ArangoDB vs. Astra DB

Use cases provided by default are good and can be improve better using Machine Learning and AI. AQL query language is very simple and efficient in use if anyone using SQL can quickly learn AQL Language.

Developers can easily map the database and can access various patterns like search, ranking.

JSON and semantic search is the latest and next-generation technology to implement to access and extract large datasets.

Incentivized

We use Astra DB to improve our management systems. Storing data has become hassle-free and quite simple. When launching a Cassandra-based cloud application, Astra DB is exactly what you need. In addition to the standard training programs and videos, the extended support and training require significant additional effort to activate and cover which I feel is a bit more tedious task.

Incentivized

• AQL query language is big plus for ArangoDB
• It can be implemented cloud as well as on-prem
• Search Engine is a very good option for ArangoDB

Incentivized

• 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

• By providing the free services for few months will be help understand for beginners
• Enhancing features in dashboard and can make UI more user-friendly
• Should conduct more surveys and adv to improve scalability

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

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

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

It uses AQL query Language, which is different from other Databases. It has flexibility to integrate in cloud, on-prem anywhere

Incentivized

We know Astra is built on Cassandra / Kubernetes / Stargate and can work on any cloud. The competitors we reviewed are cloud specific and create a lock in. We also have the option to run Cassandra / Stargate ourselves if we wanted to. The competitors don’t give that option

Incentivized

As per my experience, I never faced issues of scalability with Astra DB. We don't have at the moment a use case with millions of requests or users, so I can't give full score because of my limited use case.

Incentivized

• It is very powerful tool and should adv more to improve sales
• Should conduct more free trails and trainings
• Open source and runs everywhere

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