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Data
Infrastructure
and Data
Access in
Nubank
18/12/2018
André Tavares
Product Manager
Challenge
Platform
Data Tools
Services
ETL Jobs
Challenge
• 55 squads
• ~190 microservices and
29 models in production
• 40 Tb of data
processed each day
• 500 DAUs in data tools
Make it easy and safe to use data
for human and computer made
decisions!
How to improve the quality of
decision making in the company?
Platform
Datomic
Extractor
Online
Models
Production
Services
Metabase
Jupyter
Notebooks
Looker
Amazon
Redshift
Redshift
Loader
Batch
Models
Spark
Engine
Production
Services
Datomic DB
Logs
Amazon S3
Metadata
Manager
Non-datomic
Datasources
Non-datomic
Extractor
Static Inputs
Databricks
Loader
Databricks
Serving
Layer Loader
Amazon
Dynamo DB
Serving
Layer
Platform
ETL Jobs
9
DB1 Log S0
DB1 Log S1
DB2 Log S0
Dataset Series
contract 1
contract 2
dataset 1
dataset 2
model
policy
ETL Jobs
ETL Jobs
• Anyone in the company can
contribute ETL jobs by opening a PR
in our monorepo

• Teams are responsible for writing
and maintaining their jobs
• Jobs are written in scala (sparkSQL);
some DSLs are provided
• Use databricks to iterate on logic
• Peer review to ensure quality and
consistency
• 100 contributors making 300+
contributions per month
Data Tools
Data Tools
Metabase and Looker: Simple
queries, sharing plots
Jupyter: In depth analysis,
complex plots, training models
Databricks: Dataset building
and computationally intensive
tasks
Data Services
Data Services
Trainings: Weekly trainings on SQL,
python or scala, new employee
onboarding, new tool rollout
Support: Dedicated slack support
channels; community of users support
each other
Meetings: Forums for sharing data
scientist and analyst work, monthly
meetings to discuss state of Data
Data Analysts: Function focused to
improving data usage in the company
(not SQL slaves!)
We’re hiring!
sou.nu/jobs-at-nubank
Data Infra and Data Access in Nubank

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