Learn This Concept to be proficient in PySpark.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- PySpark Architecture
- SparkContext and SparkSession
- RDDs (Resilient Distributed Datasets)
- DataFrames
- Transformations and Actions
- Lazy Evaluation

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Reading Data from CSV, JSON, Parquet
- DataFrame Operations
- Filtering, Selecting, and Aggregating Data
- Joins and Merging DataFrames
- Working with Null Values

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Defining and Using UDFs (User Defined Functions)
- Column Operations (Select, Rename, Drop)
- Handling Complex Data Types (Array, Map)
- Working with Dates and Timestamps

𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Understanding Partitions
- Repartitioning and Coalescing
- Managing Shuffle Operations
- Optimizing Partition Sizes for Performance

𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮:
- When to Cache or Persist
- Memory vs Disk Caching
- Checking Storage Levels

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟:
- Spark SQL Introduction
- Creating Temp Views
- Running SQL Queries
- Optimizing SQL Queries with Catalyst Optimizer
- Working with Hive Tables in PySpark

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Data Cleaning and Preparation
- Handling Missing Values
- Data Normalization and Transformation
- Working with Categorical Data

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Broadcasting Variables
- Accumulators
- PySpark Window Functions
- PySpark with Machine Learning (MLlib)
- Working with Streaming Data (Spark Streaming)

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Understanding Job, Stage, and Task
- Tungsten Execution Engine
- Memory Management and Garbage Collection
- Tuning Parallelism
- Using Spark UI for Performance Monitoring

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍