Netezza Analytics Reviews

Currently, we are using Netezza to the utmost. We first used it for our data warehouse. Then we moved on to doing analytics. Also, we are now doing some packages on it. Currently we are using it for multiple purposes, but, mostly it's used for reporting.

It is a back end for our SSIS, MicroStrategy,, Tableau. All of these are connecting to get the data. To do so we are also using our analytics which is built on the data.

I'm working for a retail client. We have a lot of reports, which are for store managers. The store managers used to get all the reports using the SSIS package, which was built upon our back end and they would have a lot of performance issues. When I say performance issues, in the morning when there are almost 2,000 users trying to connect to the same report, there were a lot of problems because it was a relational database. People were trying to get information out of it and it would break the flow, actually bottleneck the flow. That's why they were getting the impact.

What we did is, we did a PoC and found this client is well built. Because of that, we were able to create a separate layer for reporting. There was a simple requirement for the DBA, where we don't need to build an index or anything like that. Those reports were improved from a couple of hours to minutes. That was the biggest benefit we've had from this appliance.

For me, mainly, it reduces my costs. It's not only the appliance cost. There are also support costs and a maintenance costs. It does reduce the costs very drastically.

We use this solution for our data warehouse in an on-premises deployment.

The most valuable features of this solution are robustness and support.

ARULES() function is the fastest implementation of the associations algorithm (a priori or tree) I have worked with. In comparison with the R associations package, it is +1000x faster. The issue with the R implementation, and R in general, is that the functions do not work on large sets of data. R would generate a 100,000 x 100,000 element matrix for some of the arules runs we do – very, very inefficient programming.

Any in db machine, learning functions help by eliminating the data transport issue. NZA goes one better by allowing you to utilize a tuned engine (software + hardware) to run the functions. The result is that we can run algorithms that would be impossible to run on other platforms.

Its query performance and speed.

Earlier there was high latency to query extraction but Netezza has improved the speed for all OLAP operations. This has helped the organization to get the business answers right on time.

We're currently trying to create a data warehouse on the solution.

The appliance is quite quiet.

The need for administration involvement is quite limited on the solution.

• Its ability to process and query large amounts of structured data.
• Low administrative support in terms of query optimization and indexing support. Indexing and data partitioning is built into the firmware.
• Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more.

I can't because we're using it in a fashion such that a traditional RDMS could have been used in place of it. Our data was relatively small so we didn't see a huge benefit in transitioning new subject areas into production.

There is minimal on-going maintenance.

There were some reports we couldn't get because of performance, but with Netezza we could get more of them.

The primary use case of this solution is for creating reports from the data in our data mart.

The most valuable feature is the performance.

There is less administrative work and you won't need more than one or two administrators.

Upgrading is very good, the IBM support is very good, migration, and the migration commands are also very good and easy.