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Begin by visiting the TMDb website and creating an account if you haven't already. Once logged in, navigate to the API section to request an API key. This key will allow you to access TMDb's data programmatically. Store this key securely as it will be used to authenticate your requests.
Log in to the Google Cloud Console and create a new project. This project will house your Firestore database. Give your project a name and note the project ID, as you will need it for setting up Firestore and for authentication purposes.
Within your newly created Google Cloud project, navigate to the "Firestore" section under "Databases." Enable Firestore in Native mode for your project, which is optimized for mobile and web apps. This setup will allow you to store and manage your data with flexible querying capabilities.
On your local development environment, ensure you have Python installed. Install the required libraries using pip. You will need libraries like `requests` for API calls and `google-cloud-firestore` for interacting with Firestore. Use the following commands:
```
pip install requests
pip install google-cloud-firestore
```
Download the service account key from your Google Cloud project by navigating to "IAM & Admin" > "Service Accounts." Generate a new key and save the JSON file to your local machine. Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of this JSON file to authenticate your application:
```bash
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"
```
Write a Python script to fetch data from TMDb using their API. Use the `requests` library to send HTTP requests. For example, to get popular movies:
```python
import requests
api_key = 'your_tmdb_api_key'
url = f'https://api.themoviedb.org/3/movie/popular?api_key={api_key}'
response = requests.get(url)
data = response.json()
movies = data['results']
```
Use the `google-cloud-firestore` library to store the fetched TMDB data into Firestore. First, initialize the Firestore client and then create documents in a Firestore collection:
```python
from google.cloud import firestore
db = firestore.Client()
collection_ref = db.collection('movies')
for movie in movies:
doc_ref = collection_ref.document(str(movie['id']))
doc_ref.set(movie)
```
This guide should allow you to manually move data from TMDb to Google Firestore without relying on third-party connectors or integrations. Adjust the scripts as necessary to fit your specific data needs and structure.
FAQs
What is ETL?
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.
What is ELT?
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.
Difference between ETL and ELT?
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: