ETL in Action: Building a Daily Automated TikTok Data Pipeline Using Python and Airflow
In today’s data-driven era, having real-time access to social media content is crucial for many businesses. TikTok, as one of the fastest-growing platforms in the world, offers an abundance of data. In this hands-on guide, we’ll demonstrate how to automate the collection of TikTok’s trending videos, comments, challenges, and collections using Python and the LuckData TikTok API. We’ll walk through building an end-to-end ETL (Extract, Transform, Load) pipeline that stores data in a PostgreSQL or BigQuery data warehouse, with built-in data validation and monitoring.
1. System Architecture Overview
This system is designed to execute the following daily tasks:
Extract trending TikTok content (videos, comments, challenges, collections)
Store data in a data warehouse (PostgreSQL or BigQuery)
Perform cleaning, transformation, and validation
Log operations and monitor failures
Tech Stack Summary:
Component | Technology |
---|---|
Language | Python 3.10+ |
Scheduler | Apache Airflow (or Cron) |
Data Source | LuckData TikTok API |
Data Warehouse | PostgreSQL / BigQuery |
Validation | JSONSchema / Custom Checks |
2. Data Extraction (Extract Phase)
We use LuckData’s TikTok API to fetch data. Below is a sample for retrieving trending videos:
import requestsdef get_hot_videos(count=20):
headers = {
'X-Luckdata-Api-Key': 'your_luckdata_key'
}
url = f"https://luckdata.io/api/tiktok-api/X2ZbQZ1YsWij?count={count}&cursor=0&keywords=trending"
response = requests.get(url, headers=headers)
return response.json()
Other endpoints you can use for comprehensive data extraction:
get comment list by video
get challenge info
get collection list by user id
get music post video list
3. Data Transformation (Transform Phase)
The raw JSON response must be flattened for easier storage:
def transform_video_data(raw_data):videos = []
for item in raw_data.get("data", []):
videos.append({
"video_id": item.get("id"),
"author": item.get("author", {}).get("unique_id"),
"description": item.get("desc"),
"create_time": item.get("create_time"),
"like_count": item.get("statistics", {}).get("digg_count"),
"comment_count": item.get("statistics", {}).get("comment_count")
})
return videos
This structure is more suitable for relational databases.
4. Data Loading (Load Phase)
Here’s how to insert the data into PostgreSQL:
import psycopg2def load_to_postgres(video_data, conn_params):
conn = psycopg2.connect(**conn_params)
cursor = conn.cursor()
for v in video_data:
cursor.execute("""
INSERT INTO tiktok_videos (video_id, author, description, create_time, like_count, comment_count)
VALUES (%s, %s, %s, %s, %s, %s)
ON CONFLICT (video_id) DO NOTHING
""", (
v['video_id'], v['author'], v['description'],
v['create_time'], v['like_count'], v['comment_count']
))
conn.commit()
cursor.close()
conn.close()
To load into BigQuery, use the google-cloud-bigquery
Python client.
5. Automation with Apache Airflow
We can orchestrate the entire ETL using Airflow:
from airflow import DAGfrom airflow.operators.python import PythonOperator
from datetime import datetime
from my_etl_scripts import get_hot_videos, transform_video_data, load_to_postgres
default_args = {
'owner': 'airflow',
'start_date': datetime(2024, 1, 1),
'retries': 1
}
with DAG('tiktok_etl_daily', default_args=default_args, schedule_interval='@daily') as dag:
extract = PythonOperator(task_id='extract', python_callable=get_hot_videos)
transform = PythonOperator(task_id='transform', python_callable=transform_video_data)
load = PythonOperator(task_id='load', python_callable=load_to_postgres)
extract >> transform >> load
This setup ensures the pipeline runs daily without manual intervention.
6. Data Quality Checks and Monitoring
Common data validation tasks include:
Ensuring required fields are not null
Confirming correct data types
Removing duplicates
Using jsonschema
, we can define a schema and validate records:
from jsonschema import validate, ValidationErrorvideo_schema = {
"type": "object",
"properties": {
"video_id": {"type": "string"},
"author": {"type": "string"},
"like_count": {"type": "integer"}
},
"required": ["video_id", "author"]
}
def validate_video(record):
try:
validate(instance=record, schema=video_schema)
return True
except ValidationError:
return False
Add logging and email notifications in Airflow for enhanced monitoring.
7. Extended Use Cases
This pipeline enables various real-world applications:
Marketing Intelligence: Track daily trending videos and hashtags
Competitor Analysis: Monitor content from specific influencers
User Segmentation: Combine video, music, and comment behavior for clustering
8. Conclusion
This guide walks you through building a fully automated ETL pipeline to extract, clean, and store TikTok data using LuckData APIs, Python, and Airflow. It can be easily scaled, extended to additional endpoints, or converted into a real-time processing architecture using tools like Kafka or GCP Dataflow.
As TikTok continues to rise as a powerful social platform, harnessing its data through a robust pipeline gives organizations the edge in marketing, trend forecasting, and audience analysis. Data isn’t just power—it’s direction.