How to load data from TMDb to Convex

Learn how to use Airbyte to synchronize your TMDb data into Convex within minutes.

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Set up a TMDb connector in Airbyte

Connect to TMDb or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Convex for your extracted TMDb data

Select Convex where you want to import data from your TMDb source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the TMDb to Convex in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync TMDb to Convex Manually

How to Sync TMDb to Convex Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

TMDb is a community built movie and TV database. The Movie Database (TMDb) is a well known, popular, user editable database for movies and TV shows. TMDb.org, which is a crowd-sourced movie information database used by many film-related consoles, sites and apps, like XBMC, Myth TV and Plex. The Movie Database (TMDb) is a database of TV shows and movies which permits users to edit data. Since 2008, the users have been editing and adding the data through TMDb.

The TMDb (The Movie Database) API provides access to a wide range of data related to movies and TV shows. The following are the categories of data that can be accessed through the TMDb API:  

- Movie data: This includes information about movies such as title, release date, runtime, budget, revenue, genres, production companies, and more.
- TV show data: This includes information about TV shows such as title, air date, episode count, season count, networks, genres, and more.
- People data: This includes information about people involved in movies and TV shows such as actors, directors, writers, and producers.
- Keyword data: This includes information about keywords associated with movies and TV shows such as plot keywords, genres, and more.
- Collection data: This includes information about collections of movies such as franchises, trilogies, and more.
- Review data: This includes information about reviews of movies and TV shows such as user ratings and reviews.
- Image data: This includes images related to movies and TV shows such as posters, backdrops, and stills.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up TMDb to Convex as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from TMDb to Convex and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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Connectors Used

TMDb
Convex

Related Syncs with TMDb

TMDb to Convex
TMDb to RabbitMQ
TMDb to Typesense
TMDb to Weaviate

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How to load data from 

TMDb to Convex