You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose

I work at Volvo Car Corporation as a consultant Project Manager. We have deployed Zabbix in all of our factories for factory monitoring because after thorough investigation we saw that Zabbix supports the wide variety of Operating Systems, hardware peripherals and devices a Car Manufacturer has.

No other tool had the same amount of support onboard for our production environment and we didn't want to end up using a different tool again for several areas. That is the major strong point about Zabbix and it's free of course. Another strong point is the documentation which is widely available; Zabbix Youtube channel with tutorial video's, Zabbix share which holds free templates, the Zabbix online documentation and the Zabbix forum also helped us out quite a bit. Deployment is quite easy since it uses templates, so almost all configuration can be done on server side.

To conclude, we are really pleased with the tool so far, it helped us detect several causes of issues that were a pain to solve in the past.

Centreon is part of the Nagios ecosystem, meaning there is a huge number of resources you may find around in the community (plugins, skills, addons). Zabbix monitoring paradigms are totally different from Centreon. Centreon plugins have some kind of intelligence when they are launched, where Zabbix monitoring rules are configured centrally with the raw data collected. Testing both will help you understand :) Users used to say Centreon may be faster for setup and deployment. And in the end, both are full of monitoring features. Centreon has out of the box a full catalog of probes from cloud to the edge https://www.centreon.com/en/plugins-pack-list/ As soon as you have defined your monitoring policies and template, you can deploy it fast through command line API or REST API. Centreon plays well in the ITSM, Automation, AIOps spaces with many connectors for Prometheus, ServiceNow, GLPI, Ansible, Chef, Splunk, ... The polling server mode is one of the differentiators with Centreon. You set up remote server(s) and chose btw multiple information-exchange mechanisms. Powerful and resilient for remote, VPN, DMZ, satellite networks. Centreon is a good value for price to do a data collection (availability, performance, fault) on a wide range of technologies (physical, legacy, cloud). There are pro support and enterprise version with dashboards and reporting. IT Central Station gathers many user feedback you can rely on both Centreon & Zabbix https://www.itcentralstation.com/products/centreon-reviews  

We highly recommend Zabbix. We have used it to build our own monitoring product (available on cloud -like datadog- or on premise with support) because of its flexibility and extendability. It can be easily integrated with the powerful dashboarding and data aggregation of Grafana, so it is perfect. All configuration is done via web and templates, so it scales well and can be distributed via proxies. I think there also more companies providing consultancy in Zabbix (like ours) than Centreon and community is much wider. Also Zabbix roadmap and focus (compatibility with Elasticsearch, Prometheus, TimescaleDB) is really really good.

Hi Vivek, what's your stack? If huge monitoring bills are your concern and if you’re using a number of JVM languages, or mostly Scala / Akka, and would like “one tool to monitor them all”, Kamon might be the friendliest choice to go for.

Kamon APM’s major benefit is it comes with a built-in dashboard for the most important metrics to monitor, taking the pain of figuring out what to monitor and building your own dashboards for weeks out of the monitoring.

https://kamon.io/apm/

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.

this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/