You 'd be surprised how good modern hardware is. We are being hit with between 1.5 and 2 million requests per minute (steady traffic no spikes except increased

You 'd be surprised how good modern hardware is. We are being hit with between 1.5 and 2 million requests per minute (steady traffic no spikes except increased usage in the weekends), and our analytics solution runs on just 2 main servers and costs $3k per month (total associated costs except human labour which is 1 engineer working on it part time now). We talked with a famous analytics company and they gave us a quote of 1 million yearly to work with us. So how did we get it down from 1m to 35k? We pretty much do the thing the article suggests (roll-ups section). We compute data hourly. Alongside with the hourly computation we also dump some extra data that can be used to compute numbers for the day (eg unique user ids, from unique user ids per hour), then from the day we get per week and then per month. We also have less moving pieces (our stack is way more traditional), and we manage our own hardware (key for keeping costs down) When you get data back from the system you only hit the pre-computed cache, no query touches the main system from the dashboards. We only allow queries running in a 30 minute window to run on the live system - to ensure that no crazy load is going to be built on top of it and we use that to mostly catch anomalies on the real time data. (our parsing time is good too, between 10 seconds and 1 minute compared to the 2-30 minutes the article gives). However this is the "You are alive but you 're not living" angst of analytics. All the data is there, but you cannot freely prod it for answers and patterns. If you want to get an answers about past data, you need to go through an overly complex process of raising a new cluster and ingesting old backups, multiple times, then waiting for a few days. It get's relatively expensive, slow and at times will demoralize you and make you back off from getting the answers you need. You could try keeping a smaller cluster that only gets % percent of the data (eg only 2%) for finding trends, drawing heatmaps etc and that one can run in realtime but your CEO will say that's a stupid idea to your face and it's realtime-all or nothing. You might say that's a situation you can live with provided the absolutely insane cost savings, but when the company goes at a nicer retreat for only a selective elite few that easily costs 20k, or runs an over the top kitsch open party party/recruiting that costs 80k, and you are being dragged into a meeting on a monday morning and confronted; Why did the "3k per month-3 billion requests per day" system cost 6k this month? (because we had multiple clusters in parallel computing historical data for the past 6 months that you asked for). You just get bitter you didn't give the analytics company the 1 million they asked for and be done with it.