Amazon Athena vs. IBM watsonx.data

If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.

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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.

• Nested Schemas like JSON data structure
• Ability to adapt the data model to fit your queries better
• Performance Improvement

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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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• Response caching can be improved.
• Data Partitioning is tricky and understanding of the same could be improved.

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• I seen the connectivity of s3 buckets on aws it is only user based authentication if it can used as role based it would be help as part of security constraint
• Mrap connections are more flexible, But using the presto and it helps a lot
• If the customization can be happened on the IBM watsonx.data side like connectivity and other things

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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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Easy to use. Scalable. Gets the job of data warehousing setup done. Using the datalake on S3 has become super convenient.

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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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Cognos Analytics wont be able to have any assets without the connectivity to IBM watsonx.data lakehouse. In our environment, IBM watsonx.data serves as the primary data source for all of our assets within our Cognos Analytics environment. IBM watsonx.data also incorporates the ability to run and verify SQL schema right within the management console.

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IBM Cognos offers exceptional scalability, making it a strong solution for organizations with growing business intelligence and reporting needs. Numerous Fortune 500 companies rely on IBM Cognos to manage complex reporting environments, support large user populations, and scale their analytics capabilities as their businesses grow.

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• The query speeds help us make more decisions in a day (speed).
• If you need more horsepower for specific times in the day this option helps scale.
• The security of your environment is well protected too.

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• It has saved us a huge cost by offloading data-warehouse which are comparatively expensive.
• Provides a single source of data to train AI models so we don't need to rely on multiple sources, saving infra/license costs.
• Prevents data duplication as multiple engines can use the same data bucket for the search.

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