Data @ QuintoAndar

How QuintoAndar evolved its Data Platform to a Distributed Strategy.

How QuintoAndar is designing the architecture for monitoring Spark clusters.

Context

A glimpse in the journey of building our machine learning platform to deliver better ML-powered systems.

How we created a receptionist chatbot to help users accessing our customer support

How we integrated Apache Atlas with our data tools

The first challenges faced by our new Data Governance team

Communicating with the standalone Hive Metastore service

Understanding the standalone Hive Metastore service

Mediator, an elegant solution for solving cross-DAG dependency in Apache Airflow data pipelines.

Although Hive could be considered an old-fashioned tool in our ever-changing tech scenario, it’s clear that it is a successful project.

Clean code is a must-read book for software engineers (SWEs) and is part of the curriculum from several software engineering programs. It…

At QuintoAndar, the Data Availability Engineering team (a.k.a DAE) is essential for the success in all operations side. This team is…

In the daily life of the Data Engineering team at QuintoAndar, we are always facing new data flow challenges. In order to process it all…

At QuintoAndar, data availability is crucial to our core business, providing data insights for our stakeholders, from operations and…

The 6 most common mistakes on experimenting and how to avoid them

How we’re improving data governance using Looker and automated testing.

Machine learning models are both dependent and sensitive to the data received. As a consequence, data acquisition processes and feature…

First off, I want to register my thanks for Julliane Alberigi and Gabriela Bassa for inputs on this article, discussion, and insights…

How we are scaling our data workflows to support the growing business analytical demands with Apache Airflow.

Since I’ve joined QuintoAndar as a Data Scientist, I noticed an awesome initiative that the team had in order to scale our data products…