26 min listen
Nathan Selikoff on Omnimodal's real-time tech stack
FromFrontend First
ratings:
Length:
88 minutes
Released:
May 8, 2019
Format:
Podcast episode
Description
Topics include:
4:23 – Overview of Omnimodal's tech stack
6:38 – Omnimodal's mission: to help cities manage transportation demand
16:10 – How to ingest open transportation data and present it in real time
21:43 – How graphics-heavy OpenGL and C++ apps can benefit from web tooling
31:06 – Why state machines are used in both video game and web development
34:55 – How JavaScript UI development compares to other paradigms
38:46 – Why Ember and Rails were chosen for Omnimodal's technology needs
42:09 – Using a prediction engine to improve on transportation schedules
44:56 - How Omnimodal gets data from its hardware trackers to the Rails server
50:55 – How services like Heroku and PubNub, custom AWS code, and the concept of a Data Lake help address scalability issues
56:40 – How deploys are coordinated across multiple services
59:47 - What the development process looks like for a multi-service tech stack
1:02:10 – What the complexity breakdown is between Omnimodal's frontend and backend
1:04:07 – Lessons learned on authentication while using Auth0
1:09:31 - Lessons learned on data modeling
1:12:21 – Tech choices, escape hatches, what's worked, and what hasn't
1:20:15 – Things Nathan loves about Ember, and things that are challenging
Links:
Nathan on Twitter
Omnimodal.io
PubNub
GTFS feed specification
Amazon Kinesis
Amazon ElastiCache
AWS AppSync
Auth0
4:23 – Overview of Omnimodal's tech stack
6:38 – Omnimodal's mission: to help cities manage transportation demand
16:10 – How to ingest open transportation data and present it in real time
21:43 – How graphics-heavy OpenGL and C++ apps can benefit from web tooling
31:06 – Why state machines are used in both video game and web development
34:55 – How JavaScript UI development compares to other paradigms
38:46 – Why Ember and Rails were chosen for Omnimodal's technology needs
42:09 – Using a prediction engine to improve on transportation schedules
44:56 - How Omnimodal gets data from its hardware trackers to the Rails server
50:55 – How services like Heroku and PubNub, custom AWS code, and the concept of a Data Lake help address scalability issues
56:40 – How deploys are coordinated across multiple services
59:47 - What the development process looks like for a multi-service tech stack
1:02:10 – What the complexity breakdown is between Omnimodal's frontend and backend
1:04:07 – Lessons learned on authentication while using Auth0
1:09:31 - Lessons learned on data modeling
1:12:21 – Tech choices, escape hatches, what's worked, and what hasn't
1:20:15 – Things Nathan loves about Ember, and things that are challenging
Links:
Nathan on Twitter
Omnimodal.io
PubNub
GTFS feed specification
Amazon Kinesis
Amazon ElastiCache
AWS AppSync
Auth0
Released:
May 8, 2019
Format:
Podcast episode
Titles in the series (100)
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