Azure Cosmos DB vs. DataStax Enterprise

Like any NoSQL database, whether it's MongoDB or not, it's best suited for unstructured data. It's also well suited for storing raw data before processing it and performing any type of ETL on the data.

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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.

• Scalable Instantly and automatically serverless database for any large scale business.
• Quick access and response to data queries due to high speed in reading and writing data
• Create a powerful digital experience for your customers with real-time offers and agile access to DB with super-fast analysis and comparison for best recommendation

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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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• Expensive, so be careful of the use case.
• We had a thought time migrating from traditional DBs to Cosmos. Azure should provide a seamless platform for the migration of data from on-premises to cloud.

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• Cassandra is a bit difficult to learn and understand
• The costs are slightly higher for our company
• Hardware requirement is moderate to high at the beginning

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It's efficient, easy to scale, and works. We do have to do a bit of administration, but less now than when we started with this a couple of years ago. Microsoft continues to improve its self-management capability.

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We will continue to use it because it scales well with commodity hardware and we are satisfied with the documentation and support.

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It has very good compatibility and adaptability with other APIs and developers can safely create new apps because it is compatible with various tools and can be easily managed and run under the cloud, and in terms of security, it is one of the best of its kind, which is very powerful and excellent.

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There is a bit of a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise but once you get the hang of denormalizing data and getting the data model correct DataStax Enterprise is very usable. Usability from the developer's standpoint is very simple - the complication is on the architecture side with the data model.

Microsoft is the best when it comes to after-sales support. They have a well-structured training and knowledge base portal that anyone can use. They are usually quick to respond to cases and are on point for on-call support. I have no complaints from a support standpoint. Pretty happy with the support.

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DataStax has the best community. They have instant customer support to solve problems and are knowledgeable of the problems faced by the customer. The documentation is pretty top-notch.

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Cosmos DB is unique in the industry as a true multi-model, cloud-native database engine that comes with solutions for geo-redundancy, multi-master writes, (globally!) low latency, and cost-effective hosting built in. I've yet to see anything else that even comes close to the power that Cosmos DB packs into its solution. The simplicity and tooling support are nice bonus features as well.

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DataStax Enterprise offered best-in-class write performance and scalability. The customer support team was very helpful in the adoption of new technology.

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• It's made managing raw data much easier
• It provides a way to maintain raw data at a low cost
• It's easy to massage the data

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• Highly Scalable Database, Highly Available Services, and Platforms.
• High Performance, Low Latency and Highest throughput across varying workloads.
• Configured, Tuned and Monitored correctly works to provide the best user experience!
• Negative: Maintenance and Debugging Corner Cases

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