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This is a guest post by Alban Perillat-Merceroz, from the Analytics team at Teads.
In part one, we described our Analytics data ingestion pipeline, with BigQuery sitting as our data warehouse. However, having our analytics events in BigQuery is not enough. Most importantly, data needs to be served to our end-users.
TL;DR — Teads Analytics big picture
In this article, we will detail:
- Why we chose Redshift to store our data marts,
- How it fits into our serving layer,
- Key learnings and optimization tips to make the most out of it,
- Orchestration workflows,
- How our data visualization apps (Chartio, web apps) benefit from this data.
Data is in BigQuery, now what?
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