Compare Apache Hadoop vs. Pivotal Greenplum

Apache Hadoop is ranked 4th in Data Warehouse with 11 reviews while Pivotal Greenplum is ranked 7th in Data Warehouse with 5 reviews. Apache Hadoop is rated 7.6, while Pivotal Greenplum is rated 7.4. The top reviewer of Apache Hadoop writes "We are able to ingest huge volumes/varieties of data, but it needs a data visualization tool and enhanced Ambari for management". On the other hand, the top reviewer of Pivotal Greenplum writes "Handles complex queries and report production efficiently, integrates with Hadoop". Apache Hadoop is most compared with Snowflake, Pivotal Greenplum and Oracle Exadata, whereas Pivotal Greenplum is most compared with Apache Hadoop, Amazon Redshift and Teradata. See our Apache Hadoop vs. Pivotal Greenplum report.
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Apache Hadoop Logo
12,931 views|11,053 comparisons
Pivotal Greenplum Logo
12,022 views|8,546 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. Pivotal Greenplum and other solutions. Updated: January 2020.
398,890 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 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.It's one of the fastest databases in the market. It's easy to use. From a maintenance perspective it's a good product. The segmentation, or architecture of the product is different than other databases such as Oracle. So even in 10 years, the data distribution for such segments will not affect other segments. The query performance of the product, for complex queries, is very good. It has good integration with Hadoop.

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Cons
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.Implementation takes a long time.One of the disadvantages, not a disadvantage with the product itself, but overall, is the expertise in the marketplace. It's not easy to find a Greenplum administrator in the market, compared to other products such as Oracle.they need to interact more with customers. They need to explain the features, especially when there are new releases of Greenplum. I know just from information I've found that it has other features, it can be used to for analytics, for integration with Big Data, Hadoop. They need to focus on this part with the customer.They need to enhance integration with other Big Data products... to integrate with Big Data platforms, and to open a bi-directional connection between Greenplum and Big Data.

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Pricing and Cost Advice
This is a low cost and powerful solution.​There are no licensing costs involved, hence money is saved on the software infrastructure​.

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We are using the open-source version of this solution.Pricing is good compared to other products. It's fine.

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Ranking
4th
out of 30 in Data Warehouse
Views
12,931
Comparisons
11,053
Reviews
10
Average Words per Review
427
Avg. Rating
7.5
7th
out of 30 in Data Warehouse
Views
12,022
Comparisons
8,546
Reviews
5
Average Words per Review
433
Avg. Rating
7.4
Top Comparisons
Compared 33% of the time.
Compared 26% of the time.
Compared 13% of the time.
Compared 38% of the time.
Compared 13% of the time.
Compared 11% of the time.
Also Known As
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.

Advanced analytics meets traditional business intelligence with Pivotal Greenplum, the world’s first fully-featured, multi-cloud, massively parallel processing (MPP) data platform based on the open source Greenplum Database. Pivotal Greenplum provides comprehensive and integrated analytics on multi-structured data. Powered by one of the world’s most advanced cost-based query optimizers, Pivotal Greenplum delivers unmatched analytical query performance on massive volumes of data.

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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 LabGeneral Electric, Conversant, China CITIC Bank, Aridhia, Purdue University
Top Industries
VISITORS READING REVIEWS
Software R&D Company35%
Financial Services Firm15%
Comms Service Provider14%
Government8%
REVIEWERS
Financial Services Firm44%
Marketing Services Firm19%
Comms Service Provider19%
Retailer6%
VISITORS READING REVIEWS
Software R&D Company31%
Financial Services Firm18%
Comms Service Provider13%
Insurance Company5%
Find out what your peers are saying about Apache Hadoop vs. Pivotal Greenplum and other solutions. Updated: January 2020.
398,890 professionals have used our research since 2012.
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