VMware Tanzu Data Solutions Reviews

I've found that the database warehouse, data compression, and ETL to be the most valuable features for us.

Loading batch data has really improved the efficiency of our organization.

I'd like so see better scaling, better performance from in-memory databases, and a higher compression rate. We have been facing some performance issue when doing batch loading with optimizer the scaling does works fine. They are working on having optimization techniques which made me write room for improvement.

I've used it for over two years. I have been working very closely with the EMC folks.

Yes, at times, but it depends on your modeling and data retrieval.

It's been stable for us.

Its scalability needs to be improved.

I would rate technical support as good and there is not much technical expertise at the start of the SR.

We tried other MPP’s.

It was complex, but there was a change in the setup.

We got support from the vendor at the start.

If you want to implement this product, you would need to scale your product well before trying to implement.

Strong integration with Greenplum Servers.

Loading data to Greenplum server after batch processing.

6 months

Yes, issues with HA and issues in syncing with the Greenplum.

You cannot expect a split second response.

On a scale of 10 I would rate it as 7

On a scale of 10 I would rate it as 8

This has just been used for POC period not for a regular use.

Initial setup works absolutely well. We were using with only Greenplum.

I've found that the data compression and ETL are the most valuable features for us.

In 4.3.8.1 Pivotal confirmed that even restoring schema level backup is possible from a DB backup.

- restoring schema from a DB level backup has been tested and working fine .

ORCA - the Pivotal Optimizer does a good query plan but does not works with all business logics. This needs to be tested based on your requirement.

Loading batch data has really improved the efficiency of our organization.

Running Extracts has drastically improved the timings. Being MPP which is a bulk operator - we were able to do 1.5 million calculation in 15 minutes.

Scaling of the solution needs to be improved.

HD connection is available where as, not to any file system.

Connecting Greenplum with Gemfire(In-Memory) to load, sync, and reconcile data would be really valuable.

I've used it for nearly for 3 years

We had deployment issues after installing new patches. Every new patches has some or other business hit where the release notes needs to be reviewed.

It's been stable for us.

They have a quick turn around but to dig into the actual information takes time, based on the Severity.

First level of technical support would not be that effective (based on own observation).

We were using Sybase and handling massive data, bulk operation was not possible.

It was simple.

Append Only tables, data compression and bulk load and extraction using External Tables are very valuable features for us.

We have improved our quarterly statements turnaround dramatically and could sustain for increasing data.

With the ORCA optimizer the earlier Append-Only feature has been upgraded to Append-Optimized where now we can update the data on earlier Append-Only tables just like any other heap tables. But I found this has increased the time taken for Vacuum Analyze operation on these tables like from 10 mins to 1 hr + (on large tables). In our case we don't need an update on our Append Only tables and hence this became a drawback. VA on Append-Optimized tables need to be improved.

Backup & Restore performance need to be improved.

ORCA optimizer when turned on is not showing consistency. Some workloads shows improved performance and some workloads became very slow. This need to be improved for consistency.

I have used it for about 4 years now.

Pre ORCA version was stable. ORCA release is not stable. Some workloads slowed down with new release even when the new optimizer is not turned ON.

Tech support is average. They lack information about new features in the new releases and the possible impact of them.

Earlier we were using OLTP based RDBMS solution. We realized we needed a OLAP solution and also something that can scale horizontally.

The most valuable feature of Greenplum is the Massively Parallel Processing (MPP).

With this solution, we've reduced load on the OLTP systems.

The fact GreenPlum is using an older version of Postgres means developers coming from other products will find many missing features in PostgreSQL, features which you would assume are standard.

Greenplum is based on Postgres 8.2.15 which was released in 2009. While the SQL syntax and functionality has continued to evolve in other platforms in the ensuing years it appears to have stagnated in Greenplum.

We haven't had any issues with deployment.

It's been stable for us.

It's scaled for our needs.

The community around GreenPlum is very small, making it difficult to learn from others experience via forums or blog posts.

We can integrate the Hadoop with DCA V2. This will be huge development in the big data technologies.

It increased the read/write process because of it MPP architecture.

EMC already developed DCA V3, But if the hardware is little stable, I prefer DCA V2.

I am from a support background, and have used this on multiple accounts, for the last four years.

There have been no issues with the deployment.

Hardware failure is a concern.

We have had no issues scaling it for our needs.

Technical support is excellent.

I know many customers are migrating from Oracle to Greenplum due to its faster processing.

It is straightforward,open source system.

Better chose EMC to perform the implementation. More over, it is not complex and we can do it easily in our environment with a little knowledge.

Greenplum is an opensource system, but they do charge for support.

Installation is very simple, make sure to set the configuration values based on the requirement.

The most valuable features are MPP and that it's polymorphic.

We have a set of workflow flows that takes 10 hours in Oracle Exadata, now it takes 4 hours with EMC Greenplum.

It doesn't work as efficiently as we'd like because it requires more segment node capacity (size, RAM, CPU) than we currently have.

I've used it since 2013.

At the moment, we don't have any issues with deployment.

At the moment, we don't have any issues with stability.

At the moment, we don't have any issues with scalability.

8/10

8/10

Yes, we have Oracle Exadata.

The initial setup is straightforward. It was simple for our personnel to install on the Unix system and network.

It's a good product when comparing the price to its functionalities.

You should consider the capacity of your data before you buy it.

• Parallel Processing and Data Distribution based architecture.
• HA built-in with mirroring.
• Highly configurable and lots of tuning features.

• This has helped us bring down our end-to-end EDW load time to 1/3 the time.
• It has enabled faster and efficient data analysis.
• Scalable environment without adding too much cost.

• It needs a much more robust and user friendly monitoring and management front-end tool.
• More stability and auto-recovery with the segments.
• Report generations on system health and recommendations.

I've used it for two years.

Up to now, we've had no issues with deployment.

Up to now, we've had no issues with stability.

Up to now, we've had no issues with scalability.

The response is fairly good but would like more support from the R&D on more complex issues. Also, they need to ensure there are logs that can be used without causing any downtimes to the system for any case analysis.

It's a highly efficient and faster DB with lot of features at much less cost to that of other MPP DB’s evaluated.

It was complex as we have to code convert everything into GP functions so as to best be able to use the GP parallel. Pushdown feature was not available via Informatica. The initial parameter setup took quite some time to test to get the sweet spot for performance.

In-house with vendor support.

Make sure you have the designs, approaches and architecture in place before kicking of the implementation. Its best to have someone involved with prior migration experience.