Roll Your Own Analytics
(Data engineer here)Nice article! I did something very similar to this for my blog but used Snowplow's javascript tracker (https://github.com/snowplow/snowplow-javascript-tracker), a cloudfront distribution with s3 log forwarding, a couple lambda functions (with s3 "put" triggers), S3 as the post-processed storage layer, and AWS athena as the query layer. The system costs under $1 per month, is very scalable, and is producing amazingly good/structured data with mid-level latency. I've written about it here:https://bostata.com/post/client-side-instrumentation-for-und...By using the snowplow javascript tracker, you get a ton of functionality out of the box when it comes to respecting "do not track", structured event formatting, additional browser contexts, etc. If you want to see how the blog site is functionally instrumented, filter network requests by "stm" (sent time) and you'll see what's being collected.I've found (after setting similar systems for 15+ companies of varying scale) that where a system like this breaks down is when you want to warehouse event data and tie it to other critical business metrics (stripe, salesforce, database tables that underpin the application, etc). Another point it starts to break down is when you need low-latency data access. At that point it makes more and more sense to run data into a stream (kinesis/kafka/etc) and have "low latency" (couple hundred ms or less) and "high latency" (minutes/hours/etc) points of centralization.Using multi-az/replicated stream-based infrastructure (like snowplow's scala stuff) has been completely transformational to numerous companies I've set it up at. A single source of truth when it comes to both low-latency and med/high-latency client side event data is absolutely massive. Secondly, being able to tie many sources of data together (via warehousing into redshift or snowflake) is eye-opening every single time. I've recently been running ~300k+ requests/minute through snowplow's stream-based infrastructure and it's rock-solid.Again, nice post! It's awesome to see people doing similar things. :)