Bypassing Pandas Object Tax for 10M Rows with Axiom-CSV

If you’ve ever written a map or reduceByKey in 𝗣𝘆𝗦𝗽𝗮𝗿𝗸, you’ve likely used a Lambda function. But here is the catch: 𝗦𝗽𝗮𝗿𝗸 𝗘𝘅𝗲𝗰𝘂𝘁𝗼𝗿𝘀 𝗮𝗿𝗲 𝗝𝗮𝘃𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. 𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗻𝗼 𝗶𝗱𝗲𝗮 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗶𝘀 𝘀𝗮𝘆𝗶𝗻𝗴. 𝗦𝗼, 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗺𝗮𝗴𝗶𝗰 𝗵𝗮𝗽𝗽𝗲𝗻? 𝗟𝗲𝘁’𝘀 𝗽𝘂𝗹𝗹 𝗯𝗮𝗰𝗸 𝘁𝗵𝗲 𝗰𝘂𝗿𝘁𝗮𝗶𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲. 🛠 The Workflow: From Driver to Executor When you hit "Run" on your PySpark script, a fascinating multi-step process kicks off: 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗖𝗹𝗼𝘂𝗱𝗣𝗶𝗰𝗸𝗹𝗲: Your Python lambda functions (the logic inside your transformations) are serialized using a framework called CloudPickle. This turns your code into "pickled" data that can be moved across the network. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁: This pickled data is sent from the Driver (where your script lives) to the Executors (where the heavy lifting happens). 𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗪𝗼𝗿𝗸𝗲𝗿: Since the JVM Executor can’t execute Python logic directly, it spins up a separate Python process on the worker node. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: The data flows from the JVM to the Python worker, the lambda is "unpickled" and executed, and the results are sent back to the JVM. In the next post I will explain how Apache Arrow helps in removing this bottleneck of CloudPickle. #ApacheSpark #Spark #DataEngineering #Arrow #CloudPickle #Pyspark

🚀 New Webinar: Fabric Data Engineering with Python Notebooks 📅 April 2, 2026 | 12:00–1:30 PM EDT | Online If you’re building on Microsoft Fabric and looking to do more with less, this session is going to be a game‑changer. Python notebooks are quickly becoming the most cost‑efficient and flexible way to engineer data in Fabric—especially for small teams and organizations watching capacity consumption closely. In this webinar, we’ll explore how to design smarter pipelines using modern libraries like Polars, Delta Lake, DuckDB, and MS SQL, and how to evaluate cost tradeoffs using the Capacity Metrics app. 🎤 Speaker: John Miner Senior Data Architect at Insight Digital Innovation 10x Microsoft MVP | 30+ years of data engineering expertise John will walk through practical patterns, real‑world examples, and cost‑optimized design strategies you can apply immediately. 💡 You’ll learn: - Why Spark notebooks and Dataflows Gen2 can be more expensive than Python notebooks - How to build efficient ETL pipelines using modern Python data libraries - How to compare engineering designs using Fabric’s Capacity Metrics - How small companies can maximize value with minimal capacity 🔗 Register here: https://lnkd.in/dnm6irSM FutureDataDriven CloudDataDriven #microsoftfabric #dataengineering #python

🚀 Day 4/20 — Python for Data Engineering Reading & Writing Files (CSV / JSON) In data engineering, data rarely comes clean. 👉 It usually comes from: files logs exports APIs So the ability to read and write data is fundamental. 🔹 Why File Handling Matters We often: ingest raw data process it store cleaned output 👉 Python helps us do all of this easily. 🔹 Reading a CSV File import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 Loads structured data into a DataFrame 🔹 Reading a JSON File import json with open("data.json") as f: data = json.load(f) print(data) 👉 Useful for API responses and semi-structured data 🔹 Writing Data to a File df.to_csv("output.csv", index=False) 👉 Save processed data for further use 🔹 Where You’ll Use This Data ingestion pipelines Data transformation workflows Exporting results Logging and backups 💡 Quick Summary Python allows you to: read data from multiple formats process it write it back efficiently 💡 Something to remember Data engineering starts with reading data… and ends with writing it in a better form. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

I still remember the day our backend system crashed under 10 million rows of user data. It was 2 AM. The ETL pipeline was choking. My first instinct? Write more loops in Python. Big mistake. That's when I learned the hard way: raw Python loops don't scale. But Pandas and NumPy do. Here's what changed everything: Instead of iterating row by row, I switched to vectorized operations with NumPy. What took 45 minutes dropped to under 3 minutes. For data transformations, I started using Pandas apply() with axis parameters and groupby() aggregations instead of nested loops. Memory usage dropped by 60%. Three practices that saved our backend: 1. Specify dtypes upfront when reading CSVs. Loading only int32 instead of int64 cut memory in half for large datasets. 2. Use chunksize for massive files. Processing 50 million rows in 100k chunks kept our servers stable. 3. Convert categorical columns to category dtype. This single change reduced memory by 70% on dimension tables. The result? Our data pipeline now handles 50 million records daily without breaking a sweat. The lesson: Efficient data processing isn't about writing more code. It's about writing smarter code. What's your go-to optimization trick for handling large datasets? #Python #BackendDevelopment #DataEngineering #SoftwareEngineering

🚀 Day 20/20 — Python for Data Engineering Writing Production-Ready Python You’ve learned: data handling transformations pipelines automation big data (PySpark) Now comes the real difference: 👉 Writing code that works vs 👉 Writing code that lasts 🔹 What is Production-Ready Code? Code that is: reliable readable scalable maintainable 🔹 Key Practices 📌 1. Clean & Readable Code # Bad x = df[df["salary"] > 50000] # Good high_salary_df = df[df["salary"] > 50000] 📌 2. Error Handling try: df = pd.read_csv("data.csv") except Exception as e: print("Error:", e) 📌 3. Logging import logging logging.info("Pipeline started") 📌 4. Modular Code def load_data(): return pd.read_csv("data.csv") 📌 5. Avoid Hardcoding file_path = "data.csv" df = pd.read_csv(file_path) 🔹 Why This Matters Easier debugging Better collaboration Scalable systems Production reliability 🔹 Real-World Flow 👉 Write Code → Test → Deploy → Monitor 💡 Quick Summary Production-ready code = clean + reliable + scalable 💡 Something to remember Code that works is good… Code that lasts is professional. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 9/20 — Python for Data Engineering Working with Large Files (Memory Optimization) By now, we know how to read, write, and transform data. But in real-world scenarios… 👉 Data is not small 👉 Files can be GBs in size If we try to load everything at once → ❌ crash / slow performance 🔹 The Problem df = pd.read_csv("large_file.csv") 👉 Loads entire file into memory 👉 Not scalable 🔹 Solution: Read in Chunks import pandas as pd for chunk in pd.read_csv("large_file.csv", chunksize=1000): process(chunk) 👉 Processes data piece by piece 👉 Memory efficient 👉 Scalable 🔹 Another Approach: Line-by-Line with open("large_file.txt") as f: for line in f: process(line) 👉 Useful for logs and streaming data 🔹 Why This Matters Prevent memory issues Handle large datasets smoothly Build scalable pipelines 🔹 Where You’ll Use This Log processing Batch pipelines Streaming systems ETL workflows 💡 Quick Summary Don’t load everything at once. Process data in parts. 💡 Something to remember Efficient data handling is not about power… It’s about smart processing. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

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

I just killed 1,904 MB of RAM bloat with 40 lines of C. 🚀 I was testing Python's standard json.loads() on a 500MB log file today. 🛑 The Result: 3.20 seconds of lag and a massive 1.9GB RAM spike. For a high-scale data pipeline, that’s not just "slow"—that’s a massive AWS bill and a system crash waiting to happen. So, I built a bridge. By offloading the heavy lifting to the metal using Memory Mapping (mmap) and C pointer arithmetic, I created the Axiom-JSON engine. ✅ Standard Python: 3.20s | 1,904 MB RAM ✅ Axiom-JSON (C-Bridge): 0.28s | ~0 MB RAM That is an $11\times$ speedup and near-perfect memory efficiency. Stop throwing more RAM at your problems. Start writing better architecture. CTA: If your data pipelines are hitting a performance wall, DM me. I’m looking to help 2 teams optimize their compute costs this week. #SystemsArchitecture #Python #CProgramming #PerformanceEngineering #DataEngineering #CloudOptimization

Pandas is essentially Excel in Python — but way more powerful. Here's what you need to know: 📌 Two Core Data Structures: • Series — 1D, single column, homogeneous • DataFrame — 2D, multiple columns, heterogeneous 📌 Essential Operations Covered: • Importing CSV/Excel/SQL datasets • Indexing with .loc (label-based) & .iloc (position-based) • Data Cleaning — handling missing values with dropna() & fillna() • Removing duplicates with drop_duplicates() • Broadcasting — performing operations across entire columns • Joins & Merges — combining multiple datasets • Lambda & Apply — handling invalid values efficiently 📌 Pro Tip: Always use inplace=True if you want changes reflected in your original DataFrame! The best part? All of this with just a few lines of code. 🚀 Starting with a clean dataset is half the battle in Data Science. Master Pandas, and you're already ahead of the curve. #DataScience #Python #Pandas #MachineLearning #DataAnalysis

🚀 Day 12/20 — Python for Data Engineering Filtering & Selecting Data (Pandas) Now that we know what a DataFrame is… 👉 The real work starts here: getting only the data you need 🔹 Selecting Columns df["name"] 👉 Select a single column df[["name", "salary"]] 👉 Select multiple columns 🔹 Filtering Rows df[df["salary"] > 50000] 👉 Get rows based on condition 🔹 Multiple Conditions df[(df["salary"] > 50000) & (df["age"] < 30)] 👉 Combine conditions 🔹 Why This Matters Reduce unnecessary data Focus on relevant records Improve performance 🔹 Real-World Use 👉 Raw Data → Filter → Useful Data 💡 Quick Summary Selecting = columns Filtering = rows 💡 Something to remember You don’t need all the data… You need the right data. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks