Compare Apache Hadoop vs. VMware Tanzu Greenplum

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Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. VMware Tanzu Greenplum and other solutions. Updated: March 2021.
475,291 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
"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.""The best thing about this solution is that it is very powerful and very cheap.""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."

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"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.""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.""The most valuable feature for us is horizontal scaling.""Pivotal Greenplum's shared-nothing architecture.""The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable.""It's super easy to deploy and it also supports different languages and analytics."

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Cons
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.""The upgrade path should be improved because it is not as easy as it should be.""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."

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"The installation is difficult and should be made easier.""Some integration with other platforms like design tools, and ETL development tools, that will enable some advanced functionality, like fully down processing, etc.""I saw some limitation with respect to the column store, and removing this would be an improvement.""Initial setup is a little complex. It took around two weeks to deploy.""The initial setup is somewhat complex and the out-of-the-box configuration requires optimization.""They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."

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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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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: It's super easy to deploy and it also supports different languages and analytics.
Top Answer: As of now, I honestly don't see any room for improvement. I am very happy with this solution, It's helped me a lot. They should add more analytics. Their documentation could also be improved so that I… more »
Top Answer: Greenplum is a distributed database that we used for data warehousing.
Ranking
3rd
out of 30 in Data Warehouse
Views
11,052
Comparisons
9,116
Reviews
10
Average Words per Review
426
Rating
7.7
8th
out of 30 in Data Warehouse
Views
9,275
Comparisons
6,312
Reviews
6
Average Words per Review
388
Rating
8.0
Popular Comparisons
Also Known As
Greenplum, Pivotal Greenplum
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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.

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.

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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
General Electric, Conversant, China CITIC Bank, Aridhia, Purdue University
Top Industries
VISITORS READING REVIEWS
Computer Software Company30%
Comms Service Provider17%
Financial Services Firm13%
Media Company5%
REVIEWERS
Financial Services Firm53%
Marketing Services Firm16%
Comms Service Provider16%
Pharma/Biotech Company5%
VISITORS READING REVIEWS
Computer Software Company35%
Comms Service Provider18%
Financial Services Firm9%
Manufacturing Company6%
Company Size
REVIEWERS
Small Business37%
Midsize Enterprise21%
Large Enterprise42%
REVIEWERS
Small Business21%
Midsize Enterprise17%
Large Enterprise62%
Find out what your peers are saying about Apache Hadoop vs. VMware Tanzu Greenplum and other solutions. Updated: March 2021.
475,291 professionals have used our research since 2012.

Apache Hadoop is ranked 3rd in Data Warehouse with 10 reviews while VMware Tanzu Greenplum is ranked 8th in Data Warehouse with 6 reviews. Apache Hadoop is rated 7.8, while VMware Tanzu Greenplum is rated 8.0. The top reviewer of Apache Hadoop writes "Great micro-partitions, helpful technical support and quite stable". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "Powerful external data integration and parallel load capabilities, with good technical support". Apache Hadoop is most compared with Snowflake, Oracle Exadata, Microsoft Azure Synapse Analytics, Vertica and Teradata, whereas VMware Tanzu Greenplum is most compared with Snowflake, Amazon Redshift, Oracle Exadata, Teradata and Vertica. See our Apache Hadoop vs. VMware Tanzu Greenplum report.

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