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:

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 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.

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