Name		Name	Last commit message	Last commit date
Latest commit History 245 Commits
Datasets		Datasets
coding		coding
theory		theory
53rd weekofyear syntax		53rd weekofyear syntax
ACID properties		ACID properties
ASEQ		ASEQ
Average marks over years		Average marks over years
Case When Usage		Case When Usage
Child Parent Grandparent		Child Parent Grandparent
Combine lists to form dataframe		Combine lists to form dataframe
Complex data with column		Complex data with column
Counting the elements in collect list		Counting the elements in collect list
Cumulative sum Window orderBy		Cumulative sum Window orderBy
Datatypes, Column, Row, Grouping		Datatypes, Column, Row, Grouping
Date formats		Date formats
Dispatched and Ordered		Dispatched and Ordered
Dropping duplicates		Dropping duplicates
Employees with equal salary		Employees with equal salary
Error handling in pyspark		Error handling in pyspark
GIT for beginners		GIT for beginners
IBM interview question		IBM interview question
Imposing and comparing the schema		Imposing and comparing the schema
IntelliJ setup		IntelliJ setup
JSON Functions		JSON Functions
JSON comparator		JSON comparator
JSON functions		JSON functions
Joins info		Joins info
Lag Function usage		Lag Function usage
Lineage Graph		Lineage Graph
Merging data frames		Merging data frames
Narrow and Wide transformations		Narrow and Wide transformations
Practice Questions - 1		Practice Questions - 1
Practice Questions - 2		Practice Questions - 2
Practice questions - 3		Practice questions - 3
Pyspark Functions, return types		Pyspark Functions, return types
Pyspark actions		Pyspark actions
RDD Vs Dataframe		RDD Vs Dataframe
RDD functions only but not dataframes		RDD functions only but not dataframes
RDD operations - 1		RDD operations - 1
RDD operations - 2		RDD operations - 2
README.md		README.md
Renaming columns using list and for loop		Renaming columns using list and for loop
Reversing the words in a string		Reversing the words in a string
Schema creation, pivot, coalesce, final balance and com		Schema creation, pivot, coalesce, final balance and com
Select top purchasing customer for each store		Select top purchasing customer for each store
Source and Target RDD validation		Source and Target RDD validation
Spark SQL and DSL		Spark SQL and DSL
Spark UI		Spark UI
Spark connection to various sources		Spark connection to various sources
Split examples		Split examples
Terminology or shortcuts		Terminology or shortcuts
Theory Interview Questions - 1		Theory Interview Questions - 1
UDF's		UDF's
Unpivot		Unpivot
Ways to kill pyspark application		Ways to kill pyspark application
Ways to represent schema, datatypes		Ways to represent schema, datatypes
Weather Observation Station Hacker rank		Weather Observation Station Hacker rank
Window		Window
Window dense rank		Window dense rank
coalesce won		coalesce won
coalesce(*cols)		coalesce(*cols)
coalesce, fillna		coalesce, fillna
collect_list and collect_set		collect_list and collect_set
complex data processing - example		complex data processing - example
complex data processing - struct		complex data processing - struct
complex data processing - struct inside array		complex data processing - struct inside array
convert dataframe to lists, tuples, dictionaries		convert dataframe to lists, tuples, dictionaries
count and % of nulls in rows and columns		count and % of nulls in rows and columns
countDistinct cust who bought all prod		countDistinct cust who bought all prod
cumulative sum - 1		cumulative sum - 1
currency converter in pyspark		currency converter in pyspark
deepdiff in pyspark		deepdiff in pyspark
dense_rank		dense_rank
distinct() Vs dropDupicates()		distinct() Vs dropDupicates()
email and number from string		email and number from string
employee country wise used project		employee country wise used project
explode function		explode function
fill forward technique		fill forward technique
fold() function		fold() function
food rating repeat function		food rating repeat function
for a list, duplicate value count and their indexes		for a list, duplicate value count and their indexes
from_json, json in dataframe cell		from_json, json in dataframe cell
getNumPartitions, rlike		getNumPartitions, rlike
git from chatgpt		git from chatgpt
group by collect_set		group by collect_set
handling bad data		handling bad data
handling job failures and no duplicates		handling job failures and no duplicates
highest number in a list		highest number in a list
lead diff of temp		lead diff of temp
list to dictionary		list to dictionary
masking the mobile and mail		masking the mobile and mail
max date		max date
max no of countries		max no of countries
min max name agg collect_set		min max name agg collect_set
missing numbers in a df		missing numbers in a df
moving or rolling average		moving or rolling average
multiple delimiters in a string		multiple delimiters in a string
multiple delimiters in a table		multiple delimiters in a table
multiple when otherwise usage		multiple when otherwise usage
no of records in each partition		no of records in each partition

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PySpark

All I know about PySpark

For all the code executions above, Please put the below code at the begining import sys from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession import os from pyspark.sql.functions import *

python_path = sys.executable os.environ['PYSPARK_PYTHON'] = python_path os.environ['JAVA_HOME'] = r'Java path' # Please write the java path here as per your laptop conf = SparkConf().setAppName("pyspark").setMaster("local[*]").set("spark.driver.host", "localhost").set("spark.default.parallelism", "1") sc = SparkContext(conf=conf) spark = SparkSession.builder.getOrCreate()

If you truly want to learn Big data tools like Hadoop, Sqoop, Hive, Spark and Cloud counterparts of them. Please ping me on whatsapp 9490716829.

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