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Presented by DataDrooler’s Data Engineering Workshop.
https://datadrooler.com/
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Table of Contents
AI is evolving rapidly, and businesses need to stay updated on the latest trends and breakthroughs. However, AI news is scattered across multiple sources and is often unstructured, making manual tracking time-consuming and difficult to analyze.
We need to build an automated solution that collects, cleans, and stores AI news in a structured database. This will eliminate the manual work and make the data accessible for analysis.
Create a DAG that automates the process of fetching AI news from various sources, cleaning the data, and storing it in a structured format in a database.
By automating this workflow, data scientists will have clean, ready-to-analyze data for tasks like trend analysis, sentiment analysis, market forecasting, and identifying emerging AI technologies. This will save time, provide actionable insights, and improve decision-making for the business.