This talk will teach you to finally integrate Django and Elasticsearch "like it's 2019".
Elasticsearch is a great addition to the Django developer's toolkit: it supports performant complex full-text queries and filters on huge datasets, where traditional relational database-only solutions fall short. But integrating Django with Elasticsearch usually is a pain: you need logic to keep database tables and Elasticsearch indexes in sync. Since data is stored in two places, it can become out-of-sync if care is not taken. Dirty index data will generate wrong search results, defeating the purpose of the integration.
A new alternative is [django- zombodb](https://github.com/vintasoftware/django-zombodb), a Django app that uses a Postgres extension for syncing tables with Elasticsearch indexes at transaction time. With django-zombodb, developers can treat an ElasticSearch index just like an internal Postgres index. This means no code is needed to synchronize Postgres with Elasticsearch, you just need to run a Django migration that executes a CREATE INDEX in the database and you're done. Any new inserts, updates or deletes on that model will reflect on an Elasticsearch index at transaction time!
django-zombodb also offers a Pythonic/Djangonic API to make Elasticsearch queries over Django models using the ORM in a queryset-friendly way. Developers are able to compose Elasticsearch queries with regular ORM queries by just chaining queryset methods and composing Q-like objects. In this talk, you'll learn django-zombodb advantages over other solutions, how it works, how to use it, and even you can contribute to it.