Compare Apache Hadoop vs. VMware Tanzu Greenplum

Apache Hadoop is ranked 3rd in Data Warehouse with 8 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 4 reviews. Apache Hadoop is rated 7.6, while VMware Tanzu Greenplum is rated 7.2. The top reviewer of Apache Hadoop writes "An inexpensive and flexible suite that helps users integrate varied legacy systems". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "A scalable and future-proof solution for data warehousing". Apache Hadoop is most compared with Snowflake, Oracle Exadata, Vertica, Teradata and SAP IQ, whereas VMware Tanzu Greenplum is most compared with Amazon Redshift, Teradata, Snowflake, Vertica and Oracle Exadata. See our Apache Hadoop vs. VMware Tanzu Greenplum report.
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Most Helpful Review
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The most valuable features are powerful tools for ingestion, as data is in multiple systems.The most valuable feature is the database.It's good for storing historical data and handling analytics on a huge amount of data.The ability to add multiple nodes without any restriction is the solution's most valuable aspect.What comes with the standard setup is what we mostly use, but Ambari is the most important.The best thing about this solution is that it is very powerful and very cheap.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.Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.

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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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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.It would be good to have more advanced analytics tools.The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.The upgrade path should be improved because it is not as easy as it should be.We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.I would like to see more direct integration of visualization applications.

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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
This is a low cost and powerful solution.

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

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425,794 professionals have used our research since 2012.
out of 32 in Data Warehouse
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out of 32 in Data Warehouse
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Popular Comparisons
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Also Known As
Greenplum, Pivotal Greenplum
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.

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.

Learn more about Apache Hadoop
Learn more about VMware Tanzu Greenplum
Sample Customers
Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web LabGeneral Electric, Conversant, China CITIC Bank, Aridhia, Purdue University
Top Industries
Software R&D Company40%
Comms Service Provider15%
Financial Services Firm10%
Media Company8%
Financial Services Firm44%
Comms Service Provider19%
Marketing Services Firm19%
Pharma/Biotech Company6%
Software R&D Company40%
Comms Service Provider14%
Financial Services Firm10%
Media Company5%
Find out what your peers are saying about Apache Hadoop vs. VMware Tanzu Greenplum and other solutions. Updated: July 2020.
425,794 professionals have used our research since 2012.

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