Reading CSV JSON Excel Avro Files with Pandas

Built a simple Flight ETL pipeline using Apache Airflow, Docker, and Python. The pipeline: reads flight data from a CSV processes it through an Airflow DAG adds a delay flag (is_delayed) outputs structured data While building this, I ran into an issue where tasks would sometimes fail even though the code looked correct. It turned out to be a race condition — the transform step was trying to read the file before it was fully written. Fixing that gave me a much better understanding of how task dependencies and execution timing actually work in Airflow. Tech stack: Airflow · Docker · Python · Pandas GitHub: (https://lnkd.in/e_GPMMcr) #ApacheAirflow #ETL #DataEngineering #Python #Docker #DockerCompose #Pandas #WorkflowOrchestration #GitHubProjects

I just leveled up my Python skills by moving beyond temporary variables. This week was all about File I/O! Key highlights: ✅ Data Persistence: Learning to use open() and with blocks to save data permanently on my disk. ✅ Structured Data with CSV: Utilizing Python's csv module to handle complex data rows—turning simple scripts into organized data management tools. ✅ Scalability: No more re-entering data every time the program restarts. My torque calculations are now logged safely in .csv files for future analysis. #Python #FileIO #DataScience #CleanCode #CS50P #SoftwareEngineering

Row Count Validation Using Python Row count validation is one of the most basic but important ETL checks. Example in Pandas: len(df) or df.shape[0] This helps verify that the number of records in the source matches the target. However, remember: Row count validation alone is not enough. We also need to validate transformations and data quality. #ETLTesting #PythonAutomation #DataValidation

Currently revisiting SQL alongside Python 👨💻 I had learned SQL earlier, but like most people, I forgot many concepts. Now focusing on: SELECT, WHERE, ORDER BY GROUP BY Basic queries I can already see that SQL + Python together will be very powerful for Data Analytics. #SQL #DataAnalytics #LearningInPublic

Just finished building an ETL pipeline project in Python — reading, cleaning, and analyzing data from a multi-table bookstore dataset. The pipeline handles real-world data quality issues (missing values, invalid entries, orphan records), loads everything into SQLite, and runs reports using joins, CTEs, and window functions. Built with Python, pandas, and SQL as part of my data engineering learning path. UPDATED LINK here: https://lnkd.in/eBtEtXi3 #DataEngineering #Python #SQL #ETL

🚀 **SQL vs Python: Data Cleaning Cheat Sheet** Data cleaning is one of the most important steps in any data workflow. I came across this simple yet powerful cheat sheet that compares how to handle common data issues using both SQL and Python (Pandas). From handling missing values and duplicates to formatting data and detecting outliers — this visual makes it easy to understand both approaches side by side. 📌 A great quick reference for anyone working in Data Analytics or Data Engineering. 💡 Clean data = better insights = smarter decisions. #DataCleaning #SQL #Python #Pandas #DataAnalytics #DataEngineering #Learning #DataScience

🔄 ETL pipelines in Python — simplified A typical Python ETL pipeline looks like: 1️⃣ Extract → APIs / DB / Files 2️⃣ Transform → Pandas / PySpark 3️⃣ Load → Data warehouse / DB 💡 Pro tip: Always separate logic into reusable modules. Clean pipelines = maintainable systems. #ETL #Python #DataEngineering

🚀 “Data Science & Analytics Cheat Sheet – Quick Reference for Python, SQL & JS” Sections: Pandas (DataFrames & Series) import pandas as pd df = pd.read_csv('data.csv') df.head() df.describe() df.info() df['column'].value_counts() df.groupby('column')['column2'].mean()

Python has changed how analysts work. Tasks that used to take hours in Excel can now be automated in minutes using: • pandas • SQL integration • simple scripts Efficiency is becoming just as important as analysis.

⚔️ Pandas vs PySpark — When to use what? 🔹 Pandas Best for: Small to medium datasets Runs in-memory → super fast for local analysis 🔹 PySpark Best for: Large-scale distributed data Handles TBs of data across clusters 💡 Rule of thumb: If your system crashes → switch to PySpark 😄 Choosing the right tool = better performance + lower cost. #Python #PySpark #Pandas #BigData