IBM Netezza Performance Server Reviews

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.

It needs a better in-house development tool.

I've used it for three years.

Functions are difficult to deploy.

No issues encountered.

No issues encountered.

7/10.

7/10.

Our customers mostly switch from traditional RDMs to appliances.

It is very easy to setup.

We used an in-house vendor who were 8/10.

Choose your most complex scenario. Do a POC and try to scale.

You should know how it works well so you can use its power. So, start with a partner or vendor team.

• Supports both business intelligence and advanced analytics
• Efficient even if being used by thousands of users at the same time
• Streaming architecture based on blades
• Compatible with the most popular business intelligence and analytic tools
• Standard SQL, ODBC, JDBC, and OLE DB interfaces

We had lot of POC's on data warehousing project with the help of Netezza, we have proved that it has really improved time on doing analytics

I have used this solution for more than two years

There were no issues with the deployment.

It's reliable and available, with 99.99% uptime.

There were no issues with the scalability.

Support for this product is good.

Initial set-up was straightforward.

It was done by an in-house team.

It is good product if you are choosing to go for datawarehouse business intelligence.

• The speed of the MPP platform
• The ability to load large amounts of data in very short order
• The fact that the hardware and software are designed to work together and are supported in unison by IBM

We can process through more data much faster than we could with prior technologies.

More monitoring tools would be nice. I have found no tools within the tool which help with long running query issues. I would like to see the ability of static binds so access path can be counted on.

I've been using this since 2010

There were no issues with the deployment.

Stability comes and goes. Every software upgrade removes some problems, but introduces others. None have been critical, though.

There were no issues with the scalability.

It's 7/10 as when something goes wrong within IBM, it can take quite a bit to get it straightened out including a phone call to the sales rep to light fires.

It was a straightforward set-up. You just have to install the appliance, turn it on, and you can start using.

Netezza themselves set up the machines. We implemented our business rules using a vendor team to get us up to speed on MPP processing.

We evaluated this and Greenplum. Netezza won the battle for being an appliance that is wholly supported by the vendor. Greenplum won the individual transaction battle, but it was not enough to overcome the ease of an appliance.

The most difficult time I have with people is getting them to understand that Netezza is not meant for individual transactions, but for full set processing.

• Elimination of space management with less time spent on capacity planning
• Push down optimization through the use of FPGA to limit data at the storage layer
• The concept of an appliance where everything is pre-built (storage, hardware, database and OS) and the support for each of these components is through one vendor.

Query and process response times have improved resulting in customers being able to process and analyse more in less time.

Certain database maintenance tasks related to the catalogue need for the database to be offline. Enabling those tasks to be done with the database on-line would be beneficial. Also, the availability of additional SQL and database tuning options would be good.

I've used it for six years. We currently use the Mako, TwinFin-6 and TwinFin-3 appliances.

Deployment is straightforward.

The appliance is stable for the most part. All components have fail-over/spare components built-in. Any failed component is replaced within 24 hours by IBM support.

Since it is an appliance, individual components within the appliance cannot be scaled. Scaling would mean buying the next generation of the appliance.

7/10

7/10

Our previous solution was in Oracle which had a custom data model. The reason for the change in technology was due to the data model. The new data model was purchased from IBM and their recommendation was to use Netezza for it.

The hardware set-up is straightforward.

It is done by IBM support in a day or two. The use of the appliance to implement the data model require for knowledgeable development and database resources. Prior working knowledge of Netezza would be an added advantage.

IBM offers great pricing on refurbished appliances. These appliances are just like new but can be negotiated to be offered at much lower prices.

Resources who have previously worked with Netezza should be asked to work on the project. If people with prior Netezza knowledge cannot be engaged then candidates who are familiar with Linux should be sent for Netezza training offered by IBM prior to engaging them to work on this platform.

The ability to support a lot of complex queries to analyze rather big data sets at the same time is a valuable feature for us.

Netezza appliances are used on several key areas such as customer analytics, pricing, predictable analytics and forecasting, and reporting etc. The platform is really versatile and cost effective.

Data lineage on column or even object level does not exist, therefore external applications have to be used

We've been using it for three to four years.

There were no issues deploying it.

It is generally a reliable and stable platform.

There were no issues scaling it.

As a developer I am not dealing with IBM customer service related activities.

As a developer I am not dealing with IBM customer service related activities

No other solution was used previously.

I was not part of the initial set up but I know that the main challenge was to set up IBM CDC to transfer data to the Netezza appliance

No other solutions were looked at.

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.

We are using an older version. Some improvements are already implemented, such as the addition of neural networks. Other improvement would be to make the existing algorithms even faster, e.g. kmeans can actually be parallelized. If this reworking of the algorithm is done, it should leverage the MPP architecture of Netzza and run much faster.

Also, some algorithms are faster than almost any other method or system e.g. NZA.ARULES() function. Whereas others are not as fast and could probably be optimized for an MPP architecture like kmeans function.

2-3 years on and off. 1 year for actual integration into another tool for mass consumption.

For deployment, ensure you follow the instructions. The version of Netezza must be matched with the version of NZA. We tried to install a newer version of NZA not supported by the version of Netezza we had and it did not work.

N/A. We did not engage IBM tech support, mainly due to past experiences where it is faster, easier and much less stressful to just figure things out yourself.

Rapidminer, R, DB2 intelligent miner, SSAS data mining were all tested for association rules. Rapidminer and R did not pass. They use matrix math, and cause +100x performance degradations. DB2 intelligent miner, SSAS data mining, and likely also NZA follows the original a prori methods of using sets, not matrices.

DBA set it up, but it seemed very simple. I helped with designing the overall architecture. If you stick with NZA as a “function repository DB” concept and do not put data on it (as recommended in the install manual) you will be ok.

In-house. Don’t over think it. Just apply some base architectural principals and give it a try. If you find something not working make adjustments.

I had nNo visibility to pricing. Netezza in general though, is very cost effective. If you compare to other MPP platforms like Hadoop. Hadoop requires a very “non-enterprise” culture to implement and a team of designers / engineers. Netezza requires maybe 1-2 admins.

I would have to understand the specifics of the organization, the data size, the tasks, etc. to make any recommendations.

Netezza partitions all database objects across all available SPU’s. With traditional RDBMS systems, partitioning database objects requires a lot of planning and configuration. Netezza, only requires to develop a partitioning scheme. This eliminates several steps in the process, vastly simplifies performance tuning leading to very high performance.

As the system is preconfigured, the site level configuration required is minimal. Eliminates the need for a separate SAN.

Reduces system administration and database administration effort. Simplifies performance tuning effort. The net effect is reduced load on DBA team and quicker turnaround.

Application development in Netezza is done using SQL scripts and stored procedures. GUI based ETL tools provides higher level of visibility and higher productivity. I hope to see a GUI based application development environment. This could also be achieved through tight integration with an ETL tool, like IBM’s own ETL tool DataStage.

We've used it for five years.

Netezza was one of the components in our application. This component had the least number of issues

Netezza was one of the components in our application. This component had the least number of issues

Netezza was one of the components in our application. This component had the least number of issues

Good. We had Netezza experts assigned to the client site and had easy access to them.

1. Compared to other RDBMS, Netezza provides much higher performance and much lower maintenance effort
2. Compared to Teradata, lower cost and lower administration

Initial set-up of the system is quite simple. Eliminates several steps required in a custom built platform, like storage system configuration, planning database storage.

I was a consultant from the system implementation vendor.

1. Netezza is well suited for data warehousing and analytics, not for OLTP application
2. The key approach is to develop an uniform data distribution scheme and collocation of data partitions across related database objects. For example, if we have a large customer dimension and a large sales by customer fact, these tables will be joined very frequently. To get the best performance, both tables should be distributed on the same key, e.g. customer_id.

• Lots of space
• Implicit compression
• High Availability with multiple redundancy

The performance has meant that we reclaimed many hours in our batch window. The ease of which we ported data onto it, the fact that it does not take a lot of administration and the overall manageability of the box have improved our company.

Ability to use constraints to enforce uniqueness would be an improvement.

We got our first Netezza box in 2004 and the Pure Data in 2014 following the acquisition by IBM.

A blade failure at the commissioning stage, but the box (and predecessors) have been extremely stable and provides us with lots of scalability.

Very good for this product. It was excellent when it was Netezza and that support seems to have continued with IBM.

Previously used DB2 LUW but this could not provide us with the performance we needed at the time. We looked at Netezza as they were trying to get a foothold in the UK and the portability of data and the speed with which it performed was incredible.

Very straightforward for Pure Data but the initial set-up was affected by the fact that we seemed to get a junior technician from IBM who was not as good as other engineers we have had.

Build a strong relationship with your technical account manager as you will be referring to them quite a lot. Could save many man hours in the long term.

I can't comment on the figures but needless to say it has been a resounding success for us.

IBM support is very good for this product. It has very few issues and it does exactly what it says on the tin. Check for the functional limitations of the logical database e.g there are no such thing as indexes/primary key constraints where you might want to force uniqueness.