Recent stories
Stories, solutions & updates from the team at Bolt.
Mastering incrementality in ads: an introductory guide
This article was written by Anton Bugaev, with contributions from Juan Carlos Medina Serrano and Garret O’Connell. It was supported by the work of Bolt Marketing Technology and Performance Marketing
Managing all the variations of (dbt) metrics
Written by Silja Märdla, Senior Analytics Engineer at Bolt. Semantic layers and metrics will be a big part of how we work with data in the future. We’ve done this
AAAAA Testing: How to make tests AI-friendly
For anyone who develops software, writes code, and eventually writes tests (unit, integration, or component), this article is for you. It doesn’t matter if you’re working on a microservice, a monolith, a frontend app, a mobile client, or a shared library. We’ve all been there: tests that are so complicated they’re harder to read, write, and refactor than the production code they’re meant to validate. If your test suite feels like it’s fighting you, you’ve come to the right place.
How data science is done at Bolt
The structure of the Data Science team at Bolt Our Data Science team at Bolt consists of talented data scientists and machine learning engineers who are the driving force behind
Fraud checkpoints: from overengineering to simplicity — and where flexibility works
Learn how Bolt built a flexible anti-fraud platform powered by real-time checkpoints that detect and stop threats instantly.
Feature leading at Bolt: More than writing code
At Bolt, engineers don’t just implement tickets. They lead features end-to-end. A feature lead owns the plan, coordinates a cross-platform squad, removes blockers, and drives the rollout to make sure the feature is truly done. This post breaks down Bolt’s feature lifecycle, what goes into a strong system design document, how success metrics and analytics are defined, and how we launch safely through controlled rollouts and A/B tests.
Exposing dbt models in Looker
This article was written by Silja Märdla, Senior Analytics Engineer at Bolt. TL;DR This article describes how we’ve replaced manual LookML writing with an automatic sync process to ensure all
Evolution of the user graph with company growth
Written by Jaroslav Judin, Senior Software Engineer at Bolt. Understanding user relationships is crucial in fraud analysis and social networks. However, the options for choosing a suitable storage model are
The evolution of data caching in a high-traffic microservice architecture
Data Modelling with dbt (core)
Written by Silja Märdla, Senior Analytics Engineer at Bolt. In Bolt, we’ve been on a mission to understand the best way to systematically prepare data for our different data users:
