IBM IMS vs. IBM watsonx.data

We have built a database of a network of bank branches with all the needs of security and recovery/restart. It requires knowledge of obsolete maintenance languages and perhaps it is too rigid in the schemes.

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Real-time transaction processing (both reads and writes) is where DataStax Enterprise shines. It's very fast with linear scalability should more resources be needed. Additional nodes are added very easily. DataStax Enterprise on its own (without Solr or Spark enabled) isn't well suited for long complicated reports. The data model doesn't support joining multiple tables together which is common in BI reporting.

• Security
• Reliability
• Big volume of data and good performance

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• Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
• Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
• Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
• Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.

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• Certainly, not being a DB relational penalizes its use
• It should be more flexible
• Open up to more recent languages

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• Interface do need some improvement to be more intuitive for analysts like me.
• Configuring multiple data sources can require support, as it isn't easy for new users. So, it can be made easier.
• Additional ETL features can be added, so users don't have to switch between tools.

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As an open source technology Cassandra can be readily used with or without any commercial support. DataStax provides value-added services and features, and in the end it is up to individual situations to strike a balance between the desirability of such support/service versus the associated cost.

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DataStax has a good community built around it and has amazing scalability options. Though the initial setup is a bit costly, in the long run, it makes up for it. It also has powerful monitoring tools and a clean UI.

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good recovery features

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scalable product

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We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.

easy to follow documentation, support is there when needed

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use saas service

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Essentially, for a question of reliability and scalability, the performance has always been of an enterprise level. The utilities are efficient and effective.

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Claude is better for troubleshooting actual OPS/REXX but that's not an IBM product so I understand why IBM watsonx.data isn't great for that, but if IBM watsonx.data did help with code then it would be the only product that I use.

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cognos integration works great

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• The investment made about 20 years ago that has been fully repaid
• Reliability and security of investments
• Unfortunately, it is quite outdated and therefore maintenance costs are higher because the skills are not always available.

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• for one automation project, we managed to cut cloud storage costs by a third through IBM watsonx.data's lakehouse optimization
• data integration projects have had a 20 % reduction in turnaround times. Can only imagine how that will improve with the Claude partnership

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