Introduction
Welcome to an exciting journey into the world of Reddit data engineering! 🚀 This report explores how to harness the power of various tools and technologies to analyze subreddit data effectively. Whether you're a data enthusiast or a seasoned engineer, this guide will inspire you to dive into the depths of Reddit data and uncover valuable insights.
Summary
This report delves into the analysis of the 'r/dataengineering' subreddit using advanced data processing techniques. By leveraging the Pushshift API, PMAW, asyncpraw, and DuckDB, data is efficiently gathered and stored. The integration with BigQuery and dbt enables comprehensive NLP analysis, culminating in a dynamic dashboard that highlights top submissions by topic.
Data Gathering and Storage
The first step in our journey involves gathering data from Reddit using the Pushshift API and PMAW. This allows us to pull data from any subreddit efficiently. We then use asyncpraw to retrieve submission and comment data asynchronously, ensuring a smooth and fast data collection process. The data is stored in DuckDB, which is perfect for handling upserts and managing large datasets. Here's a glimpse of the code that makes this possible:
# Example code snippet for data retrieval
async def fetch_data(subreddit):
# Use asyncpraw to gather data
pass
For more details, check out the GitHub repository.
Data Upload and Modeling
Once the data is stored in DuckDB, the next step is to upload it to BigQuery for further analysis. This is where dbt comes into play, allowing us to model the data and perform NLP analysis. The integration with BigQuery facilitates complex queries and data manipulation, enabling us to extract meaningful insights from the subreddit data. Here's a snippet of how we handle data uploads:
# Example code snippet for data upload
async def upload_to_bigquery(data):
# Upload data to BigQuery
pass
Explore the detailed implementation in the flows_api_to_bq.py file.
Dashboard and Analysis
The final piece of the puzzle is the creation of a dynamic dashboard that showcases the top submissions by topic. By using TF-IDF analysis, we identify relevant topics and score submissions based on comments and upvotes. This dashboard provides a visual representation of the data, making it easy to interpret and act upon. Here's a sneak peek at the dashboard setup:
# Example code snippet for dashboard setup
def create_dashboard(data):
# Visualize data
pass
For more insights, visit the utils_.py file.
Conclusion
In conclusion, this project showcases the seamless integration of multiple technologies to analyze Reddit data. By using Pushshift API, PMAW, asyncpraw, and DuckDB, we efficiently gather and store data. The subsequent analysis with BigQuery and dbt provides deep insights into subreddit topics, empowering users to make data-driven decisions. The dashboard serves as a testament to the power of modern data engineering techniques.