DataMitraBI- Batch1 - SparkPart1

🚀 Microsoft Fabric Batch 1 | PySpark Part 1 – Hands-On Data Engineering for Beginners Welcome to *Day 2 of the DatamitraBI Microsoft Fabric Learning Batch!* 🎯 In this session, we begin our hands-on journey into **Apache Spark and PySpark in Microsoft Fabric**. We explore how to read data from a Fabric Lakehouse, work with Spark DataFrames, perform data transformations, handle data types, and write processed data back to the Lakehouse. This session is designed for beginners who want to understand PySpark practically and build a strong foundation in Microsoft Fabric Data Engineering. 📌 *WHAT YOU WILL LEARN IN THIS SESSION* ✅ *1. Microsoft Fabric Lakehouse Setup* Understanding the Fabric workspace and Lakehouse. Introduction to the Medallion Architecture and Bronze Layer. Organizing source and destination folders. Uploading CSV files to the Lakehouse. Connecting a Fabric Notebook to the Lakehouse. Understanding the difference between a Lakehouse and its SQL analytics endpoint. ✅ *2. Reading Data Using PySpark* Understanding Spark DataFrames. Reading CSV files using `spark.read`. Using `.format()`, `.options()`, and `.load()`. Displaying DataFrames using `display()` and `.show()`. Understanding column data types and schema inference. ✅ *3. Schema Inference vs. Explicit Schema* Why CSV columns may initially be interpreted as strings. Using `inferSchema` to detect column data types. Understanding the limitations of automatic schema inference. Defining an explicit schema to improve control over data types and avoid unnecessary schema inference. ✅ *4. Date Conversion and Data Type Handling* Understanding date-format issues in CSV files. Converting string-based dates into proper date values using `to_date()`. Using `col()` to reference DataFrame columns. Applying `withColumn()` to create or transform columns. Extracting date components using functions such as `dayofmonth()` and `dayofyear()`. ✅ *5. Selecting, Sorting, and Limiting Data* Selecting specific columns using `select()`. Understanding different ways to reference DataFrame columns. Sorting records using `orderBy()`. Using descending order to identify the highest values. Limiting the result set using `limit()`. ✅ *6. Grouping and Aggregating Data* Grouping records using `groupBy()`. Performing aggregations using `agg()` and `sum()`. Renaming aggregated columns using `alias()`. Understanding how grouping and aggregation work together. ✅ *7. DataFrame Transformations* Creating new columns using `withColumn()`. Applying multiple transformations to a DataFrame. Working with column expressions and functions. Understanding how to build readable, maintainable PySpark code. ✅ *8. Filtering and Handling Data* Applying conditions to filter DataFrame records. Working with column values and logical conditions. Exploring practical data manipulation scenarios. ✅ *9. Writing Data Back to the Lakehouse* Writing processed data in different file formats. Working with CSV and Parquet files. Understanding Spark write operations and save modes. Exploring `append`, `overwrite`, and `ignore` modes. Understanding common issues encountered while saving data. 💡 *KEY TAKEAWAY* By the end of this session, you will have a practical understanding of how to read, inspect, transform, aggregate, filter, and write data using PySpark in Microsoft Fabric. These concepts form the foundation for building scalable data engineering solutions using Lakehouses, Notebooks, and the Medallion Architecture. 🎯 *WHO SHOULD WATCH THIS VIDEO?* Beginners learning PySpark and Apache Spark. Power BI developers transitioning to Data Engineering. Microsoft Fabric learners. Data analysts looking to expand their Python and Spark skills. Aspiring Fabric Data Engineers. Professionals preparing for Microsoft Fabric and PySpark interviews. 📚 *ABOUT DATAMITRABI* DatamitraBI focuses on practical learning in Microsoft Fabric, Power BI, SQL, PySpark, and modern Data Engineering. Our goal is to help learners build real-world technical skills through hands-on demonstrations, practical examples, and interactive learning sessions. 🔔 *SUBSCRIBE FOR MORE CONTENT* Subscribe to the channel for more tutorials on Microsoft Fabric, PySpark, Power BI, Data Engineering, and Analytics. 👍 If you find this session helpful, please LIKE the video, SHARE it with other learners, and COMMENT with your questions. 📩 *Connect with Abhijeet Kumar* Microsoft Fabric | Power BI Architect | Data Engineering Coach #MicrosoftFabric #PySpark #ApacheSpark #DataEngineering #FabricLakehouse #SparkDataFrame #DatamitraBI #MedallionArchitecture #MicrosoftFabricTutorial #LearnPySpark