THE VIDEO SHELF · dynamodb data modeling
AWS re:Invent 2018: Amazon DynamoDB Deep Dive: Advanced Design Patterns for DynamoDB (DAT401)
Rick Houlihan's AWS re:Invent 2018 deep dive on advanced DynamoDB design patterns. It covers index overloading, GSI sharding, composite keys, graph processing and transactions.
Why HN readers recommended it
The go-to recommendation for learning DynamoDB data modeling, with many saying it made access-pattern-driven design click.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Amazon Web Services
- Topic
- dynamodb data modeling
- Format
- talk
- Level
- advanced
- HN recommenders in dataset
- 12
- HN mentions in dataset
- 16
- Recommendation years in dataset
- 2019–2025
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
If you go single table you can use filtered indexes if your database supports it to speed up queries instead of having to do a row by row search with a where clause. Personally this seems like a job for DynamoDB but that’s a whole …
gigatexal on Hacker News · 2019-09
In Designing database: split into different tables or group them together?AWS re:invent 2018 talks: [VIDEO] He has a sequence of 2-3 great talks on DynamoDB, the history of relational databases and the rise of access-pattern oriented db design.
tmsh on Hacker News · 2019-09
In Ask HN: Resources for Learning about DatabasesI've seen this done for new projects, and it works really well. If your data access patterns are truly relational (varied lookup paths) then it is probably not the right tool, but many apps can be modeled in a way that DDB handles well. Highly …
kbanman on Hacker News · 2020-03
In The good parts of AWS: a visual summary
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.