Netflix Data Engineering @ Uber Engineering Meetup

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People, Platform, Projects: these slides overview how Netflix works with Big Data. I share how our teams are organized, the roles we typically have on the teams, an overview of our Big Data Platform, and two example projects.

Data & Analytics

NETFLIX
DATA ENGINEERING &
ANALYTICS
BLAKE IRVINE
DATA ENGINEERING & ANALYTICS
2016.09.22

TOPICS
PEOPLE
WHO WE ARE
PLATFORM
HOW WE WORK
PROJECTS
WHAT WE DO

PEOPLE
Analytic Groups @ NFLX
Data
Engineering &
Analytics
Science &
Algorithms
FP&A

PEOPLE
(Data) Engineering Groups @ NFLX
Data
Engineering &
Analytics
Cloud
Platform
Engineering
Product
Engineering

PEOPLE
Aligned Teams = Maximum Context
Marketing Product Playback Content FinOps

PEOPLE
Roles + Freedom & Responsibility
Data Analysts
Viz Engineers
Analytics Engineers
Data Engineers

PEOPLE
Role Core Functions
Data Analysts
Viz Engineers
Analytics Engineers
Data Engineers
PipelinesDashboardsJS / VizAnalytics

PEOPLE
What is the Netflix data platform?

PLATFORM
Simplified Overview
Big Data
Portal

PLATFORM
Event Pipeline
〉 83 Million members
〉 1000+ Devices
〉 Up to 1 Trillion daily events
〉 ~1m traversal time
〉 S3 warehouse is 60 PB

PLATFORM
Query Engines
〉 Hadoop (EMR) is primarily batch
〉 Presto is primarily ad-hoc
〉 Spark is emergent batch + ad-hoc

〉 S3 is our source-of-truth
〉 We copy forward to Redshift
〉 Indexed data loaded to Druid
〉 Data access via Big Data API
PLATFORM
Data Stores

〉 Big Data Portal
〉 Tableau
〉 JS / React / Node Apps
Analytic Tools
PLATFORM
Big Data
Portal

〉 Big fast data
〉 Efficient compute
PROJECTS
WHAT WE DO

〉 Partner Ecosystem Dashboard
PROJECTS
Big Fast Data

PROJECTS
Partner Example - Trip to Manhattan

PROJECTS
Partner Example - Work & Home Again

PROJECTS
Partner Example - Multiple Partners

〉 This dashboard’s dimensional grain >1B rows
PROJECTS
Partner Ecosystem Dashboard
〉 Current solution is
〉 ETL in Pig
〉 Prepare Druid indexed dataset with Hadoop job
〉 Load to 100 historical nodes
〉 Queries are typically <2 seconds
〉 Solution v1 was Tableau + Redshift SSD
〉 Some views rendered in <10 seconds
〉 But multiple views with filters took >1 minute
〉 Future solution is...

〉 Counting things
〉 Measuring time
PROJECTS
Efficient compute

〉 Easy: playback events, units of time, other discrete events
〉 Hard: uniques, especially over windows or groups
PROJECTS
Efficient compute - Counting
〉 Solution: estimates, i.e. HyperLogLog(++)
〉 Where we’ll implement:
〉 Distribution arrays in staging tables
〉 In Druid data sources
〉 Merge functions in query layer, so UDFs for Pig/Hive/Presto/JS

〉 Easy: average, but not accurate
〉 Hard: percentiles
PROJECTS
Efficient compute - Measuring time
〉 Solution: estimates, most likely T-digest
〉 Where we’ll implement:
〉 Digests in staging tables
〉 In Druid data sources
〉 Merge functions in query layer, so UDFs for Pig/Hive/Presto/JS

THANK YOU
PEOPLE
WHO WE ARE
PLATFORM
HOW WE WORK
PROJECTS
WHAT WE DO

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Hadoop, streaming, terabytes, machine learning, batch, etc. Ces problématiques sous-tendent le déploiement d’une architecture Big Data en production. Mais si fondamentales soient-elles, qu’en est-il de l’utilisation de ces données ? Du point de vue de l’utilisateur, il s’agit de répondre à des questions comme : qu’y-a-t-il dans mes données ? Quel est le modèle numérique pertinent pour adresser mes questions métiers ? Mon modèle délivre-t-il la valeur attendue ? Comment partager cette valeur ? Ces questions partagent une même racine, à savoir, comment passer de Hadoop ou du data lake à un environnement de travail utile et utilisable sans se noyer ? C’est ce que nous vous proposons de découvrir dans cette session Nous naviguerons au travers de différents cas d’utilisation (exploration, interprétation, et communication des résultats) et découvrirons les architectures et les outils à notre disposition à même de nous ouvrir des horizons presque infinis : Superset, Tableau, PowerBI pour naviguer, des notebooks (Jupyter, Zeppelin, R) pour analyser, D3.js pour créer des visualisation personnalisées dans le browser. Pour cela, nulle raison de plonger dans un océan de données. Un sous ensemble modeste, un échantillon qui peut tenir dans la mémoire d’un laptop suffit dans la plupart des cas. On parle alors de data science, de data lab, mais surtout de data visualization. En se basant sur des cas d’utilisation, nous illustrerons ces différentes étapes et découvrirons ensemble comment faire sortir l’information de votre data lake pour l'amener sur votre écran.

big data et data viz - du lac à votre écran - afterwork

OCTO Technology Suisse

SQLSaturday #230 - Introduction to Microsoft Big Data (Part 1)

Sascha Dittmann

Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies. Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.

Hadoop meets Agile! - An Agile Big Data Model

Uwe Printz

Real time analytics

Leandro Totino Pereira

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big data et data viz - du lac à votre écran - afterwork

SQLSaturday #230 - Introduction to Microsoft Big Data (Part 1)

Hadoop meets Agile! - An Agile Big Data Model

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29. PROJECTS Partner Example - Work & Home Again

30. PROJECTS Partner Example - Multiple Partners

31. 〉 This dashboard’s dimensional grain >1B rows PROJECTS Partner Ecosystem Dashboard 〉 Current solution is 〉 ETL in Pig 〉 Prepare Druid indexed dataset with Hadoop job 〉 Load to 100 historical nodes 〉 Queries are typically <2 seconds 〉 Solution v1 was Tableau + Redshift SSD 〉 Some views rendered in <10 seconds 〉 But multiple views with filters took >1 minute 〉 Future solution is...

32. 〉 Counting things 〉 Measuring time PROJECTS Efficient compute

33. 〉 Easy: playback events, units of time, other discrete events 〉 Hard: uniques, especially over windows or groups PROJECTS Efficient compute - Counting 〉 Solution: estimates, i.e. HyperLogLog(++) 〉 Where we’ll implement: 〉 Distribution arrays in staging tables 〉 In Druid data sources 〉 Merge functions in query layer, so UDFs for Pig/Hive/Presto/JS

34. 〉 Easy: average, but not accurate 〉 Hard: percentiles PROJECTS Efficient compute - Measuring time 〉 Solution: estimates, most likely T-digest 〉 Where we’ll implement: 〉 Digests in staging tables 〉 In Druid data sources 〉 Merge functions in query layer, so UDFs for Pig/Hive/Presto/JS

35. THANK YOU PEOPLE WHO WE ARE PLATFORM HOW WE WORK PROJECTS WHAT WE DO

36. FINAL QUESTION BREAK ?!

Netflix Data Engineering @ Uber Engineering Meetup

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