OpenText Analytics Database (Vertica) Reviews

Analytical features are amazing, the integration is wonderful.

It has helped us escalate, we need information almost real-time.

Documentation, there are functions that are not documented. UDF SDK, I'd like to see a step by step simulator example in a manual. The read-me code is good, however, an example would be great for starters.

3 years

Yes, cluster migration takes time.

Yes, in data streaming ROS containers is a pain to work with.

Not yet.

It is good, they answer in good time. There are times that they really don't come with a proper answer.

Decent.

Yes, regular relational databases. We switched for scalability reasons.

In-house.

Yes, Netezza.

I like the new things they are introducing. I want to see more with Python.

Fast inserts, queries way faster than in SQL Server.

Certain research which was unattainable beforehand, now is in reach.

Some GUI Tools out of the box, better python integration. I would love to see some nice query engine, tooled specifically to Vertica extensions of SQL (with IntelliSense). We currently use TOAD, but it has a lot of bugs and does not provide full support of all Vertica features. For example with the "copy to" command it is buggy - extremely hard to debug.

Other issues would be better query plan display and better management tools like command line admin tools. (MC would probably solve a bit here, I saw a demo on the conference). We have several avid Python users in the company, but this how maybe lower priority after I reviewed my conference materials and found that Vertica now has native Python driver.

2 years.

I was acting as my own DBA for a while, so a lot of hurdles before. But things are getting easier as more people in the company bought into the solution. I also got HP Training in house. (Thanks Herb!)

We can scale a lot, wish it was a bit more affordable.

It complements our SQL Server solution.

Own efforts.

Switch to per node from per TB.

Scale-out, analytical functions, ML.

We are an HP partner. A SQL-based compute platform like Vertica enables far less human overhead in operations and analytics.

More ML, both data prep, models, evaluation and workflow.  Improved support for deep analytics/ predictive modelling with machine learning algorithms. This area of analytics need a stack of functionality in order to support the scenario. The needed functionality includes:

• Data preparation. Scaling, centering, removing skewness, gap filling, pivoting, feature selection and feature generation
• Algorithms/models. Non-linear models in general. More specifically, penalized models, tree/rule-based models (incl. ensambles), SVM, MARS, Neural networks, K-nearest neighbours, Naïve bayes, etc.
• Support the concept of a “data processing pipeline” with data prep. + model. One would typically use “a pipeline” as the overall logical unit used to produce predictions/scoring.
• Automatic model evaluation/tuning. With algorithms requiring tuning, support for automated testing of different settings/tuning parameters is very useful. Should include (k fold) cross validation and bootstrap for model evaluation
• Some sort of hooks to use external models in a pipeline i.e. data prep in Vertica + model from Spark/R. 
• Parity functionality for the Java SDK compared to C++. Today the C++ SDK is the most feature rich. The request is to bring (and keep) the Java SDK up to feature parity with C++.
• Streaming data and notifications/alerts. Streaming data is starting to get well supported with the Kafka integration. Now we just need a hook to issue notifications on streaming data. That is, running some sort of evaluation on incoming records (as they arrive to the Vertica tables) and possibly raising a notification.

Two years.

No, not really.

No.

No.

Postgresql, MySQL, SQL Server. Switched because of scalability and reliability, analytics functionality. V being a better engineered product.

Straightforward. Good docs helped a lot.

Its reasonably priced for non-trivial data problems.

Yes, Hadoop / Spark, SQL Server.

See additional functionality above.

Super-fast aggregated results from massive data.

We can process vast amounts of data, fast and with a high degree of reliability.

Better feedback from installation. I would like to see more meaningful errors returned and more graceful handling of those. Thankfully, we don't often hit error conditions.

4 years.

No

No

Depends on the environment. Generally pretty good. If you have a large catalog, you can get timeouts adding nodes. Large catalog issues have been dealt with it recent releases so this should make scaling up even more robust.

Excellent. It can take some time to get to the right people but generally our issues are all addressed in an acceptable timeframe.

Greenplum. It was less stable.

Vertica is very robust and recovers predictably from unexpected infrastructure failures.

Great overall solution.

Query Performance.

More analytical functions. Optimization around DML operations such that we be able to use it more. I understand that Vertica is not meant for that, but I would really love it if it has capability to support procedural processing.

Optimization around DML refers to fast DELETE and UPDATE statement such that we can leverage Vertica around those operations. I do understand that Vertica is not meant as an OLTP system, neither I'm asking to have it similar but if DML operations can be optimized, that would be admirable.

Regarding, my comment on the capability to support procedural processing - Vertica as of now is mainly used via SQL only. If we have to use any procedure based operation, we do it via User Defined Functions. If within Vertica itself, we have the capability to create & execute procedure similar to that for functions, it would be a plus. Again, I do understand that this may be against the architecture of Vertica but if there is anything that can be revised to get these supported, that would be preferred.

3+ years

Initially yes, but now we're used to it. Challenges do come up but it's all well understood.

We had confronted couple of issues during Vertica upgrades for which we do align with your support group.

Replication

Replication and Node recovery in 8.0.

vbr.py needs to be improve to support diff no of nodes source to target.

5 years

No

Yes

No

8

8/10

Speed of columnar data.

Performance tuning; user community is needed.

1 year

Node recovery is very inconsistent and impacts performance.

Concurrency

Terrible

Terrible

SQL Server and Oracle.

Simple

Scalability, query speed.

It is the foundation of our new Data Warehouse platform.

Data velocity and manageability.

The utilization/launching project has lasted for a bit more than a year now.

There have been some issues with deleting and updating data from tables.

No

No

I have not been in contact with their customer service.

I have not been in contact with their tech support.

We used Oracle but it did not scale well enough

It was straightforward.

I was not involved with the implementation.

Don't know.

The pricing is very flexible

There was a proof of concept for a number of technologies but I wasn't involved in those.

It looks promising.