IBM Netezza Performance Server Reviews

It is extremely easy to use. The platform is very efficient and robust as well.

Generally, the machine utilized for the PoC is rarely returned once the overwhelming results are seen! Customers decide to keep the project, almost from scratch! The initial KPIs are delivered with low investment. There is quick implementation and considerable outcomes which make the platform a valuable asset to the organization.

Marketing, I dare say, should be improved. The ads should never mention “big data” since the platform was not initially designed for it.

I worked years and years ago with Teradata, and this is the platform to which it should be compared. It is important to emphasize that Netezza and Teradata have different markets. Netezza performs very well “in its peculiar and valuable small world.”

I have used this solution for years.

We did not have any stability issues.

It is not designed to process a huge volume of data.

Technical support is good.

I worked for different companies and my experience is really wide. Teradata is the paramount solution! Oracle’s Exadata invests a lot in both technology and marketing, sometimes losing the focus on “real” analytics. They used to address the platform as both OLAP and OLTP (“one size fits all”), which is not correct.

The rule of thumb in the data warehousing/business intelligence space starts with a simple PoC. That being said, there must be a few questions to be answered by the platform. The data model must be simple and flexible enough to address some “extra questions” within a set timetable, in case the customer wants to extend the PoC prior to making the purchase.

Comparisons with other solutions, including pricing and licensing, is as important as a PoC.

I compared Netezza to Teradata and Exadata.

IBM PureSystems or NPS has a patented NDAC or Netezza data accelerator card inside each of the SPUs (Snippet Processing Arrays). The SPUs act as individual processors that share part of a query. This helps in high-speed parallel processing of big data volumes. Querying a large data volume takes just a few seconds to minutes, if it is done right.

Analyzing years of data requires high processing power and storage. IBM PDA has exactly that. Years of processed data (tables) can be queried and retrieved based on management requirements. This can be done in minutes for analysis. This is extremely important in identifying trends for decision making in higher management, to serve customers better in today’s business environment.

It is a highly complicated architecture and only IBM engineers/support, or someone who worked on the hardware side of the system, can understand the system architecture completely.

This means that:

• Replacements can only be done by an IBM engineer
• Components are not generic and are not on the market
• Many of the systems are IBM patented
• Service support costs are high

Storage, although high, is limited depending on the rack configuration. For example, an N3001-80, which is an eight-rack Mako Server and also the top end model with the highest storage, can store a maximum of 384TB. Should your data exceed this limit, the storage cannot be extended.

It cannot be used for unprocessed data. The data has to be in a table format.

We have been using the solution for five years.

We rarely had stability issues. You may expect an average of one outage every two months for a few minutes, if it is maintained properly. This is mostly due to “pollreplytimeout” errors of the SPU.

All the rack components are dual for redundancy. Almost all components have failsafe/backup, including the host that is configured in cluster for high availability. With this, the system is up and running all the time, or you can get it up at the earliest possible time in case of component failures/system down situations.

We had scalability issues. Storage, although high, is limited depending on the rack configuration. For example, an N3001-80, which is an eight-rack Mako Server and also the top end model with the highest storage capacity, can store a maximum of 384TB. Should your data exceed this limit, the storage cannot be extended. You will have to purchase a new appliance.

I would rate the technical support at 7/10.

We did not use a previous solution.

IBM sets up the NPS and does the initial configurations. As an admin, you will have a completely configured system ready to work on.

This is a multi-million dollar product. The software and hardware are both IBM patented.

Netezza SQL ('nzsql') runs only on PDA systems, and cannot be installed on other enterprise-class servers. Power consumption is high and these systems must have mandatory IBM support, due to the patented and exclusive IBM hardware. However, the benefits outweigh the costs if you have large data volumes that require fast analysis on a day-to-day basis.

We didn't really examine any alternatives. I started working directly on IBM PDA (Netezza) and I haven't had any chance to work on the competitors' products. I was not a decision maker in the selection of this product. This was a higher management decision.

A close competitor of IBM PDA is Apache Hadoop. As of now, no other product is as fast or as stable.

You can definitely consider this appliance if you have:

• A large volume of processed data (tables) that are created on a daily basis
• Data that requires daily analysis, critical for decision making, and a budget to complement it.

This is one of the most stable and fastest data warehouse appliances available in the market today.

Distribution (no index or partition), built-in analytics is a major advantange.

It has improved our management information systems and reporting capabilities.

It should also consider cloud based solutions.

We have been using the solution for more than ten years from the 8000 series,

We did not encounter any issues with stability.

I can't extend the storage, only up to 6x compress of data. You need to plan this when selecting the right product to buy.

Netezza support is good.

We used Oracle previously. The performance required less maintenance when comparing index and data maintenance issues.

The setup took only a few hours with all built-in apps. It is very quick and easy to use.

One license and one support, also cheaper compared to other products.

We evaluated MS SQL server.

It is easy to use. Make sure you select the right ETL and reporting tool. Also select the right tool for the organization to hold it in the long run.

It has a compression engine and FPGA on but you should still analyze your volume of data and decide on the right model and size.

We use Aginity to access Netezza database. We really like the way we can dominate the physical distribution of the data hence know how to improve the performance of the query. Netezza on its own is very fast which makes our life easier to run huge queries for analytics.

This has really helped us to improve the performance of our Data Marts and warehouses. We can run our reports very quickly. It has also improved the turn around time of business requests. 

In Aginity there should be a way to format the SQL queries. I think we can't format the query the way we can do it in Oracle editor (beautifier). Say, for example, if we are trying to get the DDL of an existing view, we lost the formatting. It's a minor issue, but important from usability point of view. Other than this, I think we are good so far with Netezza as a whole.

We have been using this product from last 5 years. 

We did not experience any issues.

We haven't had any issues.

We've not experienced any issues.

If volume is the issue, use Netezza. Nothing is better than this product.

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.