OpenText Analytics Database (Vertica) Reviews

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.

Bulk loading data using copy

Fast analytical functions

Ability to extend functionality

Great reporting.

Faster deletes!

4 years

None

Sometimes users write bad queries that has brought down the cluster. Need a way to better manage resources.

None

Great

Great

No

In house implementation.

Analytic functions.

We are trying to data mine customer event data. Having the ability invoke analytic functions without having write self join SQL statements ... just brilliant.

Ability to use analytic functions in where clauses, being able to use aliases in the where and order by clauses will make query writing/reading a lot easier.

2 years.