The problem I had was mongo doesn't scale super well when really going to big data-sets and sharding it is very ops heavy
I considered Cassandra but decided against it because it's also very ops heavy and documentation (at the time) was lacking
Hence we went to DynamoDB as it's super scalable, sharded by default and doesn't require ops (managed service). Beware though the data modelling is very different (and very close to Cassandra)
I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).
The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight
NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon
I chose notion.so to be our knowledge sharing platform accross the company. We use it for planning new initiatives, to track our OKRs but also to share our company policies. It's also very good at linking every other asset we have been producing accross the years.
It's so versatile that, at first, it a bit hard to onboard but the notion.so team is always sharing new tips and tricks on their Slack.
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
I chose Amazon EC2 Container Service to power our production cluster. It's mostly all managed, you just have to provide a few EC2 (for examples through an autoscaling group) to set it up. Configuration is super easy and it work really well to get a few simple http services to production.
It's no Kubernetes but is much easier to understand.
Also when we started Sqreen, it was the only managed tool available in AWS.
