Data Infra and Data Access in Nubank

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This document summarizes Nubank's data infrastructure and access. It notes that Nubank has 55 squads processing 40 TB of data daily across 190 microservices and 29 models. The goals are to make data easy and safe to use for decisions and improve decision making. The infrastructure includes platforms like Datomic, Redshift, Spark and services like Metabase, Looker, Jupyter notebooks. Over 500 people access data tools daily. There are over 300 ETL job contributions monthly to extract, transform and load data from various sources into the data warehouse. Tools like Metabase, Looker and Jupyter notebooks are used for analysis and Databricks for complex tasks. Training, support channels and meetings help users.

Datos y análisis

Data
Infrastructure
and Data
Access in
Nubank
18/12/2018
André Tavares
Product Manager

Challenge
Platform
Data Tools
Services
ETL Jobs

• 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?

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

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
Metabase and Looker: Simple
queries, sharing plots
Jupyter: In depth analysis,
complex plots, training models
Databricks: Dataset building
and computationally intensive
tasks

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!)

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1. Data Infrastructure and Data Access in Nubank 18/12/2018 André Tavares Product Manager

2. Challenge Platform Data Tools Services ETL Jobs

3. Challenge

4. • 55 squads • ~190 microservices and 29 models in production • 40 Tb of data processed each day • 500 DAUs in data tools

5. 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?

6. Platform

7. 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

8. ETL Jobs

9. 9 DB1 Log S0 DB1 Log S1 DB2 Log S0 Dataset Series contract 1 contract 2 dataset 1 dataset 2 model policy ETL Jobs

10. 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

11. Data Tools

12. Data Tools Metabase and Looker: Simple queries, sharing plots Jupyter: In depth analysis, complex plots, training models Databricks: Dataset building and computationally intensive tasks

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14. 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!)

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