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PySpark Projects:- Scenario Based Complex ETL projects Part1
String Manipulation on PySpark DataFrames
df = df.withColumn(“name_length”, length(df.first_name)), how to calculate length with leading or trailing spaces or any special characters inserted Great question! In PySpark, the length() function counts all characters, including: ✅ Example: Count Characters with Spaces and Specials ✅ Output: 🧠 Key Notes: ⚠️ Compare With Trim:
Pyspark Dataframe programming – operations, functions, all statements, syntax with Examples
✅ Schema Enforcement in PySpark DataFrames In PySpark, schema enforcement ensures that the structure (schema) of your DataFrame strictly matches the expected data types and field names during read and write operations. 🧠 What is Schema Enforcement? Schema enforcement (also called schema-on-write) is the ability of Spark to validate incoming data against a predefined schema,…
Python Project Alert:- Dynamic list of variables Creation
Python Code Execution- Behind the Door- What happens?
Temporary Functions in PL/Sql Vs Spark Sql
How PySpark automatically optimizes the job execution by breaking it down into stages and tasks based on data dependencies. can explain with an example
Understanding Pyspark execution with the help of Logs in Detail
Pyspark RDDs a Wonder -Transformations, actions and execution operations- please explain and list them
Great question — understanding SparkSession vs SparkContext is essential, especially when dealing with RDDs, DataFrames, or any Spark internals. 🔍 TL;DR Difference Feature SparkContext SparkSession (since Spark 2.0+) Purpose Low-level entry point to Spark functionality Unified entry point to Spark: SQL, Streaming, Hive, RDD API Focus RDDs only DataFrames, Datasets, SQL, RDDs Usage (Modern) Used…
Are Dataframes in PySpark Lazy evaluated?