Pivotal Greenplum Review

MPP architecture is important to process data in such volume.​ Better integration with big data tech stack is needed.


Valuable Features

MPP (Massive Parallel Processing); processing large amount of data.

Improvements to My Organization

We process billions of rows of data every hour; MPP architecture is important to process data in such volume.

Room for Improvement

  • Better integration with big data tech stack
  • Scalability: for example, system schema (pg_catalog) is one bottleneck for scalability

Use of Solution

I've used it for four to five years.

Scalability Issues

The system has some bottleneck, like system schema; some commands (batch loading) is bottlenecked by master DB architecture

Customer Service and Technical Support

Customer Service:

It's very poor.

Technical Support:

It's very poor.

Previous Solutions

Used Greenplum originally. We have evaluated other products; the final decision is based on ROI

Initial Setup

Products was installed by vendor; all ETL scripts and schema are designed and implemented in-house.

Implementation Team

The product was installed by vendor; all ETL scripts and schema are designed and implemented in-house.

Pricing, Setup Cost and Licensing

Greenplum has an open source version now.

Other Advice

Scalability is one major concern; once data reach certain level; the performance is dropped and much more issues will be triggered, like disk error; out of memory; etc. Be sure that you proper scope your need (including growth) before the decision of system and its size.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Add a Comment
Guest
Sign Up with Email