Astra DB vs. DataStax Enterprise

I would start with the scenarios which are well-suited. I can see Antra DB excels at the scalability and global distribution part as with the company's continuously growing size, we need our data to be growing at the same speed, which ASTRA DB largely supports, and if we talk about the less appropriate scenario, I would say when we need to incorporate external tools for the better monitor, it sorts of fails purpose.

Incentivized

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.

• It's very resilient and scalable, no downtime and no issues scaling up to meet our needs.
• Low latency reads and writes
• Cost effective - The on demand model worked out cheaper than running our own clusters
• Great support for any of our questions or issues

Incentivized

• 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.

Incentivized

• Need better fine-grained Security options.
• The support team sometimes requires the escalate button pressed on tickets, to get timely responses. I will say, once the ticket is escalated, action is taken.
• They require better documentation on the migration of data. The three primary methods for migrating large data volumes are bulk, Cassandra Data Migrator, and ZDM (Zero Downtime Migration Utility). Over time I have become very familiar will all three of these methods; however, through working with the Services team and the support team, it seemed like we were breaking new ground. I feel if the utilities were better documented and included some examples and/or use cases from large data migrations; this process would have been easier. One lesson learned is you likely need to migrate your application servers to the same cloud provider you host Astra on; otherwise, the latency is too large for latency-sensitive applications.

Incentivized

• 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

Incentivized

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

Incentivized

It's a great product but suffers with counters. This isn't a deal breaker but lets down what is otherwise a good all round solution

Incentivized

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.

Their response time is fast, in case you do not contact them during business hours, they give a very good follow-up to your case. They also facilitate video calls if necessary for debugging.

Incentivized

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.

Incentivized

Graph, search, analytics, administration, developer tooling, and monitoring are all incorporated into a single platform by Astra DB. Mongo Db is a self-managed infrastructure. Astra DB has Wide column store and Mongo DB has Document store. The best thing is that Astra DB operates on Java while Mongo DB operates on C++

Incentivized

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

Incentivized

We are well aware of the Cassandra architecture and familiar with the open source tooling that Datastax provides the industry (K8sSandra / Stargate) to scale Cassandra on Kubernetes.

Having prior knowledge of Cassandra / Kubernetes means we know that under the hood Astra is built on infinitely scalable technologies. We trust that the foundations that Astra is built on will scale so we know Astra will scale.

Incentivized

• The high availability capabilities of Astra DB can assist in reducing downtime, which is crucial for revenue-generating applications.
• The developer-friendly features of Astra DB, as well as support for known query languages, can help expedite development, save development time, and minimize labor costs. This can result in a shorter time to market and a higher ROI.

Incentivized

• 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

Incentivized