Compare Apache Hadoop vs. Microsoft Parallel Data Warehouse

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Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. Microsoft Parallel Data Warehouse and other solutions. Updated: July 2021.
524,194 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
"What comes with the standard setup is what we mostly use, but Ambari is the most important.""The ability to add multiple nodes without any restriction is the solution's most valuable aspect.""It's good for storing historical data and handling analytics on a huge amount of data.""The most valuable feature is the database.""The most valuable features are powerful tools for ingestion, as data is in multiple systems.""The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so.""The performance is pretty good.""Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."

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"I am very satisfied with the customer service/technical support.""The most valuable features are the performance and usability.""The most valuable feature for me is querying.""One of the most important features is the ease of using MS SQL.""The most valuable feature of this solution is performance.""The data transmissions between the data models is the most valuable feature.""The most valuable feature is the business intelligence (BI) part of it.""We are able to monitor daily jobs, so if there is anything that needs to be done then we can do it."

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Cons
"In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.""There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.""The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.""It would be good to have more advanced analytics tools.""It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake.""The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning.""The solution is very expensive.""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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"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.""More tools to help designers should be included.""I would like to see better visualization features.""I think that the error messages need to be made more specific.""The reporting for certain types of data needs to be improved.""This solution would be improved with an option for in-memory data analysis.""If the database is large with a lot of columns then it is difficult to clean the data.""I would like the tool to support different operating systems."

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Pricing and Cost Advice
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"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."

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Questions from the Community
Top Answer: Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability.
Top Answer: I don't have any concerns because each part of Hadoop has its use cases. To date, I haven't implemented a huge product or project using Hadoop, but on the level of POCs, it's fine. The community of… more »
Top Answer: I'd like to connect something like ES that I can use on objects on our SQL Server. We'd like to be able to understand how to export data to something like Semantic Technologies or like Graph DB. Some… more »
Ranking
7th
out of 30 in Data Warehouse
Views
8,838
Comparisons
7,187
Reviews
8
Average Words per Review
429
Rating
7.5
6th
out of 30 in Data Warehouse
Views
2,515
Comparisons
1,859
Reviews
11
Average Words per Review
486
Rating
7.6
Popular Comparisons
Also Known As
Microsoft PDW, SQL Server Data Warehouse, Microsoft SQL Server Parallel Data Warehouse, MS Parallel Data Warehouse, MS Parallel Data Warehouse, MS Parallel Data Warehouse
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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.

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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
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider17%
Financial Services Firm13%
Energy/Utilities Company5%
REVIEWERS
Computer Software Company27%
Pharma/Biotech Company18%
Comms Service Provider9%
Hospitality Company9%
VISITORS READING REVIEWS
Computer Software Company23%
Comms Service Provider17%
Financial Services Firm11%
Insurance Company7%
Company Size
REVIEWERS
Small Business37%
Midsize Enterprise21%
Large Enterprise42%
REVIEWERS
Small Business32%
Midsize Enterprise18%
Large Enterprise50%
Find out what your peers are saying about Apache Hadoop vs. Microsoft Parallel Data Warehouse and other solutions. Updated: July 2021.
524,194 professionals have used our research since 2012.

Apache Hadoop is ranked 7th in Data Warehouse with 8 reviews while Microsoft Parallel Data Warehouse is ranked 6th in Data Warehouse with 11 reviews. Apache Hadoop is rated 7.6, while Microsoft Parallel Data Warehouse is rated 7.6. The top reviewer of Apache Hadoop writes "Great micro-partitions, helpful technical support and quite stable". On the other hand, the top reviewer of Microsoft Parallel Data Warehouse writes "Collects data through SSIS packages from different sources and puts them all in one data repository". Apache Hadoop is most compared with Snowflake, Microsoft Azure Synapse Analytics, VMware Tanzu Greenplum, Oracle Exadata and Microsoft Analytics Platform System, whereas Microsoft Parallel Data Warehouse is most compared with Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake, SAP BW4HANA and Amazon Redshift. 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.