Logging & Monitoring in Python Data Pipelines

🚀 Day 10/20 — Python for Data Engineering Logging Basics So far, we’ve been writing code that runs… But in real-world data pipelines: 👉 You need to track what’s happening 👉 You need to debug issues later That’s where logging comes in. 🔹 What is Logging? Logging is recording events that happen while your program runs. 🔹 Why Not Just Use print()? print("Data loaded") 👉 Works for small scripts 👉 But not useful in production 🔹 Using Logging Module import logging logging.basicConfig(level=logging.INFO) logging.info("Data pipeline started") logging.warning("Missing values detected") logging.error("File not found") 🔹 Log Levels INFO → General updates WARNING → Something unexpected ERROR → Something failed 🔹 Why Logging Matters Track pipeline execution Debug failures easily Monitor production systems 🔹 Real-World Use 👉 Data pipeline starts → logs events → errors captured → easy debugging 💡 Quick Summary Logging helps you: understand what your code is doing identify problems quickly 💡 Something to remember If your pipeline fails and you don’t know why… you don’t have logging. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

Python for data engineering is not the same as Python for data science. Two completely different skill sets that share a language. Data science Python: pandas, matplotlib, sklearn, notebooks, exploratory work. Data engineering Python: PySpark, asyncio, subprocess, logging, retries, unit testing, packaging, Dockerfiles. The DE-specific habits I've built: → Every utility function has a unit test (pytest + mocking) → Secrets come from environment variables — never hardcoded → All scripts accept CLI arguments (argparse or Typer) → Logging not print statements (structured logs with JSON output) → Dependencies pinned in requirements.txt and containerized Gulf tech companies running modern data stacks want DE engineers who write production Python — not notebook Python. Do you write production-grade Python? What's your standard? #Python #DataEngineering #PySpark #BestPractices #DubaiTech

Presenting the latest block of code which I have been working on. It's a rolling window for real-time sensor data. I have recently been exploring advanced Python concepts and came across deques. Deques are short for double ended queues and they allow developers to add and remove elements from both ends efficiently. If you are a beginner or intermediate Python developer, you come across lists, dictionaries and tuples as data types which can be used to store data. One of the biggest issues with using lists is what happens to the configuration of data inside a list when an item is removed. When you remove the first item in a standard Python list, Python physically shifts every single remaining element down in memory. As a result of this physical shifting, removing the first item of a standard list has a time complexity of O(N). In contrast, a deque handles this operation in O(1) time, which is why we are able to remove objects instantaneously regardless of how large the rolling window gets. Real World Scenario: In the following block of code I simulate the storage of data from a live chemical reactor which holds information for Temperature (K), Pressure (atm), Concentration of species A (mol/L). By using this deque-based buffer with a running sum, we can efficiently calculate a rolling average to smooth out high-frequency noise in real-time. Check out the demo video! #Python #Engineering #DataScience #ChemicalEngineering #SoftwareDevelopment #ChemicalEngineer #opentowork

🚀 Journey to Becoming a Data Scientist — Day 13 Today I continued the Intermediate Python phase of my roadmap. I learned focusing on Logic, Control Flow, and Filtering using NumPy. 📚 What I learned today • Understanding comparison operators (>, <, ==, !=) • Working with Boolean values (True / False) • Using logical operators (and, or, not) • Applying logical operations on NumPy arrays • Using np.logical_or() for element-wise comparison • Combining multiple conditions to filter data 💡 Key takeaway When working with datasets, we often need to filter data based on conditions. NumPy logical operations like logical_or() allow us to apply conditions across entire arrays efficiently, which is very useful in data analysis. 📊 Example use case Filtering data where: • Condition 1 OR Condition 2 is true This helps in selecting relevant data for further analysis. Thanks to DataCamp for the hands-on exercises. #DataScienceJourney #Python #NumPy #DataScience

🐍 Day 2/30 — Python for Data Engineers Lists & Tuples. These two will follow you everywhere. In my 3 years as a Data Engineer, barely a day passed without using either of these. Here's what I wish someone told me on Day 1: Lists = Dynamic. You'll append rows, filter tables, and loop through pipeline stages. Tuples = Fixed. Every DB record you fetch comes back as a tuple. The one mistake beginners always make 👇 one = (42) ❌ # this is just an int one = (42,) ✅ # THIS is a tuple And the thing that makes Python lists actually powerful: List Comprehension — transform data in one line: active = [t for t, ok in all_tables if ok] That single line replaces 5 lines of for-loop code. 📌 Full cheat sheet in the image — save it for your daily reference. Day 3 tomorrow: Dictionaries & Sets 🔑 Follow Jaswanth Thathireddy if you're learning Python for Data Engineering 👇 #Python #DataEngineering #30DaysOfPython #LearnPython #DataEngineer

This week’s edition is live! Watch a step-by-step Python webinar where you build an interactive food ordering app using dictionaries, loops, and functions. Then focus on building real skills with 30 data science projects, 20 guided data analyst projects, and a hands-on RAG system tutorial built from scratch. You’ll also find community insights on improving the business impact of your projects, why ML engineers need data engineering skills, clustering best practices, and a new collaboration opportunity, along with practical reads on running Claude Code for free and using it more effectively. Read the full edition: https://buff.ly/K7ShTOB

Most small businesses lose hours every week updating data manually. ⏳ I recently built a reliable Python pipeline that handles the heavy lifting: ✅ Fetches data directly from APIs ✅ Cleans data & removes duplicates ✅ Stores everything in a structured PostgreSQL database ✅ Updates automatically every day No more manual copy-paste. No more messy spreadsheets. 🚫📊 This is a game-changer if you deal with: • Growing Excel files that crash constantly • API data that needs daily manual updates • Repetitive, boring reporting tasks If this sounds familiar, I can help you automate your workflow and reclaim your time. 🚀 Check out the Demo & Code here: 👇 https://lnkd.in/dyXCXSPk #DataAutomation #Python #ETL #SmallBusiness #Automation

Most people consume content about data engineering. Very few build. 674 contributions in the last year. Consistent across October through March, no dead months, no gaps. Not because every day was productive. Because the habit of showing up compounds faster than any course ever will. If you're trying to break into data engineering, close the tutorial. Open a terminal. Build something broken, fix it, commit it, repeat. Your GitHub is either evidence or silence. Make it evidence. What's stopping you from committing something today? #DataEngineering #Python #GitHub

Day 3 | The Art of Data Transformation 🏗️ Python for Data Science: Why Type Casting is Your First Line of Defense 🐍 In Data Science, your models are only as robust as the data you feed them. Real-world datasets are often "dirty"—numbers arrive as strings, and mismatched types can break a production pipeline. Today, I explored Type Casting and Data Conversion, the essential tools for ensuring data integrity before analysis begins. Key Technical Insights : Explicit Type Casting: Mastering int(), float(), and complex() to force raw data into the correct numeric format for accurate computation. The Logic of Truth (bool): Understanding Python’s internal "Truthiness"—where any non-zero or non-empty value is True, while 0, 0.0, and empty sequences are False. Memory Efficiency with range(): Utilizing sequence generation that is immutable and highly memory-efficient—a must-have for large-scale iterations. Binary Data Management: Differentiating between bytes (immutable) and bytearray (mutable) for handling raw data streams. Data Integrity (Mutability vs. Immutability): Identifying which objects can be modified in place and which are protected from accidental changes in memory. I've realized that Type Casting isn't just a coding trick; it is a critical form of Data Validation. By mastering these fundamentals, we build resilient Machine Learning pipelines that don't fail when they encounter unexpected formats. Immense gratitude to my mentor, Nallagoni Omkar Sir, for the deep technical clarity and structured guidance that made these concepts second nature. Next Milestone: Powering up with Python Operators! 🚀 #Python #DataScience #DataEngineering #TypeCasting #LearningInPublic #JuniorDataScientist #MachineLearning #ProgrammingFundamentals #CleanCode #NeverStopLearning

🔷 Data Cleaning Pipeline Project I recently developed a structured and scalable data cleaning pipeline using Python, designed to transform raw datasets into analysis-ready data with improved quality and consistency. The pipeline follows a systematic workflow: • Data Inspection: Understanding dataset structure and data types using .info() • Statistical Analysis: Generating descriptive statistics to uncover initial patterns • Missing Value Handling: Identifying and treating null values efficiently • Duplicate Removal: Ensuring data integrity by eliminating redundancies • Outlier Detection: Detecting and managing anomalies in the dataset • Correlation Analysis: Evaluating relationships between variables for deeper insights 🌐 Live Application: https://lnkd.in/dr9DXfPA 💻 Source Code: https://lnkd.in/dKyQUZpc This project highlights the importance of robust data preprocessing in building reliable data-driven solutions and reflects my ability to design clean, reproducible data workflows. I look forward to applying these techniques to more advanced analytics and machine learning projects. #DataAnalytics #DataScience #Python #DataCleaning #DataPreprocessing #MachineLearning #GitHub #Streamlit