Compare Microsoft Parallel Data Warehouse vs. VMware Tanzu Greenplum

Microsoft Parallel Data Warehouse is ranked 7th in Data Warehouse with 9 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 4 reviews. Microsoft Parallel Data Warehouse is rated 7.6, while VMware Tanzu Greenplum is rated 7.2. 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". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "A scalable and future-proof solution for data warehousing". Microsoft Parallel Data Warehouse is most compared with Oracle Exadata, Microsoft Azure SQL Data Warehouse and Snowflake, whereas VMware Tanzu Greenplum is most compared with Apache Hadoop, Amazon Redshift and Teradata. See our Microsoft Parallel Data Warehouse vs. VMware Tanzu Greenplum report.
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Most Helpful Review
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Find out what your peers are saying about Microsoft Parallel Data Warehouse vs. VMware Tanzu Greenplum and other solutions. Updated: May 2020.
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
Collecting the data through SSIS packages from different sources and putting them all in one data repository is the most powerful thing. While others have this feature, they don't have the simplicity or ease of use when getting a resource and knowing everything about it.We are able to monitor daily jobs, so if there is anything that needs to be done then we can do it.The most valuable feature is the business intelligence (BI) part of it.The data transmissions between the data models is the most valuable feature.The most valuable feature of this solution is performance.One of the most important features is the ease of using MS SQL.The most valuable feature for me is querying.The most valuable features are the performance and usability.

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Pivotal Greenplum's shared-nothing architecture.The most valuable feature for us is horizontal scaling.Scalability is simple because it's an MPP database. If you need more processing power or you need more storage, you just add a few more nodes in the cluster. It works on common commodity hardware. You can use any type of server. You don't need to have proprietary hardware. It's fairly flexible.We chose Greenplum because of the architecture in terms of clustering databases and being able to have, or at least utilize the resources that are sitting on a database.

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Cons
I would like the tool to support different operating systems.If the database is large with a lot of columns then it is difficult to clean the data.This solution would be improved with an option for in-memory data analysis.The reporting for certain types of data needs to be improved.I think that the error messages need to be made more specific.I would like to see better visualization features.More tools to help designers should be included.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.

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Initial setup is a little complex. It took around two weeks to deploy.I saw some limitation with respect to the column store, and removing this would be an improvement.Some integration with other platforms like design tools, and ETL development tools, that will enable some advanced functionality, like fully down processing, etc.The installation is difficult and should be made easier.

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Pricing and Cost Advice
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.I think the program is well-priced compared to the other offerings that are out in the market.

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We are using the open-source version of this solution.

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Ranking
7th
out of 32 in Data Warehouse
Views
2,674
Comparisons
1,929
Reviews
9
Average Words per Review
463
Avg. Rating
7.7
9th
out of 32 in Data Warehouse
Views
11,870
Comparisons
8,317
Reviews
4
Average Words per Review
352
Avg. Rating
7.3
Top Comparisons
Compared 12% of the time.
Also Known As
Microsoft PDW, SQL Server Data Warehouse, Microsoft SQL Server Parallel Data WarehouseGreenplum, Pivotal Greenplum
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Microsoft
VMware
Overview

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.

Parallel Postgres for enterprise analytics at scale
With improved transaction processing capability and support for streaming ingest, Greenplum can address workloads across a spectrum of analytic and operational contexts, from traditional business intelligence to deep learning.

Offer
Learn more about Microsoft Parallel Data Warehouse
Learn more about VMware Tanzu Greenplum
Sample Customers
Auckland Transport, Erste Bank Group, Urban Software Institute, NJVC, Sheraton Hotels and Resorts, Tata Steel EuropeGeneral Electric, Conversant, China CITIC Bank, Aridhia, Purdue University
Top Industries
REVIEWERS
Software R&D Company30%
Comms Service Provider10%
Healthcare Company10%
Hospitality Company10%
VISITORS READING REVIEWS
Software R&D Company32%
Comms Service Provider9%
Financial Services Firm9%
Wholesaler/Distributor7%
REVIEWERS
Financial Services Firm44%
Comms Service Provider19%
Marketing Services Firm19%
Pharma/Biotech Company6%
VISITORS READING REVIEWS
Software R&D Company37%
Comms Service Provider14%
Financial Services Firm12%
Retailer5%
Find out what your peers are saying about Microsoft Parallel Data Warehouse vs. VMware Tanzu Greenplum and other solutions. Updated: May 2020.
419,360 professionals have used our research since 2012.
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