Mastering SQL Python PySpark in Small Steps Daily

SQLAlchemy 2.0 made querying simpler — but also more explicit. When working with Async SQLAlchemy in FastAPI, one important thing to understand is: 👉 Query building is synchronous 👉 Execution is asynchronous Most confusion comes from mixing these two. In this post, I’ve focused on the core query building patterns you’ll use daily: ✔️ select() — building the base query ✔️ where() — filtering data ✔️ join() — working with relationships And then executing them using: 👉 await session.execute() No unnecessary theory — just practical patterns that map closely to SQL. 📌 This is Part 1 of a series: Part 2 → Execution Layer Part 3 → Insert, update, delete + transactions If you're using FastAPI with PostgreSQL, this will make your ORM usage much clearer. 💬 Do you prefer explicit queries style like this, or more ORM abstraction like in the previous version? #sqlalchemy #fastapi #postgresql #python #backenddevelopment

You studied data for three years. You knew Python. SQL. How to build a model. You were ready. Then your first real brief arrived. Someone forwarded a spreadsheet. No context. No clean columns. No instructions. Just: “Can you tell us what’s happening here?” And you opened the file. The silence that follows that moment is something no course prepares you for. Not because the technical skills weren’t there. But because nobody had ever handed you a messy, incomplete, real-world problem and asked you to navigate it. That gap between what data education teaches and what data work actually demands is where most people lose confidence early. It’s not a skills gap. It’s an exposure gap. The professionals who close it fastest aren’t always the most technically gifted. They’re the ones who found someone who’d already been in that room and learned from them directly. #DataCareers #EarlyCareer #DataAnalytics #CareerDevelopment

Building your first data pipeline with Python + SQL is easier than you think. You don’t need complex tools to get started. Just the right flow 👇 1️⃣ Start with the connection Use Python to connect to your database: → SQLAlchemy → pandas Define your source and target tables clearly 2️⃣ Extract & Transform in one flow → Write a clean SQL query to extract data → Load it into a pandas DataFrame → Apply transformations (cleaning, joins, calculations) 3️⃣ Load & schedule → Use df.to_sql() to load data back → Wrap everything in a single .py file → Schedule it using cron (or Airflow later) That’s it. You’ve built your first pipeline using Python + SQL. Start simple. Focus on understanding the flow. Tools can come later. But many people struggle at this stage. They focus too much on tools, ignore the fundamentals, and underestimate SQL. This often leads to random learning, no clear structure, no preparation strategy… And when you’re stuck in that loop, having the right mentor can make a huge difference. That’s why, if you want to go deeper into building real-world pipelines, I recommend checking out Bosscoder Academy’s Data Engineering program. They focus on fundamentals, projects, and system-level thinking. 🔗 Check their program here: bcalinks.com/39Hf27EV Every advanced pipeline starts with a simple one. #DataEngineering #Python #SQL

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

I Tracked My Expenses Using Python & NumPy — Here's What ₹38,940 Taught Me About My Spending Habits I built a Personal Finance Tracker using just Python and NumPy — no Pandas, no fancy libraries. Here's what I discovered about my own spending 👇 The project started simple: a CSV file with 50 transactions across 3 months. But when I ran the numbers through NumPy, the insights hit different. What the data revealed: • Shopping eats 40% of my budget — with just 6 transactions • My Top 5 purchases alone = 36% of total spending • Average spend (₹779) vs Median (₹465) — proof that a few big buys skew everything • 56% of money goes to just 11 "high-tier" transactions What I actually built: → Read raw CSV data using Python's csv module → Converted everything to NumPy arrays for fast computation → Used np.sum(), np.mean(), np.max(), np.median(), np.std() → Boolean masking to filter by category & month → np.argsort() to rank top expenses → np.percentile() for distribution analysis → A formatted summary report printed right to the console. Key takeaway: You don't need complex tools to get powerful insights. NumPy + a CSV file + curiosity = real, actionable data about your life. Watch the screen recording below to see the full report output! This is Week 1 of my Python data journey. Next stop: Pandas & Matplotlib. #NumPy #DataAnalysis #PersonalFinance #LearningInPublic #PythonProjects #BuildInPublic #Python #DataScience #CodeNewbie #Programming #TechTwitter #DataDriven #100DaysOfCode #FinanceTracker

Swipe through the slides first 👉 then read below 👇 🚀 Day 17 of 30 — Learning PySpark from Scratch What if a built-in function doesn't exist for what you need? You write your own. That's a UDF. 🛠️ Here's what I learned on Day 17 👇 ⚡ What is a UDF? UDF = User Defined Function. A regular Python function that works as a Spark column function. from pyspark.sql.functions import udf from pyspark.sql.types import StringType def categorise_salary(salary):   if salary < 50000: return "Junior"   elif salary < 80000: return "Mid"   else: return "Senior" cat_udf = udf(categorise_salary, StringType()) df.withColumn("level", cat_udf(col("salary"))).show() ⚠️ The big warning about UDFs UDFs are slow. They break Spark's internal optimisation by serialising data to Python. → Native functions: stay in JVM = super fast → UDFs: data goes Python → JVM → Python = 10x slower Always check if when().otherwise() can replace your UDF first. 💻 The faster alternative # Instead of a UDF — use when() df.withColumn("level",  when(col("salary") < 50000, "Junior")  .when(col("salary") < 80000, "Mid")  .otherwise("Senior") ) ✅ 3 things I didn't know before today → Always specify return type in udf() — StringType(), IntegerType() etc. → pandas_udf (vectorised UDF) is much faster than regular UDF → UDFs can be registered for SQL use with spark.udf.register() 💡 My Day 17 takeaway UDFs are a tool of last resort. If a native function or when() can do the job — use that instead. ❓ Have you ever written a custom function for data transformation? Drop it in the comments 👇 Follow me for Day 18 tomorrow → Partitions and Performance tuning 🔔 #PySpark #DataEngineering #BigData #Python #LearnInPublic #30DaysOfPySpark

🧠 Python Concept: dataclasses (Clean Data Models) Write less boilerplate code 😎 ❌ Traditional Class class User: def __init__(self, name, age): self.name = name self.age = age def __repr__(self): return f"User(name={self.name}, age={self.age})" 👉 More boilerplate 👉 Repetitive code ✅ Pythonic Way (dataclass) from dataclasses import dataclass @dataclass class User: name: str age: int 👉 Automatically generates: __init__ __repr__ __eq__ 🧒 Simple Explanation Think of it like a shortcut ➡️ You define data ➡️ Python builds the rest 💡 Why This Matters ✔ Cleaner code ✔ Less boilerplate ✔ Easier to maintain ✔ Used in real-world apps ⚡ Bonus Example @dataclass class User: name: str age: int = 18 👉 Default values supported 😎 🧠 Real-World Use ✨ API models ✨ Config objects ✨ Data handling 🐍 Write less code 🐍 Let Python do the work #Python #AdvancedPython #CleanCode #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

🚀 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

Every new skill starts with confusion, errors, and a lot of debugging. Working with SQL connections and PostgreSQL through Python has been exactly that—figuring out errors, understanding why queries fail, and learning how databases actually function. But that’s also where the real learning happens. Step by step, I’m becoming more comfortable with querying data, managing connections, and thinking more logically about how information is stored and retrieved. It’s a process, but one that’s building a solid foundation for data analytics and future work in finance. #SQL #PostgreSQL #Python #DataAnalytics #DataScience #Finance #LearningByDoing #MittalschoolofBusiness #lpu

One Python expression, 22+ SQL dialects, zero rewrites 🐍 Running queries across multiple databases often means rewriting the same logic for each backend's SQL dialect. A query that works in DuckDB may require syntax changes for PostgreSQL, and another rewrite for BigQuery. Ibis removes that friction by compiling Python expressions into each backend's native SQL. Swap the connection, and the same code runs across 22+ databases. Key features: • Write once, run on DuckDB, PostgreSQL, BigQuery, Snowflake, and 18+ more • Lazy execution that builds and optimizes the query plan before sending it to the database • Intuitive chaining syntax similar to Polars 🚀 Article comparing Ibis with other libraries: https://bit.ly/3MnsHs7 #Python #DataScience #SQL