ETL Pipeline Built with Python and SQL

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

🚀 **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

Python libraries every data analyst needs. The only Python libraries you need to start: 📊 pandas: data manipulation 📈 matplotlib + seaborn: visualization 🔢 numpy: numerical computing 📋 openpyxl: Excel automation 🔌 sqlalchemy: database connections That's it. Master these 5 and you can handle 90% of real-world analytics work. Don't get distracted by ML libraries until the basics are solid. #Python #DataAnalytics #DataTools #Pandas

🚀 “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()

🚀 Day 1/20 — Python for Data Engineering From SQL to Python: The Next Step After spending time with SQL, I realized something: 👉 SQL helps us query data 👉 But real-world data engineering needs more than that. We need to: process data transform data move data across systems That’s where Python comes in. 🔹 Why Python? Python helps us go beyond querying: ✅ Process data from multiple sources ✅ Build data pipelines ✅ Automate workflows ✅ Handle large datasets efficiently 🔹 Simple Example import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 From raw file → usable data in seconds 🔹 SQL vs Python (Simple View) SQL → Get the data Python → Work with the data Together, they form the foundation of data engineering. 💡 Quick Summary SQL is where data access begins. Python is where data engineering truly starts. 💡 Something to remember SQL gets the data. Python makes the data useful. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Time Series Analysis in SQL & Python — Real-World Challenges & Solutions Time series calculations in SQL can be surprisingly frustrating… At first, it feels simple — but once you start working on real business problems, things get tricky: When to use > vs >= Defining last 7 days vs last week correctly Identifying users who haven’t ordered in the last 30 days Rolling vs calendar-based calculations Even a small mistake in date logic can completely change your insights. While working with product and sales teams, I came across multiple such scenarios where accurate time-based logic was critical for decision-making. 👉 To organize my learning, I’ve created a small project where I’ve documented: Practical SQL time-based problems Clear and correct approaches Python (Pandas) validation using Jupyter Notebook 📂 I’ve shared: SQL queries Jupyter Notebook A quick reference guide on my GitHub: 👉 https://lnkd.in/gn5kg-xh I’ll continue adding more real-world tasks as I come across them while working on different use cases. 👉 Follow me for more practical tasks and insights like this. #SQL #Python #DataAnalytics #TimeSeries #DataScience #BusinessAnalytics #LearningInPublic #Analytics

I spent 2 hours cleaning data in Excel. My colleague did the same in 8 seconds. The difference? Python. Just 3 simple commands — One to load the file. One to remove duplicate rows. One to drop rows where key columns are empty. That's it. No formulas. No manual scrolling. No "find and replace" nightmares. Here's what most analysts don't realise → 60% of your time in Excel is spent on work Python can automate completely. That 60% is time you could spend on actual analysis. On insights. On decisions. On things that actually get you noticed. The 3 Pandas functions every analyst should learn first: → read_csv — loads your entire dataset in milliseconds → drop_duplicates — kills every duplicate row instantly → dropna — cleans empty rows in one shot Python isn't hard to learn. The hardest part is deciding to start. Are you already using Python in your workflow, or is Excel still your go-to? #Python #DataAnalytics #DataAnalyst #PandasPython #DataScience #ExcelVsPython #Analytics #CareerGrowth #TechSkills #Bengaluru

Stop loading massive datasets into memory and crashing your pipeline. 🛑 I used to load multi-gigabyte CSVs into Pandas, only to watch my memory usage spike to 100% and trigger an OOM kill. Switching to Python generators transformed how we handle large-scale data ingestion. Before (messy): import pandas as pd data = pd.read_csv("large_file.csv") for row in data.itertuples(): process(row) After (clean): import pandas as pd def stream_data(file_path): for chunk in pd.read_csv(file_path, chunksize=10000): yield from chunk.itertuples() for row in stream_data("large_file.csv"): process(row) Why this matters for data engineers: By processing data in chunks rather than loading the entire file, you keep your memory footprint constant regardless of file size. This allows your small containers to handle massive files without crashing. What is your go-to method for memory-efficient data processing in Python? #DataEngineering #Python #BigData #DataPipelines #SoftwareEngineering

Week 14(notes) Python Pandas Essentials for Data Analysis ✨ 🐍 Python + Pandas = Powerful Data Analysis some fundamental Pandas operations that every data analyst should know: 📌 1. View First Rows Use head() to display the first 5 rows of a dataset. df.head() 📌 2. View Last Rows Use tail() to display the last 5 rows. df.tail() 📌 3. Statistical Summary Get quick insights like count, mean, std, min, max using: df.describe() 📌 4. Select Single Column df['Name'] 📌 5. Select Multiple Columns df[['Name', 'Age']] 📌 6. Add New Column df['Salary'] = df['Age'] * 1000 📌 7. Basic Filtering Filter rows based on a condition: df[df['Age'] > 25] 💡 Pandas makes data cleaning and analysis fast, simple, and efficient. #Python #Pandas #DataAnalysis #Data #Aspiring #LinkedInLearning #100DaysOfCode #Analytics #CareerTransition #Techdatacommunity #LearningJourney.

Learning never stops. Over the last weeks we’ve been diving deep into Python, SQL, and NoSQL – building small projects, breaking things on purpose, and then fixing them again. It’s a great way to understand not only how to write queries and scripts, but also how data actually flows through real applications. Step by step, it’s starting to connect: Python for logic and automation, SQL for structured data, and NoSQL for flexible, modern workloads. Looking forward to turning this practice into real‑world projects soon. https://lnkd.in/dcPkK-hX #sql #nosql #python