
    Find out what your peers are saying about Apache Hadoop vs. Microsoft Parallel Data Warehouse and other solutions. Updated: March 2024.
    763,955 professionals have used our research since 2012.

    Apache Hadoop is ranked 5th in Data Warehouse with 11 reviews while Microsoft Parallel Data Warehouse is ranked 8th in Data Warehouse with 12 reviews. Apache Hadoop is rated 7.8, while Microsoft Parallel Data Warehouse is rated 7.6. 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 "User-friendly UI and good support". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Azure Data Factory, Oracle Exadata, Snowflake and Teradata, whereas Microsoft Parallel Data Warehouse is most compared with Microsoft Azure Synapse Analytics, Oracle Exadata, SAP BW4HANA, Snowflake and VMware Tanzu Greenplum. See our Apache Hadoop vs. Microsoft Parallel Data Warehouse report.

    See our list of best 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.