Post by Dattaraj Bhoi

Working with Data Sources: Mastering File Formats & APIs in Python 🔗 Big thanks to Anurag Srivastava Sir for breaking down these essentials! Data engineers constantly juggle multiple data formats and sources. Today's lesson unlocked the fundamentals: CSV, JSON, text files, and APIs — and how to handle each in Python. Here's what you need to know: 📊 CSV Files Structured, tabular data. Best for relational datasets. Python's csv module or pandas.read_csv() makes parsing straightforward. Clean, organized, efficient. 🔀 JSON Files Nested, hierarchical data. The internet's lingua franca. Use json.loads() for strings, json.load() for file objects. Perfect for APIs and semi-structured data. Flexibility at its finest. 📝 Text Files Unstructured or minimally structured. Read with open(), process line-by-line. Essential for logs, raw documents, and custom data formats. The foundation of data. ⚡ APIs (Application Programming Interfaces) Real-time data delivery. Use requests library to hit endpoints, parse JSON responses, handle status codes. This is where you pull live data into your pipelines. The lifeblood of modern data engineering. 🔄 The Data Engineer's Workflow: Fetch from API → Parse JSON response → Transform → Store as CSV/database Each format has its place. Understanding when to use each and how to access each in Python is foundational to building reliable, scalable data pipelines. 🚀 What data source formats do you work with most? Drop your thoughts below! 👇 #DataEngineering #Python #DataXBootcamp #APIs #ETL #FileHandling #DataPipelines #Databricks #PySpark