Ju R.’s Post

📰 An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling <p>In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing [&#8230;]</p> <p>The post <a href="https://lnkd.in/d96dTpxz">An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling</a> appeared first on <a href="https://lnkd.in/dAdcKkWg">MarkTechPost</a>.</p> 🔗 https://lnkd.in/d96dTpxz #أخبار_التقنية #ذكاء_اصطناعي #تكنولوجيا

I like using parquet files for storing and moving data around. It's great seeing lots of examples with it. Most data lake performance issues don't come from bad queries or slow engines. They come from how the data is physically laid out on disk. The article below walks through seven practical Parquet partition designs and when each one actually makes sense for real Python ETL workloads. It moves from simple date partitioning through multi-tenant layouts, hash bucketing, hot/cold splits, and versioned snapshots, each with clear folder structures, code samples, and honest trade-offs. It includes lots of good patterns matched to specific problems. Modexa's article is great for anyone building or maintaining data lake pipelines. If your team has ever battled tiny file storms or runaway scan times, this is a worthwhile read. https://lnkd.in/eeBWJcBd

In my last post, I introduced PydanTable—Pydantic-native tables, lazy transforms, and Rust-backed execution. Now, let's explore the next layer: SQL. Many data tools follow a familiar pattern for SQL sources: pulling rows into Python, transforming them, and then writing them somewhere else. While this approach works, it becomes cumbersome when dealing with large datasets or when your write target is the same database from which you read. The process of “extracting everything locally” can feel more like a burden than a benefit. PydanTable now offers an optional SQL execution path, allowing you to keep transformations within the database as long as the engine supports them. You only materialize data when you actually need it on the Python side. This shifts the paradigm from classic ETL—Extract, Transform locally, Load—to a more efficient TEL: Transform in SQL, extract locally when needed, then load. The primary advantage is operational efficiency. When your load target is on the same SQL server, you can often bypass the costly step of transferring the entire result set through the application, enabling a direct transition from transformation to loading, with the server handling the heavy lifting. This approach also indicates our future direction: a more intelligent execution strategy for PydanTable. The planner will optimize work on the read side when it is safe and efficient, selecting the best compute resources rather than defaulting to local resources or a single engine that may not be ideal for the task. On the roadmap, we have plans for a MongoDB engine to allow aggregation to remain on the server before extraction or writing back, as well as a PySpark-engine that introduces strong typing to traditional Spark-style operations. I am excited to continue advancing PydanTable beyond merely “strongly typed dataframes” toward strong typing where the data already resides. #DataEngineering #Python #OpenSource #SQL #ETL

Data quality checks across 10 columns = 10 queries? You don't need that. STRING_AGG handles everything in a single column 👇 Handling NULLs and inconsistencies can be a BIG headache in large datasets. But it doesn't have to be. Using STRING_AGG + UNION ALL + SUBQUERY, you can have all errors/ nulls pointed out in a single column. It works this way: 🔹Subquery: You place all the null/ sanity checks in the subquery 🔹UNION ALL: stacks all the errors by key (customers in the example below). In case of multiple columns with error, each customer will have multiple rows in the subquery table 🔹 STRING_AGG: Collapses the errors pointed out in the subquery into a single column. If there was only one error column it will bring it, if there was no error, it will be 'NULL' ⚠️NOTE: STRING_AGG may not work the same across engines. It's supported by engines like PostgreSQL, BigQuery and Redshift. While Sqlite uses GROUP_CONCAT, but the idea tends to be the same. ⚠️NOTE 2:The order of errors within the cell may vary depending on the database engine ⚠️ NOTE 3: This solution assumes no duplicate keys in your dataset. If duplicates exist, errors may repeat within the cell. Consider removing duplicates first. Which tricks do you use to ensure data quality? Leave it in the comments 👇 📌Save it and never waste time hunting down errors anymore. #SQLTips #DataAnalytics #DataScience #SQL #DataPipeline #DataQuality #DataEngineer #Python

🚀 Built an End-to-End Data Pipeline using API & SQL Server! Excited to share my recent hands-on project where I built a complete data pipeline from scratch 👇 🔹 What I did: 1. Source Database (SQL Server)   ↓ 2. Create API using FastAPI   ↓ 3. Expose endpoint (/data)   ↓ 4. Call API using Python (requests)   ↓ 5. Get data in JSON format   ↓ 6. Connect to Target SQL Server   ↓ 7. Auto-create table (if not exists)   ↓ 8. Insert data into target table   ↓ 9. Verify data in SSMS 🔹 Tech Stack: Python | FastAPI | SQL Server | pyodbc | requests 🔹 Key Learnings: 💡 How APIs act as a bridge between systems   💡 Converting JSON data into structured format   💡 Building real-world ETL pipelines   💡 Automating data movement without manual intervention  This project helped me understand how real-world data engineering pipelines work — from data extraction to loading 🚀 Looking forward to building more such projects and improving my skills! #DataEngineering #Python #FastAPI #SQLServer #ETL #DataPipeline #LearningInPublic #100DaysOfData #BuildingInPublic

💬 SQL Challenge of the Day 📝❓ Question Using the "Recursive CTEs" topic, write a SQL query to generate a Fibonacci sequence up to the 10th number. The Fibonacci sequence starts with 0 and 1, and each subsequent number is the sum of the two preceding numbers. 💡 Answer ```sql WITH RECURSIVE FibonacciCTE AS ( SELECT 0 AS n, 0 AS fib UNION ALL SELECT 1, 1 UNION ALL SELECT n + 1, CASE WHEN n = 0 THEN 0 WHEN n = 1 THEN 1 ELSE (SELECT fib FROM FibonacciCTE WHERE n = t.n - 1) + (SELECT fib FROM FibonacciCTE WHERE n = t.n - 2) END FROM FibonacciCTE t WHERE n < 10 ) SELECT fib AS Fibonacci_10th_Number FROM FibonacciCTE WHERE n = 9; ``` ✨ Explanation This query uses a recursive common table expression (CTE) to generate a Fibonacci sequence up to the 10th number. The CTE starts with the base cases of 0 and 1, then recursively calculates the Fibonacci numbers by summing the two previous numbers. The final result returns the 10th number in the Fibonacci sequence. 🛠️ Example (for ease of understanding) For the Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34 The query will output: ``` Fibonacci_10th_Number 34 ``` #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data

🚀 Excited to share my recent learning on ETL (Extract, Transform, Load)! Over the past few days, I’ve been exploring how ETL plays a crucial role in data analytics by enabling efficient data integration from multiple sources. ETL involves extracting raw data, transforming it into a clean and structured format, and loading it into systems for analysis and reporting. I also gained hands-on understanding of how ETL processes are implemented using tools and technologies like Python, SQL, and Excel for data cleaning, transformation, and pipeline creation. This process is essential for ensuring data quality, consistency, and reliability in real-world analytics workflows. Looking forward to applying these concepts in building efficient data pipelines and deriving meaningful insights from data. #DataAnalytics #ETL #DataEngineering #Python #SQL #LearningJourney

People ask me why I use PySpark when SQL can do most things. Honest answer - SQL does do most things. If I can write a clean SQL transformation that runs in under a minute, I'm not reaching for Spark. But there's a point where SQL stops being the right tool and you feel it immediately. Multi-step transformations where intermediate results are too large to join cleanly. Data quality logic that needs conditional branching across 15 columns. Deduplication across 50 million records where a window function in SQL is timing out. That's when PySpark earns its place. The thing about PySpark that takes time to actually internalize - it's lazy. You can chain 10 transformations and nothing has run yet. Spark is building a plan. It only executes when you force it - a write, a count, a show. What this means in practice: the line that throws the error is almost never where the problem is. The error surfaces at execution. The bug is three transformations back. I spent more time than I'd like to admit debugging the wrong line before I understood this properly. The way I work through it now - add a count() after each major transformation step while debugging. Force execution at each stage. Slow to run but you isolate the problem in one pass instead of five. PySpark rewards the engineers who understand what's happening underneath. It punishes the ones who treat it like fast SQL. #dataengineering #pyspark #spark #python #etl #dataengineer #awsglue

You won’t master SQL in a week. You won’t master Python in a week. You won’t master Snowflake in a week. You won't master ETL pipelines in a week. You won't master Data Modelling in a week. But everyone starts from 0. That Senior Data Engineer that makes +$300k a year? He started from 0. Here are little steps that you can do: 𝗦𝗽𝗲𝗻𝗱 𝟯𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝗱𝗮𝘆 𝘄𝗶𝘁𝗵 𝗦𝗤𝗟 → CTEs → Joins → Syntax → Aggregations → Window functions 𝗦𝗽𝗲𝗻𝗱 𝟮 𝗵𝗼𝘂𝗿𝘀 𝗼𝗻 𝘁𝗵𝗲 𝘄𝗲𝗲𝗸𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 → Pandas → Functions → Automation → JSON to CSV → Writing to databases 𝗦𝗽𝗲𝗻𝗱 𝟭 𝗵𝗼𝘂𝗿 𝗽𝗲𝗿 𝘄𝗲𝗲𝗸 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 → ETL vs ELT → Stream or Batch? → Monitoring and Error Handling → Data lake, Data Mart or Data Warehouse? → How to handle slowly changing dimensions (SCD) 𝗦𝗽𝗲𝗻𝗱 𝘁𝗶𝗺𝗲 𝘀𝘁𝘂𝗱𝘆𝗶𝗻𝗴 𝗽𝗲𝗼𝗽𝗹𝗲 𝟯 𝘆𝗲𝗮𝗿𝘀 𝗮𝗵𝗲𝗮𝗱 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂 → Where did they start? → How did they make it? → What skills they have that I'm missing? --- At the beginning, you will see no progress. And it's the most difficult part. But... look at this: 1.00^365 = 1.00 1.01^365 = 37.7 A little daily effort will make the difference in a year. Doing nothing = staying the same. Doing a little bit more each day = exponential growth! Start learning TODAY! --- ♻️ Repost if this motivated you To Learn 👉𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 -->𝐅𝐑𝐄𝐄 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 & 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥 𝐭𝐨 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐚𝐭 𝐁𝐢𝐠 𝐌𝐍𝐂'𝐬: https://lnkd.in/di7ZmRyn