The journey toward a self-service data platform at Netflix - sf 2019

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The Netflix data platform is a massive-scale, cloud-only suite of tools and technologies. It includes big data tech (Spark and Flink), enabling services (federated metadata management), and machine learning support. But with power comes complexity. Kurt Brown explains how Netflix is working toward an easier, "self-service" data platform without sacrificing any enabling capabilities.

The journey toward a
self-service data platform

“Self Service?”
• Clear, easy, enabling
• Minimal (to no) coordination
• “Product mindset”
• “User-centric”

The Business
DVD
Streaming
International
Licensed Original
Original Production
Intl Original ProductionBy BlueMint – https://commons.wikimedia.org/wiki/File:Netflixenvelope.jpg Under CC BY 2.5 License – https://creativecommons.org/licenses/by/2.5/

Data Platform - Data
100+ PB
~10s Millions 1+ Trillion
34 TB

Data Platform - Team
80
ETL
Reporting
DBA
“Tools guy”
MLI, RTDI, UX
BD Compute
& Services,...
4

Principles / Approaches (MLI)
• Meet users where they are
• Simplify the integrations
• Building blocks

Principles / Approaches (Portal)
• Rich metadata
• Iterate
• Virtuous cycle
• Little things
• Buy yourself credibility
• Engineers != designers

import kragle as kg
trans_info = kg.transport.Transporter()
.source('metacat://prodhive/default/my_table')
.target('metacat://snowflake/test/demo_table')
.execute()
Kragle
(aka Big Data API)

Principles / Approaches (Code)
• Choose your own adventure
• Self-service != expertise-free
• Abstractions
• Heavy lifting handled

Jupyter (nteract) ecosystem
• Papermill
• Programmatic notebooks execution (w/params)
• Bookstore
• (Auto)save & version
• Commuter
• Share and Clone notebooks
• ….

Principles / Approaches (Notebooks)
• Weave it into your world
• Reimagine
• Extend ecosystem

Principles / Approaches (Meson)
• Do the work once
• Create a center of gravity
• Minimize cognitive load
• Don’t take away what’s working
• Go with (and to) the flow

Public Service Announcement
Don’t name it “that”!

“There are only two hard things in Computer
Science: cache invalidation & naming things.”
- Phil Karlton

Project Naming
• Clear is better than clever
• You hear it. You know what it is. (Great!)
• You hear it. Are told what it is. Association
cemented. (Good)
• It’s in a theme (Not so good)
• It’s, it’s…
• What does that tool do again?
• (Sad, typical reality)

Naming Tips
• What does it do?
• Let is soak awhile
• Delegate
• Don’t name it

Key Roles
• (Developer) education
• First responder
• Sub-team on-call
• New: Platform Partners

Principles / Approaches (Edu & Support)
• Need clarity (all around)
• Good at it / like it
• “Drinking your own champagne”
• Establish paved paths

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