Astra DB, now part of IBM watsonx.data vs. Icinga

We've been super happy with Astra DB. It's been extremely well-suited for our vector search needs as described in previous responses. With Astra DB’s high-performance vector search, Maester’s AI dynamically optimizes responses in real-time, adapting to new user interactions without requiring costly retraining cycles.

Incentivized

Icinga is a world-class monitoring system. It can be used for most general monitoring situations. It is not a silver bullet, however, and there are instances where domain-specific monitoring systems are necessary. However, the output from those monitoring systems can be funneled into Icinga as a central monitoring and alerting system.

Incentivized

• We need to be able to process a lot of data (our biggest clients process hundreds of milions of transactions every month). However, it is not only the amount of data, it is also an unpredictable patterns with spikes occuring at different points of time - something athat Astra is great at.
• Our processing needs to be extremaly fast. Some of our clients use our enrichment in a synchronous way, meaning that any delay in processing is holding up the whole transaction lifecycle and can have a major impact on the client. Astra is very fast.
• A close collaboration with GCP makes our life very easy. All of our technology sits in Google Cloud, so having Astra in there makes it a no-brainer solution for us.

Incentivized

• Wealth of community-developed plugins.
• Stable codebase.
• Icinga 2 supports distributed monitoring.
• Very performant, can support tens of thousands of checks per server.

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

• High learning curve, setting up Icinga from scratch can be a bit of a challenge starting out.
• If the io2db process fails you UI stops updating, which can be very frustrating.
• There is no simple mechanism for adding new hosts and services through the web UI, it's all very config-file based.

Incentivized

Icinga is a solid solution which does everything it promises. It is backwards compatible with most Nagios instances, making the transition very easy. Once you get the hang of installing new plugins and editing configuration files expanding its monitoring capabilities are easy.

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

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

Icinga is better than Nagios because of its nicer user interface. New Relic can monitor CPU/memory and disk usage, but it's more of a performance and application troubleshooting tool rather than monitoring

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

• Better uptime due to the managed service having no outages
• Less technical debt because we don't need to worry about upgrading our Cassandra clusters
• Lower cost on infrastructure as a whole
• Quick and easy to integrate vector search into our tech stack

Incentivized

• With one check you know which applications are faulty e.g. after an upgrade. Which is big time saver
• You easily detect outages ion the applications so that your customer ideally does not even realize there was an outage.
• Detect if the environment does deliver the same result as in the same time as before to detect shortages.
• Additional information when debugging. Saved us several hours where we could simply point to a database which was slow.

Incentivized