Engineering Blog

Teads tested Privacy Sandbox functionalities (Protected Audiences, Topics, Attribution Reporting) for ad campaigns.

A dedicated framework for unit-testing has been released with dbt v1.8.0-b1, we gave it a try at Teads

Building a modular monolith in Scala is challenging. In this article, we review how sbt remote caching enhances the development experience.

How to streamline front-end testing with Playwright without a backend setup

Challenges encountered while migrating Redshift + Spark pipelines to our new dbt + BigQuery stack

How did I come to leave my previous job in marketing and join Teads, an ad-tech company, as a developer, despite having no prior experience…

How we use BigQuery’s table-valued functions to prevent data leakage

Building a great interview process that provides a good signal of how well a candidate will fit your engineering team can be complex. There…

Data relocation, the key to our new reporting solution

This article describes how and why microservices have been introduced at Teads and outlines a concrete feature we implemented this way.

How do we keep the Teads Platform and its data secure on a global scale?

Combining ingestion-time partitioning and partition copy is a great way to achieve great performance for your models

How switching to a push architecture allowed decoupling a highly scaled process from our fixed-sized Cassandra cluster

Will see what’s Apple presents about AdTech at the last WWDC, SKAdNetwork, Ads with SharePlay, Pasteboard access, Location attribution…

Gradle came out few years ago with Gradle 5.0, they brought a new way of writing Gradle scripts using Kotlin language.

A preview from the CTO Forum that will take place at VivaTech on June 17

A compilation of good practices and lessons learned in a production environment

A Slim CI is a lightweight version of a CI in which we only want to run and test what is relevant. Let’s see how to use one for dbt.

Behind The Scenes - Discover what the Format feature team does at Teads, and what our challenges and responsibilities are.

Understanding key Elasticsearch optimization features: the empirical way