IBM Netezza Performance Server vs. Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

Netezza Performance Server enhances the data scientist's ability to rapidly design, train, and deploy machine learning models in the database. Netezza Performance Server is essential for implementing cognitive machine learning, providing data scientists with in-database analytics, and enabling them to collaborate on a single platform. Netezza Performance Server supports the use of built-in Spark as well as Python and R for rapid and sophisticated data science and machine learning adoption at scale.

Incentivized

If you need to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.

Incentivized

• The speed of operations is really second to none
• The data control aspect is really effective and very useful
• Stability and no latency issues notices during operations

Incentivized

• Apache MADlib provides popular machine learning functionality.
• Allows you to query terabytes of data databases.
• Interoperability for AWS S3 is effortless.

Incentivized

• The cost is expensive for medium-sized and smaller companies.
• IBM could bring back the technical account managers.
• The procurement process is a hassle.

Incentivized

• Running on Azure is a little more difficult.
• Synchronization with Kafka may be a little easier.

Incentivized

They were very helpful. We needed support for initial implementation.

Incentivized

With a simple user interface and
access to IBM's online instructions, Netezza Performance Server is easy to
operate. It also has built-in contribution facilities, as well as good customer
support, to make the DBA's job easier.

Incentivized

• It allows us to work together simultaneously in the database to query our custom results.
• It requires users to know the object name as it does not pop up in their text editor. Thus, users take more time in writing queries.

Incentivized

• There was a noticeable reduction in system reliability.
• Saw a reduction in unsuccessful analytics operations.

Incentivized