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."Data ingestion: It has rapid speed, if Apache Accumulo is used."
"The tool's stability is good."
"One valuable feature is that we can download data."
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"The most valuable feature is scalability and the possibility to work with major information and open source capability."
"Hadoop is extensible — it's elastic."
"Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial."
"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."
"It is not a pricey product compared to other data warehouse solutions."
"It has allowed fast daily loads and analysis of millions of rows of data, which eventually moved to near real-time."
"Microsoft Parallel Data Warehouse integrates beautifully with other Microsoft ecosystem products."
"We can store the data in a data lake for a very low cost."
"The UI is very simple and functional for my clients, most of the clients that use the solution are not experts."
"Microsoft Parallel Data Warehouse provides good firewall processing in terms of response time."
"Data collection and reporting are valuable features of the solution."
"I like Data Warehouse's data integrity features. Data integrity is what databases are made for as opposed to spreadsheets."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"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."
"The stability of the solution needs improvement."
"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 key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks."
"Hadoop's security could be better."
"The upgrade path should be improved because it is not as easy as it should be."
"The only issue with the product is that the process is very slow when we have a huge amount of data."
"Some compatibility issues occur during deployment, so we need to build the product from scratch for some features."
"I would like the tool to support different operating systems."
"They need to incorporate a machine learning engine."
"It could offer more development across the solution."
"I would like to see better visualization features."
"The product does not have all of the features that the native products have."
"The solution is expensive and has room for improvement."
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Apache Hadoop is ranked 5th in Data Warehouse with 32 reviews while Microsoft Parallel Data Warehouse is ranked 8th in Data Warehouse with 32 reviews. Apache Hadoop is rated 7.8, while Microsoft Parallel Data Warehouse is rated 7.6. The top reviewer of Apache Hadoop writes "A file system for data collection that contains needed information and files". On the other hand, the top reviewer of Microsoft Parallel Data Warehouse writes "An easy to setup tool that allows its users to write stored procedure, making it a scalable product". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas Microsoft Parallel Data Warehouse is most compared with Microsoft Azure Synapse Analytics, Oracle Exadata, SAP BW4HANA, VMware Tanzu Greenplum and Snowflake. See our Apache Hadoop vs. Microsoft Parallel Data Warehouse report.
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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.
