Automated ETL Pipeline for Social Text Processing and Analytics

Overview

This workflow automates the extraction, transformation, and loading (ETL) of Twitter data for advanced text processing and analytics. It integrates multiple platforms to streamline data collection, analysis, and reporting.

Key Features

• Automated Scheduling: Uses a cron trigger to run the workflow at defined intervals.
• Data Extraction: Collects tweets from Twitter based on specified criteria.
• Text Analysis: Processes tweet content using Google Cloud Natural Language for sentiment and entity analysis.
• Data Storage: Stores raw and processed data in Postgres and MongoDB databases for structured and unstructured data needs.
• Conditional Logic: Employs IF and NoOp nodes to manage workflow branching and error handling.
• Notifications: Sends real-time updates and alerts to Slack channels.

Benefits

• Time Savings: Eliminates manual data collection and analysis, reducing operational overhead.
• Improved Insights: Leverages NLP to extract actionable insights from social media content.
• Centralized Data: Ensures all relevant data is stored and accessible for further analytics or reporting.
• Seamless Integration: Connects cloud services, databases, and communication tools in one automated flow.

Use Cases

• Social media sentiment tracking for marketing teams
• Automated reporting of trending topics or brand mentions
• Enriching CRM or analytics platforms with real-time social data