Python Free workshop && Python Usage in Data Engineer projects-Part2 #python #azure

Python is a high-level, general-purpose programming language known for being: Easy to read and learn Cross-platform Open-source Extremely versatile That’s why it’s one of the most popular languages for data engineering, data science, web apps, AI, automation, and more. 🚀 Top Use Cases of Python Domain How Python Is Used Key Libraries / Tools 🧱 Data Engineering Build and automate ETL pipelines, read/write data from databases, APIs, cloud storage pandas, pyarrow, pyspark, airflow, boto3, azure-storage, sqlalchemy 📊 Data Analysis Analyze and visualize data numpy, pandas, matplotlib, seaborn, plotly 🧠 Machine Learning / AI Model training, prediction, NLP, image recognition scikit-learn, tensorflow, pytorch, transformers ☁️ Cloud / DevOps Automate cloud operations, manage infrastructure, CI/CD boto3 (AWS), azure-mgmt, google-cloud, paramiko, docker-py 🌐 Web Development Build web apps and APIs flask, django, fastapi ⚙️ Automation / Scripting Automate file management, Excel updates, log parsing, system tasks os, shutil, subprocess, openpyxl, schedule 📈 Data Visualization / BI Create dashboards and interactive visuals dash, streamlit, plotly 💬 APIs & Integration Consume or build REST APIs requests, flask, fastapi, aiohttp 🧪 Testing / QA Automated testing, CI/CD pipelines pytest, unittest, selenium 🧠 Why Data Engineers Love Python Python is the “glue language” of modern data engineering: Connects databases → cloud → analytics tools Handles structured (SQL) and unstructured data (JSON, logs, images) Integrates with Spark (PySpark) for big data Works inside Databricks, Airflow, Azure Synapse, AWS Glue, etc. Easy to debug, schedule, and version-control in pipelines Example — A simple ETL snippet import pandas as pd # Extract sales = pd.read_csv("s3://raw/sales.csv") # Transform sales['total'] = sales['price'] * sales['quantity'] # Load sales.to_parquet("s3://curated/sales_summary.parquet") That same concept can scale to millions of rows using PySpark. 🧩 Python in Azure & Databricks Azure Service How Python Is Used Azure Databricks PySpark notebooks, Delta Lake pipelines Azure Data Factory Custom Python scripts in pipelines Azure Functions Serverless Python APIs and triggers Azure Synapse Analytics Notebooks and data transformations Azure ML Model training, scoring, and deployment ⚙️ Python Strengths ✅ Simple syntax (like English) ✅ Huge library ecosystem (200K+ packages) ✅ Integrates with everything (SQL, APIs, cloud) ✅ Open-source and supported by all major clouds ✅ Excellent for automation and scripting ⚠️ Python Limitations ❌ Slower than compiled languages (like C++ or Java) for CPU-heavy loops ❌ Requires external libs for UI or mobile apps ❌ Needs environment management (venv, pip, conda) 💼 Real-World Data Engineer Example Workflows Task How Python Helps Ingest 10GB+ of data daily from APIs Use requests, pandas, asyncio Clean & join datasets Use pandas / pyspark Write to Azure Data Lake Use azure-storage-blob Build a Databricks job Use pyspark.sql + Delta Orchestrate pipelines Use Airflow DAGs in Python Validate data quality Use Great Expectations Deploy ML models Use mlflow + azureml