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:

1. Extract trending TikTok content (videos, comments, challenges, collections)

2. Store data in a data warehouse (PostgreSQL or BigQuery)

3. Perform cleaning, transformation, and validation

4. Log operations and monitor failures

Tech Stack Summary:

2. Data Extraction (Extract Phase)

We use LuckData’s TikTok API to fetch data. Below is a sample for retrieving trending videos:

import requests
def 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 psycopg2
def 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 DAG
from 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, ValidationError
video_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.

Articles related to APIs :

• Free Ways to Access TikTok Data: From Official APIs to Third-Party Tools

• A Deep Dive into LuckData TikTok API: Making Social Data Scraping Easy

• TikTok Data Platform Architecture Design and Implementation Guide