Jeena Stanly’s Post

In data engineering, one of the most important things is orchestrating data workflows i.e. ensuring tasks run automatically in the right order and at the right time. This is where Apache Airflow shines. At the heart of Airflow is something called a DAG, which stands for Directed Acyclic Graph. A DAG simply defines how workflow runs. It indicates: ✅ which tasks should execute ✅ in what sequence ✅ and how often. Each task in a DAG might represent something like: 🔹 Running a Python script 🔹 Moving data from one source to another 🔹 Transforming data with SQL or pandas Airflow makes it possible to define all of this in Python code, making your workflows automated, structured, and easy to monitor through its intuitive UI. Here is what makes DAGs powerful: ✅ They remove the chaos of manual runs ✅ They help you visualize task dependencies ✅ They ensure reliability with retries and scheduling ✅ They scale easily as workflows grow Every solid data pipeline starts with a well-structured DAG. It is the backbone of automation in Airflow. #DataEngineering #ApacheAirflow #Python #ETL #Automation #DataPipelines #WorkflowOrchestration #Data

SQL vs Pandas: Both are powerful for data analysis — just used differently 👇 🔹 SQL → Works best for querying large databases. 🔹 Pandas → Great for data manipulation in Python. Example: SQL: SELECT AVG(salary) FROM employees; Pandas: df['salary'].mean() Different tools, same goal — turning data into insights! 📊 #SQL #Pandas #Python #DataAnalytics #LearningEveryday

We’re excited to share our latest blog: “Python 3.14’s Free Threading Finally, Python Can Use All Your CPU Cores” on the Data OmnI Solutions site. Explore how Python 3.14 lifts traditional threading limits, enabling full CPU core utilization. Dive into real-world examples, performance gains, and how to level up your Python apps. Read it here: https://lnkd.in/dTjKd6JK #Python #Python314 #Concurrency #Threading #Performance #SoftwareEngineering

Stop Running Out of Memory! How to Write Memory-Efficient Data Processing Scripts in Python Just read an excellent article from Start Data Engineering that completely changed how I think about processing large datasets in Python. Here are the key takeaways: The Problem We've All Faced: Ever had your Python script crash with MemoryError while processing large CSV files or streaming data? I definitely have! Traditional approaches load everything into RAM - but there's a better way. The Game Changer: GENERATORS! Why Generators Rock for Data Engineering: - Lazy Evaluation: Process data row-by-row instead of all at once - Memory Efficient: Only one item in memory at a time - Faster Startup: Begin processing immediately without loading everything - Perfect for: ETL pipelines, log processing, large CSV/JSON files, and streaming data Other Memory-Saving Techniques Covered: - Chunking with Pandas: pd.read_csv(chunksize=10000) - Using efficient data types (int32 vs int64) - Context managers for proper resource cleanup - Database streaming with proper cursor management Credit & Further Reading: Big thanks to Start Data Engineering for the comprehensive guide! Check out the full article for detailed examples and benchmarks. https://lnkd.in/eGfdy9aa Your Turn: What's your favourite memory optimization technique? Have you faced memory issues in your data projects? Share your stories below! #Python #DataEngineering #BigData #ETL #DataProcessing #MemoryManagement #Generators #DataPipeline #CloudComputing #TechTips

This week, I’ve been working on SQL queries for different analytical use cases. I’ve focused on data filtering, grouping, joins, and query optimization — essential steps to prepare clean and meaningful data for dashboards and automated reporting systems. SQL becomes truly powerful when combined with Python and automation tools. #SQL #Python #DataAnalytics #Automation #DataEngineer

Today I revised some of my SQL concepts and practiced a few Python loops to strengthen my logic-building skills. Here’s a quick glimpse - SQL Practice: Created a View (vw_Category_Profit) using CTE + Subquery to calculate total revenue and total cost per category. Then built another query using two CTEs to calculate Total Profit and extract the Top 3 categories by profit. It’s amazing how much clarity comes when you connect concepts like CTEs, Views, and Joins together! - Python Practice: Nested for loops to print pattern combinations Practiced looping through two lists (Colors & Sizes) Wrote a while loop and a limited-attempt for loop to build simple user input validation logic Each day I try to connect what I know with what I learn new. SQL builds structure; Python builds logic — together, they’re the backbone of Data Analytics. #SQL #Python #DataAnalytics #LearningJourney #ContinuousLearning #CareerGrowth #LinkedInLearning #PracticeMakesPerfect #CTE #View #Loops

📊 Python Pandas for Data Analytics Python’s Pandas library provides a powerful foundation for handling data in analytics and data science workflows. From loading Excel or CSV files into structured DataFrames and Series, it enables efficient sorting, filtering with loc or iloc, and adding or renaming columns for clarity. The library allows users to group, aggregate, and merge datasets seamlessly while ensuring data quality through cleansing, handling missing values, and performing transformations with map, apply, or lambda functions. With advanced techniques like pivot tables, cross-tabulations, joins, and appending data, Pandas simplifies complex data blending and reshaping tasks into clear, actionable insights. cc : Digital Skola #Python #Pandas #DataTransformation #DataAnalytics #DataScience

1️⃣ Data Acquisition using Pandas Caption: 🚀 Exploring Data Acquisition with Pandas! Under the guidance of Prof. Ashish Sawant, I explored how to import and manage datasets efficiently using Python’s Pandas library. Data acquisition is the foundation of every data-driven project. I practiced reading data from various sources like CSV, Excel, JSON, and SQL. Also learned to inspect data using .head(), .info(), and .describe(). Clean and structured data is the first step toward meaningful analysis. This practical gave me a clear understanding of how data flows into the analytics pipeline. For more info,you can visit :- GitHub :-https://lnkd.in/edWY72Hg G drive:https://lnkd.in/ewkPtNtH #DataScience #Pandas #Python #DataAcquisition #LearningByDoing

🚀 New Project: Data Analysis in SQL using Pandas I’m excited to share my latest project where I performed data analysis using SQL-style queries within Python. For this project, I used a synthetic NHS dataset containing 100,000 records, which I cleaned earlier using Pandas to make it ready for analysis. This project is a continuation of my previous work on Exploratory Data Analysis (EDA) in Pandas — but this time, I focused more on the analytical and SQL aspects. Here’s what I did: 🔹 Used Pandas to run SQL-like queries in Python 🔹 Solved multiple real-world, scenario-based queries (like identifying trends, insights, and optimization cases) 🔹 Showcased how large datasets can be efficiently analyzed using SQL logic in Python 📺 YouTube Video: https://lnkd.in/dDYhV3_T I’ve also uploaded the complete code and dataset on my GitHub so anyone can try it out. 📂 GitHub: https://lnkd.in/dhyjBThH Always open to feedback, ideas, and collaborations! #Python #SQL #Pandas #DataAnalysis #NHSData #SyntheticData #DataScience #MachineLearning #PythonProjects #GitHub #LinkedIn #Analytics #Coding

Before Python 🐍 and R 📊 ruled the data world, one tool dominated data-driven decisions — SAS! From predictive analytics to business intelligence, SAS paved the way for smarter, faster, and accurate data insights. Want to master the tools that shaped the analytics universe? Start your Data Science journey today! 🚀 #DataScience #SAS #Analytics #DataDriven #Python #RStats #BusinessIntelligence #PredictiveAnalytics #DataInsights #MachineLearning #DataAnalytics #TechSkills