VMware Tanzu Greenplum (Hourly)
Pivotal Software Inc. | v6.8.0 - Rocky 8Linux/Unix, CentOS 7.9 - 64-bit Amazon Machine Image (AMI)
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Robust, Reliable and Multiple Parallel Processing database that checks limitation of PostgreSQL
What do you like best about the product?
Greenplum is fast because of MPP architecture. Segment fail-over and recovery is very good as system automatically fails over to the mirror segment if a primary copy becomes unavailable.
Greenplum is reliable.
Greenplum is reliable.
What do you dislike about the product?
There is no option to create procedures like we have in OracleDB. We do have Functions but procedures would make a lot of requirements easier to achieve.
Also, I feel, the querying interface can be much better.
Also, I feel, the querying interface can be much better.
What problems is the product solving and how is that benefiting you?
We had Data Lake created in Greenplum. working as a senior data engineer, we dumped data from various sources viz. oracle, sql server, csv logs to Greenplum DB. It is easily configurable with HVR tool as well. while the data could be used by data engineers for further consumption and creating functions easily, it was equally easy to work with for Data Scientists.
Recommendations to others considering the product:
Greenplum is a perfect database if you are looking for a robust, reliable solution with multiple parallel processing data warehouse / data lake. It will support real time integration with HVR/ golden gate. While visualizing the data, it supported equally good to Tableu/Sisense.
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One stop solution for OLAP systems
What do you like best about the product?
It is highly scalable and runs on both public and ptivate clouds
What do you dislike about the product?
As of now I did find any issues. It would make sense to implement JDBC’s available for different systems or tools
What problems is the product solving and how is that benefiting you?
Scalability issues for OLAP systems which is not addressed in RDBMS
As greenplum developer it's been 3+ years and it's fantastic for massive data
What do you like best about the product?
Best helpful is distributed system. To load huge data it's just simply superb
What do you dislike about the product?
Update statement wil work Hinder the fastness of GP
What problems is the product solving and how is that benefiting you?
Processing high volume finance data
Recommendations to others considering the product:
Yes. I wil advice others to consider
Great MPP Experience
What do you like best about the product?
- I have been working on Greenplum database from last 3 years. This is the first ever database for me with Massive Parallel Processing architecture to explore, learn and work on.
- Compared to my previous experience with other traditional RDBMS, Greenplum's MPP with distribution of data and processing the work load across multiple segments and hosts was quite interesting and was a great learning.
- In my opinion, we dont really have to go for Big data technologies like Hadoop to deal with the big data.
- Querying the database using postgresql is quite easy to use for users with basic sql knowledge
- Compared to my previous experience with other traditional RDBMS, Greenplum's MPP with distribution of data and processing the work load across multiple segments and hosts was quite interesting and was a great learning.
- In my opinion, we dont really have to go for Big data technologies like Hadoop to deal with the big data.
- Querying the database using postgresql is quite easy to use for users with basic sql knowledge
What do you dislike about the product?
We have faced some issues in the beginning 3 years ago because of the on premise commodity hardware. But it is good to hear that Greenplum can now run on cloud environments as well.
What problems is the product solving and how is that benefiting you?
We have a huge historical transnational data in PB and a daily incoming load from transnational systems in GB. Greenplum's MPP is the right solution for handling such big data for our requirements
- The data distribution and parallel processing by several segments made it really easy to get fast results for our queries against database
- The concept of Resource Queues was quite useful to allocate the resources to queries based on business requirements
- The data distribution and parallel processing by several segments made it really easy to get fast results for our queries against database
- The concept of Resource Queues was quite useful to allocate the resources to queries based on business requirements
Greenplum Review
What do you like best about the product?
Performance at scale. Speed. Easy set-up initially. It was a great platform in the beginning of the Big Data movement.
What do you dislike about the product?
Complexity, hard to tune. Need for specialized support people. Not many people are available that are trained in operating the product. Cloud implementation is sub-optimal. Has NOT enhanced the architecture. It has fallen behind other solutions.
What problems is the product solving and how is that benefiting you?
Ability to ingest several different data types from many different sources. Query response times and access to all of our data assets
Recommendations to others considering the product:
Become better at deploying in the cloud and supporting Cloud-native solutions
It's powerful feature of supporting multiple native scripting api
What do you like best about the product?
Native scripting Api, and inbuilt array data type
What do you dislike about the product?
Some sql native fiction like unique is bit slow what I observed
What problems is the product solving and how is that benefiting you?
Used mostly financial data storing, it's data clustering is too good.
Recommendations to others considering the product:
It's the most flexible data base must know tech in data rich domain.
GP has Artificial Intelligence capability and promising
What do you like best about the product?
Pivotal Greenplum has AI capability and can execute point queries and can handle workloads in day to day Organizations activities aside that it is also scalable.
What do you dislike about the product?
I think there is no dislike it just need the resources or the required architecture for the Pivotal GP to maximize its features and technology.
What problems is the product solving and how is that benefiting you?
Using REST API and Advance Analytics. MADlib with Greenplum provides multi-node, multi-GPU and deep learning capabilities. It has model versioning for fast automation, also have the capability to push models from training to production thru REST API. Easy deployment or re-coding analytical models.
Recommendations to others considering the product:
If you are looking for Advance analytics, fast queries, AI capable, scalability, deployment to Public or Private Cloud or on premise. This is a great database technology you can have. It has more features than I thought.
It helps me a lot for OLAP transaction in banking industry.
What do you like best about the product?
With my experience to greenplum pivotal. It helps a lot to join the required entity for our main table and also helps to quickly extract the necessary information for our enterprise application for OLAP transaction. With pivotal it helps me in joining the different entity to the main clause.
What do you dislike about the product?
Did not felt any issues yet. So no dislikes.
What problems is the product solving and how is that benefiting you?
Before pivotal we were really fed up to make many joining in the tables it causes performance issue during translation loading.
Consultant for GreenPlum
What do you like best about the product?
Information_Schema --> Metadata maintenance. Partition Exchange.
What do you dislike about the product?
Utilities. Unlike many other MPP , GP has no varieties of utilities for ETL.
What problems is the product solving and how is that benefiting you?
Partition exchange one of good option on Huge data access/retrieval/manipulate
Excellent mpp tool and beat for data warehouse and ingestion through big data
What do you like best about the product?
1)UI and scripting options for greennplum.
2) Greenplum command center and alert with out of memory, max connections and segment failure helps the Dba to solve the issues quickly.
2) Greenplum command center and alert with out of memory, max connections and segment failure helps the Dba to solve the issues quickly.
What do you dislike about the product?
Some inbuilt maintenance like vaccum should be there. And some new issues comes up after upgrade so more testing from pivotal is required.
What problems is the product solving and how is that benefiting you?
Have solved indexing issue in greenplum and backups option as well. Have solved segment failures issue, Max connections and Out of memory and also solved fragmentation issues
Recommendations to others considering the product:
Good mpp technology can always go ahead with greenplum for ingestion and big data use cases
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