dbt-gizmosql — a month of new capabilities Six releases shipped for dbt-gizmosql, the dbt adapter for GizmoSQL (an Apache Arrow Flight SQL engine backed by DuckDB). The headline: the adapter now supports a lot of things it just... didn't before. → Python models (brand new!) Write dbt transformations in Python. dbt.ref() / dbt.source() pull upstream tables as Arrow; return a DuckDB relation, pandas DataFrame, or PyArrow Table and the result is shipped back to the server via ADBC bulk ingest. Incremental Python models supported too. → session.remote_sql() — server-side pushdown for Python models Python models run client-side, so dbt.ref('big_table') streams the whole upstream table across the wire before your code sees it. The new remote_sql() escape hatch runs SQL directly on GizmoSQL and returns only the result — the filter executes server-side: def model(dbt, session): schema = dbt.this.schema return session.remote_sql( f"select * from {schema}.big_table where name = 'Joe'" ) → External materialization — write straight to files A new 'external' materialization issues a server-side COPY to Parquet, CSV, or JSON. Anywhere the server's DuckDB can reach: local disk, s3://, gs://, azure://, MinIO. Supports partitioning, codecs, and format inference. The result is ref()-able downstream. → Microbatch incremental strategy Time-windowed incrementals via dbt's microbatch strategy — reprocess a recent event_time window each run, with automatic batching. → Snapshot merge rewritten around MERGE BY NAME Snapshots now use DuckDB's native MERGE ... UPDATE / INSERT BY NAME — more robust to column reordering, far clearer than a hand-rolled merge. → Much faster seed loading Seeds are now read with DuckDB's CSV reader (correct null handling, proper type inference) and bulk-ingested as Arrow via ADBC instead of row-by-row INSERTs. A shout-out to ADBC None of this would be practical without ADBC (Arrow Database Connectivity). It gives the adapter a columnar, zero-copy path to the server: seeds and Python-model results ship as Arrow record batches, ref() pulls land as Arrow tables, and remote_sql() streams Arrow results back. It's the reason the adapter can move real data volumes without the usual row-by-row ODBC/JDBC tax. Huge thanks to the Apache Arrow community and the adbc-driver-flightsql maintainers. And finally — the best GizmoSQL features come from the user community. GizmoData thanks our users for their great feedback and engagement! pip install dbt-gizmosql https://lnkd.in/ewKGMUCe #dbt #duckdb #dataengineering #apachearrow #adbc #flightsql

Free GitHub Repos to Practise Real Data Cleaning Messy. Imperfect. Exactly like real work. Most tutorials hand you clean datasets. Actual jobs don’t. For Complete Beginners NamanGupta001 / Messy-Dataset-Cleaning A single, extremely messy dataset packed with missing values, bad date formats, numeric columns full of symbols, and plenty of typos. Perfect first project. https://lnkd.in/dBMPrFdu Python Data Cleaning sumony2j / Data_Cleaning_Preprocessing Multiple datasets cleaned using core Python libraries. Covers the full range of issues you’ll face in real roles. https://lnkd.in/dedtqMxe pb111 / Data-Cleaning-with-Python-and-Pandas A comprehensive walkthrough of cleaning techniques with NumPy and Pandas: missing values, outliers, duplicates, untidy structures, and more. https://lnkd.in/dDhVKeUk Multi‑Tool Projects (Python, SQL, Excel) eyowhite / Messy-dataset HR and healthcare datasets cleaned across Python, Excel, SQL, and Power BI. Great for seeing how different tools solve the same problem. https://lnkd.in/dYmEbifF ojalp26 / Cleaning-dirty-data-samples Eight messy datasets cleaned using Power Query in Excel—sales, customers, hospital data, invoices. Very close to what you’ll see in real files. https://lnkd.in/dGhCaD6h Dirty Data Challenges OxfordIHTM / messy-data Real Ministry of Health datasets including vaccine studies and population data. Harder than typical practice sets because they come from actual government systems. https://lnkd.in/d6w9yJpU Advanced Cleaning (Text, Dates, Edge Cases) misszeferino / Nashville-Housing-Data-Cleaning A well-known SQL project covering date standardisation, address parsing, NULL handling, and duplicate removal. https://lnkd.in/dw8X4qAC underthecurve / pandas-data-cleaning-tricks Real-world messy datasets with documented Python tricks for handling edge cases once you’ve mastered the basics. https://lnkd.in/dbcW5rku If you want to get better at data work, start by getting good at cleaning messy data. Save this for your next project! #DataAnalytics #DataCleaning #DataScience #Python #SQL #PowerBI #Excel #Pandas #PortfolioProject #Github

Most data engineers lint their Python. Nobody lints their SQL. You've got flake8 on every commit, formatters on your notebooks, type checkers on your APIs. But the SQL that actually touches production data? That gets eyeballed in a PR and merged with a "looks good to me." I started running SQLFluff on a client's dbt project almost by accident. Someone mentioned it in a thread, I added it to the pre-commit config, and within a week I couldn't imagine working without it. It's open source. It supports most major warehouses - Redshift, BigQuery, Snowflake, Postgres - and has a dbt templater that actually handles Jinja SQL without losing its mind. Basic setup takes minutes. Tuning it for your team's conventions takes a bit longer, but that's time well spent. What it catches isn't dramatic. Inconsistent casing across models. Ambiguous joins that pass review because everyone reads them differently. Implicit column references that'll break the next time someone touches the schema. None of it is catastrophic on its own. But messy SQL accumulates. And accumulated mess is where bugs hide. The real value isn't even the linting itself. It's that your team stops arguing about SQL style in pull requests. The linter decides. You move on. And when you onboard someone new, they don't have to reverse-engineer your conventions from 50 different models - the rules are in the config. Run it in CI and it prevents style drift over time, not just during review. That matters more as your team grows. We treat SQL like it's somehow exempt from the standards we apply to every other language in the stack. It shouldn't be. It's the language closest to your data, and it deserves at least the same rigor as the Python wrapping it. SQLFluff is free. There's no reason not to try it.

I take a university class 1 night/week. Next week's homework had a bonus task: create Medallion architecture using Spark. I wrote some Python, started a spark session, and copied a SQL data warehouse to parquet. From raw files (bronze), with transforms (silver), and finally some dims and facts (gold). When I write Python, I like to make it modular and reusable. So I tend to write functions with parameters. But what do you do when you get to the silver layer? every project will have wide variations. This means writing unique functions for every table transformation. I am also not a big fan of typing. So I edited my SQL <-> DAX translator to SQL - > Spark. It's pretty nifty. Point and click for bronze table, and silver table goal. Click conversion button. AI analyzes the sql schemas and converts to spark syntax for transformation from bronze to silver. Then click Copy. Now there's portable code to insert into a silver.py. There may be times it has to be tweaked a little. Not an issue, the AI leaves notes. The tables have to exist, but don't necessarily need to have data. This could be very useful in migrations. I don't think it saves much on figuring out the spark syntax, it just reduces typing lol. I suppose if you don't know Spark it could be helpful. But take a look at one of the lines: F.col("id").cast("int").alias("ID") all it really means is take column "id", make it an integer, and rename it "ID". It's definitely not "English" as we know it but it's not completely foreign and this sort of operation is easy to pick up. I may make a "rolodex" for python apps, if I can figure it out. I am slowly creating a bunch of useful AI tools. I don't normally have repetitive tasks- like receiving Excels or sending them out. Or sending regular emails. No major everyday repetitiveness. So I usually fall back on a small library of python scripts I made, that I think of as my swiss army knife (collection). Because out of several hundred there's one that can usually be used for whatever I am doing. Well, now they're evolving into AI tools. They should go in a toolbox or rolodex. I know, most people would put them on their startbar or start menu.

🚨 𝗦𝗺𝗮𝗹𝗹 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁, 𝗕𝗶𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 𝗶𝗺𝗽𝗼𝗿𝘁 * 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 A lot of PySpark code works perfectly in notebooks… but fails to scale in production for small reasons like this 👇 👨💻𝗠𝗼𝘀𝘁 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝘄𝗿𝗶𝘁𝗲: from pyspark.sql.functions import * ✔️ It works ❌ But this small shortcut creates long-term problems in real data pipelines ⚠️ 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗺𝗽𝗼𝗿𝘁 * 👉When you use import *, you’re pulling 100+ functions into your global namespace That leads to: • ❓ Function ambiguity — where is this function coming from? • ⚠️ Overriding built-ins like sum(), max(), min() • 🐛 Harder debugging as pipelines grow • 👀 Poor readability in shared codebases 📌𝗘𝘅𝗮𝗺𝗽𝗹𝗲: sum([1,2,3]) 🤔 Is this Python’s sum or Spark’s aggregation? 👉 In large projects, this confusion is not theoretical 👉 It causes real production bugs ✅ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: from pyspark.sql import functions as F 📌 𝗨𝘀𝗮𝗴𝗲: F.col(“salary”) F.sum(“salary”) F.when(…) ✔️ Same output 🔥 Much better code quality 🔍 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 In small notebooks → both approaches feel identical But in production 👇 • 📏 Pipelines are hundreds of lines long • 👥 Multiple engineers contribute • ⚡ Debugging must be fast & precise ⚖️ 𝗡𝗼𝘄 𝗰𝗼𝗺𝗽𝗮𝗿𝗲: df.groupBy(“dept”).agg(sum(“salary”)) vs df.groupBy(“dept”).agg(F.sum(“salary”)) 💡The second version instantly tells you: 👉 This is a Spark transformation 👉 Not a Python function 👉 Not a custom utility 🧠 That clarity = faster debugging + cleaner reviews + easier maintenance ⚙️ 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 • ☁️ Azure Databricks jobs (scheduled ETL pipelines) • 🔄 Incremental loads & complex transformations • 👨💻 Team-based code reviews • 🚨 Debugging production failures 👉 When something breaks…you don’t want to guess where a function came from 🧠𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗲𝘄𝗲𝗿 𝗸𝗲𝘆𝘀𝘁𝗿𝗼𝗸𝗲𝘀 It’s about: • 🎯 Clear ownership of logic • 🔍 Predictable behavior • 🧱 Maintainable pipelines 🔥 𝗦𝗵𝗼𝗿𝘁 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝗯𝗲𝘁𝘁𝗲𝗿…𝗖𝗹𝗲𝗮𝗿 𝗰𝗼𝗱𝗲 𝗶𝘀. 💬 What do you use in your projects — import * or F? #DataEngineering #PySpark #AzureDatabricks #BigData #ETL #CodingStandards #Spark

🚨 𝗦𝗺𝗮𝗹𝗹 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁, 𝗕𝗶𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 𝗶𝗺𝗽𝗼𝗿𝘁 * 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 A lot of PySpark code works perfectly in notebooks… but fails to scale in production for small reasons like this 👇 👨💻𝗠𝗼𝘀𝘁 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝘄𝗿𝗶𝘁𝗲: from pyspark.sql.functions import * ✔️ It works ❌ But this small shortcut creates long-term problems in real data pipelines ⚠️ 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗺𝗽𝗼𝗿𝘁 * 👉When you use import *, you’re pulling 100+ functions into your global namespace That leads to: • ❓ Function ambiguity — where is this function coming from? • ⚠️ Overriding built-ins like sum(), max(), min() • 🐛 Harder debugging as pipelines grow • 👀 Poor readability in shared codebases 📌𝗘𝘅𝗮𝗺𝗽𝗹𝗲: sum([1,2,3]) 🤔 Is this Python’s sum or Spark’s aggregation? 👉 In large projects, this confusion is not theoretical 👉 It causes real production bugs ✅ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: from pyspark.sql import functions as F 📌 𝗨𝘀𝗮𝗴𝗲: F.col(“salary”) F.sum(“salary”) F.when(…) ✔️ Same output 🔥 Much better code quality 🔍 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 In small notebooks → both approaches feel identical But in production 👇 • 📏 Pipelines are hundreds of lines long • 👥 Multiple engineers contribute • ⚡ Debugging must be fast & precise ⚖️ 𝗡𝗼𝘄 𝗰𝗼𝗺𝗽𝗮𝗿𝗲: df.groupBy(“dept”).agg(sum(“salary”)) vs df.groupBy(“dept”).agg(F.sum(“salary”)) 💡The second version instantly tells you: 👉 This is a Spark transformation 👉 Not a Python function 👉 Not a custom utility 🧠 That clarity = faster debugging + cleaner reviews + easier maintenance ⚙️ 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 • ☁️ Azure Databricks jobs (scheduled ETL pipelines) • 🔄 Incremental loads & complex transformations • 👨💻 Team-based code reviews • 🚨 Debugging production failures 👉 When something breaks…you don’t want to guess where a function came from 🧠𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗲𝘄𝗲𝗿 𝗸𝗲𝘆𝘀𝘁𝗿𝗼𝗸𝗲𝘀 It’s about: • 🎯 Clear ownership of logic • 🔍 Predictable behavior • 🧱 Maintainable pipelines 🔥 𝗦𝗵𝗼𝗿𝘁 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝗯𝗲𝘁𝘁𝗲𝗿…𝗖𝗹𝗲𝗮𝗿 𝗰𝗼𝗱𝗲 𝗶𝘀. #PySpark #ApacheSpark #DataEngineering #BigData #DataEngineer #Spark #ETL #PythonBestPractices #CodeQuality #CleanCode #CodeReadability #SoftwareDesign #CodingStandards #Debugging #CodeSmells #TechDebt #MaintainableCode #BugPrevention #CodeReview #ProductionReady #ScalableSystems #DataPipeline #DataArchitecture #CloudEngineering #Databricks #Programming #Coding #SoftwareEngineering #CloudComputing #AI #MachineLearning #Analytics #PySpark #ApacheSpark #DataEngineering #BigData #CleanCode #PythonBestPractices #CodeQuality #CodeReadability #Debugging #DataPipeline #SoftwareEngineering #CloudComputing #Databricks #ETL #ScalableSystems

Even the best AI model is useless if the database is down or the data is corrupted. 👨💻 I recently asked myself: 1. How do web systems actually access a database? 2. Can a programmer access or modify a database without writing raw SQL? In my journey, I discovered how SQLAlchemy and Alembic are used to manage databases seamlessly using Python. Here is the breakdown of the core stack: SQLAlchemy: An Object-Relational Mapper (ORM) that translates our Python objects into database table formats. Alembic: Think of this as 'Git' specifically designed for database schemas. It tracks changes and allows us to upgrade or downgrade our table structures (adding/removing columns) safely. Psycopg2: The engine's driver used by both Alembic and SQLAlchemy to send queries to the database. It acts as the essential transporter. Pydantic: A powerful library used for data validation. It acts as a strict gatekeeper—if a system requires an integer and the user sends a string, Pydantic catches the error immediately, even before the code executes. There are two distinct workflows used to communicate with the database: 1. For creating or modifying the Schema (The Blueprint): This workflow manages the structure: Alembic -> SQLAlchemy -> Psycopg2 -> Database 2. For adding, retrieving, or removing Data: This workflow manages the actual records: Python Code -> SQLAlchemy -> Psycopg2 -> Database Key Note: Alembic only creates versions for the database schema (the structure), not the actual data stored inside it.

## 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝗮𝗽: 𝗦𝗤𝗟 𝘁𝗼 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 🐍📊 Navigating the world of data often involves working with both SQL and Python. Understanding how to translate common SQL operations into Python can significantly streamline your data analysis and manipulation workflows. This quickstart guide offers a handy reference for common tasks, from filtering and ordering data to handling missing values and merging datasets. 𝗞𝗲𝘆 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻𝘀: • 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴: `WHERE column = 'value'` → `df[df['column'] == 'value']` • 𝗢𝗿𝗱𝗲𝗿𝗶𝗻𝗴: `ORDER BY column ASC` → `df.sort_values(by='column', ascending=True)` • 𝗥𝗲𝗺𝗼𝘃𝗶𝗻𝗴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀: `SELECT DISTINCT col1, col2` → `df.drop_duplicates(subset=['col1', 'col2'])` • 𝗙𝗶𝗹𝗹𝗶𝗻𝗴 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗩𝗮𝗹𝘂𝗲𝘀: `COALESCE(col, 'xxx')` → `df['column'].fillna('xxx')` • 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲𝘀: `CAST(col AS INTEGER)` → `df['column'].astype(int)` • 𝗥𝗲𝗻𝗮𝗺𝗶𝗻𝗴 𝗖𝗼𝗹𝘂𝗺𝗻𝘀: `SELECT col AS new_col` → `df.rename(columns={'col': 'new_col'})` • 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻𝘀: `SUM()`, `AVG()`, `MIN()`, `MAX()`, `COUNT()` → `.sum()`, `.mean()`, `.min()`, `.max()`, `.count()` • 𝗠𝗲𝗿𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀: `JOIN` → `pd.merge(table1, table2, on='key')` • 𝗔𝗽𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀: `UNION ALL` → `pd.concat([table1, table2])` Mastering these translations can unlock greater efficiency and flexibility in your data projects. What are your favorite SQL to Python translation tips? Share them in the comments below! 👇 ♻️ Repost if you find it helpful #SQL #Python #DataAnalysis #DataScience #DataEngineering #Programming #Coding #Pandas

✅ *Top Python Interview Q&A - for Data Science Roles* 🌱 *1️⃣ What is Pandas and why use it?* Pandas is Python's most popular library for data analysis and manipulation. It provides DataFrames (Excel-like tables) and Series (columns). Perfect for cleaning, transforming, analyzing CSV/Excel data. ``` import pandas as pd df = pd.read_csv('sales.csv') # Load data print(df.head()) # First 5 rows print(df.shape) # Rows, columns ``` *2️⃣ How do you load a CSV file into Pandas?* Use pd.read_csv(). Most common data source in interviews. Handles large files efficiently. ``` df = pd.read_csv('data.csv') # Common options: df = pd.read_csv('data.csv', sep=';', encoding='utf-8', nrows=1000) ``` *3️⃣ What is the difference between DataFrame and Series?* DataFrame = table (rows + columns) Series = single column DataFrame has 2D structure, Series is 1D. ``` df = pd.DataFrame({'A': [1,2], 'B': [3,4]}) # DataFrame series = df['A'] # Series print(type(df)) # <class 'pandas.core.frame.DataFrame'> print(type(series))# <class 'pandas.core.series.Series'> ``` *4️⃣ How do you check basic info about DataFrame?* Use info(), describe(), head(), tail(), shape, columns. Essential for data exploration. ``` df.info() # Data types, memory, missing values df.describe() # Stats (mean, std, min, max) print(df.head(3)) # First 3 rows print(df.shape) # (1000, 5) print(df.columns) # Index(['name', 'age', 'city']) ``` *5️⃣ How do you select single column from DataFrame?* Use df['column_name'] or df.column_name (if no spaces). Returns Series. ``` df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]}) names = df['name'] # Series ages = df.age # Same Series print(names[0]) # Alice ``` *6️⃣ How do you filter rows based on condition?* Use boolean indexing. Most common data selection method. ``` # Age > 25 high_age = df[df['age'] > 25] # Multiple conditions adult_male = df[(df['age'] > 18) & (df['gender'] == 'M')] ``` *7️⃣ How do you add a new column to DataFrame?* Simple assignment. Creates column with same length as rows. ``` df['bonus'] = df['salary'] * 0.1 # 10% bonus df['high_earner'] = df['salary'] > 50000 # Boolean column df['name_length'] = df['name'].str.len() # String length ``` *8️⃣ How do you sort DataFrame by column?* Use sort_values(). ascending=False for descending. Common for ranking. ``` # Sort by salary (descending) df.sort_values('salary', ascending=False, inplace=True) # Multiple columns df.sort_values(['department', 'salary'], ascending=[True, False]) ``` *9️⃣ How do you check for missing values?* isnull().sum() gives count per column. Critical first step in data cleaning. ``` print(df.isnull().sum()) # age 5 # salary 0 # city 10 print(df.isna().sum()) # Same as isnull() ```

🔥 Topic: Python 📄 Title: Stop Profiling Data Manually — Auto-Generate It Instead 🚨 Problem You receive a new data source from a Finance client. How many nulls does each column have? What are the min, max and mean values? Are there duplicates hiding in the primary key? You write the same exploratory queries every single time. In Consulting — you do this for every new client. Every single project. Manual data profiling is the most repeated and most skipped step in analytics. 🛠️ Solution Auto-generate a full data profile report from any CSV or SQL source using Python: • Row count, null count and null percentage per column • Min, max, mean and distinct value counts automatically • Duplicate detection on any key column • Exported as a clean Excel report ready to share with stakeholders One script. Every new data source profiled in seconds. 📊 Example import pandas as pd df = pd.read_csv("client_data.csv") profile = pd.DataFrame({ "Column": df.columns, "DataType": df.dtypes.values, "RowCount": len(df), "NullCount": df.isnull().sum().values, "NullPct": (df.isnull().mean() * 100).round(2).values, "Distinct": df.nunique().values, "Min": df.min(numeric_only=False).values, "Max": df.max(numeric_only=False).values, }) duplicates = df.duplicated().sum() print(f"Duplicate rows detected: {duplicates}") profile.to_excel("data_profile.xlsx", index=False) print("Data profile generated successfully") Every column. Every quality metric. Every duplicate flagged. Full profile exported and ready before the first stakeholder meeting. ✅ Result ⚡ Any data source fully profiled in under 10 seconds 🧠 Null counts, duplicates and ranges caught before modelling begins 🔒 Consistent quality checks across every Consulting and Finance project 📊 Profile report shared with stakeholders before questions are even asked #Python #DataEngineering #DataQuality #ETL #DataPipelines #Automation #DataAnalytics #PowerBI #FinancialReporting #ConsultingLife #UKTech #HiringUK #LondonData #Analytics