🚀 Databricks & PySpark Full Course | Master Big Data Processing from Scratch

Welcome to the Databricks & PySpark full-course tutorial! If you're looking to become a Data Engineer, Data Analyst, or Big Data Expert, this video is for you. We take a hands-on approach to mastering Databricks using PySpark, covering everything from basics to advanced concepts.

This is your one-stop guide to understanding how PySpark works inside Databricks, with real-world examples and practical demonstrations.

💡 Why Learn Databricks & PySpark?

Fast & Scalable: Apache Spark is 100x faster than traditional big data tools.
Easy to Use: PySpark makes working with big data simple & efficient.
Industry Demand: Databricks and Spark are widely used in AI, ML, and Data Engineering.

Cloud-Ready: Works with AWS, Azure, and Google Cloud.

🔥 What You’ll Learn in This Course
📌 Step 1: Loading & Understanding Data
✅ Reading CSV files in Databricks
✅ Checking schema and data types

📌 Step 2: Data Cleaning & Transformation
✅ Renaming columns for better clarity
✅ Converting categorical values into numerical format
✅ Handling NULL values using fillna(), dropna()

📌 Step 3: Working with DataFrames
✅ Using filter(), sort() operations
✅ Performing column operations like withColumn(), alias(), and cast()

📌 Step 4: Advanced PySpark Functions
✅ Using explode(), collect_list(), pivot(), when(), otherwise()
✅ String functions: initcap(), upper(), lower()
✅ Date functions: current_date(), datediff(), date_add(), year(), month()

📌 Step 5: Joins & Data Merging
✅ Inner, Left, Right & Outer Joins
✅ Union & UnionByName for combining datasets

📌 Step 6: Window Functions & Ranking
✅ Using rank(), dense_rank(), and cumulative sum()
✅ Partitioning and ordering data efficiently

📌 Step 7: User Defined Functions (UDFs)
✅ Writing custom functions in PySpark
✅ Applying UDFs to transform and clean data

📌 Step 8: Writing & Saving Data
✅ Writing datasets in CSV, JSON, ORC, and Delta formats
✅ Overwriting, appending, and handling errors while saving

📌 Timestamps -
00:00:00 - Intro
00:00:28 - A] Agenda
00:00:51 - B] Data Understanding
00:02:05 - C] Compute Creation
00:02:43 - D] Data Ingestion
00:03:14 - E] Folder & Notebook Creation
00:05:04 - F] Data Reading
00:11:39 - G] Data Cleaning & Transformation
00:11:45 - 1. Column Name Rename
00:14:44 - 2. When - Otherwise, Col, Lit
00:20:08 - 3. WithColumn, regexp_replace, Col
00:22:41 - 4. FillNa
00:25:42 - 5. Select, Alias
00:28:25 - 6. Filter
00:30:16 - 7. Sort
00:32:18 - 8. DropDuplicates
00:33:42 - 9. Select, Initcap, Lower, Upper
00:36:19 - 10. DropNa
00:41:37 - 11.FillNa - Another Example
00:43:58 - 12. Drop Column
00:45:01 - 13. Joins - Inner, Left, Right, Outer
00:56:07 - 14.Union & UnionByName
01:01:23 - 15. Date Functions
01:13:33 - 16. Array
01:16:08 - 17. Explode
01:18:44 - 18. Collect_List
01:22:58 - 19. Count
01:24:49 - 20.PIVOT
01:30:07 - 21. When-Otherwise
01:33:15 - 22. Window - Rank & Dense Rank
01:40:05 - 23. Cumulative Sum
01:44:51 - 24.User Defined Function
01:54:58 - 25. Data Export with different modes & different formats
02:05:09 - Conclusion

📌 Dataset Used: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists

📌 Relevant Videos:
1. From Data to Business Insights: PySpark on Databricks for Amazon Prime Dataset Analysis 📊🚀 - https://youtu.be/7aZGAf8Luys?si=c7N4eVkX07DYva2_

2. Databricks Journey Begins: Compute, Catalog, Workflows, Data Management, and More! - https://youtu.be/4qreAFJfID4?si=my-Y7qD69SfAS-zP

3. Apache Spark & Databricks: Lazy Evaluation| Fault Tolerance| DAG| Catalyst Optimizer(Theory) - Part 4 - https://youtu.be/12IDOqhsv2w?si=Dfxi2WZZkzUdaRoe

4. Spark & Databricks: RDDs| DataFrames| Datasets| Spark Ecosystem| RDD Operations (Theory) - Part 3 - https://youtu.be/5Ckap52tuHk?si=hbPYC7U4zXHeYx7l

5. Spark & Databricks - Spark Architecture |Memory Management |Application Workflow (Theory) - Part 2 - https://youtu.be/T6CGh-R9C84?si=koVHbkD2Cks9z2w_

6. Introduction to Apache Spark | Databricks (Theory) - Part 1 - https://youtu.be/lbFax1jxSec?si=WNWL7nhon8mJ-Wmf

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