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Types of SQL /Spark SQL commands- DDL,DML,DCL,TCL,DQL
Python Pandas Series Tutorial- Usecases, Cheatcode Sheet to revise
Pandas operations, functions, and use cases ranging from basic operations like filtering, merging, and sorting, to more advanced topics like handling missing data, error handling
PySpark Projects:- Scenario Based Complex ETL projects Part3
PySpark Projects:- Scenario Based Complex ETL projects Part2
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling
Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…
TroubleShoot Pyspark Issues- Error Handling in Pyspark, Debugging and custom Log table, status table generation in Pyspark
When working with PySpark, there are several common issues that developers face. These issues can arise from different aspects such as memory management, performance bottlenecks, data skewness, configurations, and resource contention. Here’s a guide on troubleshooting some of the most common PySpark issues and how to resolve them. 1. Out of Memory Errors (OOM) Memory-related issues are among the most frequent…
Pyspark Memory Management, Partition & Join Strategy – Scenario Based Questions
CPU Cores, executors, executor memory in pyspark- Explain Memory Management in Pyspark
Partitioning a Table in SQL , Hive QL, Spark SQL