learn here about data pipeline for FREE.....data pipeline consists of several stages that work together to ensure that data is processed efficiently and accurately. it involves....1. data ingestion2. data transformation3. data analysis4. data visualisation5. data storage📌 complete data pipeline diagram can be found here....https://lnkd.in/gdifVyHY📌 FREE guide to data pipeline in AWS, Azure cloud....https://lnkd.in/gtq_8rd9📌 learn more... Continue Reading →
Pyspark Scenario ~ Find Average
Write a solution in PySpark to find the average selling price for each product. average_price should be rounded to 2 decimal places.Solution :import datetimefrom pyspark.sql import SparkSessionfrom pyspark.sql.functions import col, sum, roundfrom pyspark.sql.types import StructType, StructField, IntegerType, DateType# Initialize Spark sessionspark = SparkSession.builder.appName("average_selling_price").getOrCreate()# Data for Prices and Units Soldprices_data = [(1, datetime.date(2019, 2, 17), datetime.date(2019,... Continue Reading →
Pyspark Scenarios
Check out these 23 complete PySpark real-time scenario videos covering everything from partitioning data by month and year to handling complex JSON files and implementing multiprocessing in Azure Databricks. ✅ Pyspark Scenarios 1: How to create partition by month and year in pyspark https://lnkd.in/dFfxYR_F ✅ pyspark scenarios 2 : how to read variable number of... Continue Reading →
Insert, Update and Delete in PySpark
Here's the scenario: We had two data tables, Table_A and Table_B, each containing a "Name" and "Age" column. 📋💡Table_A:Name | Age------------S1 | 20S2 | 23-------------------------Table_B:Name | Age------------S1 | 22S4 | 27Our mission was to determine the differences between these tables and generate a Action between Update, Delete, Insert🚀 and here's the solution we came up... Continue Reading →
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