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PySpark DataFrames provide a powerful and user-friendly API for working with structured and semi-structured data. In this article, we present a set of practice questions to help you reinforce your understanding of PySpark DataFrames and their operations. Loading DataLoad the "sales_data.csv" file into a PySpark DataFrame. The CSV file contains the following columns: "transaction_id", "customer_id",... Continue Reading →
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 storage๐ complete data pipeline diagram can be found here....https://lnkd.in/gdifVyHY๐ FREE guide to data pipeline in AWS, Azure cloud....https://lnkd.in/gtq_8rd9๐ learn more... Continue Reading →
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 →
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. โ Pyspark Scenarios 1: How to create partition by month and year in pyspark https://lnkd.in/dFfxYR_F โ pyspark scenarios 2 : how to read variable number of... Continue Reading →
๐๐๐ญ๐๐๐ซ๐ข๐๐ค๐ฌ ๐๐ง๐ ๐๐ฒ๐๐ฉ๐๐ซ๐ค ๐๐ซ๐ ๐ญ๐ก๐ ๐ฆ๐จ๐ฌ๐ญ ๐ข๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐ฌ๐ค๐ข๐ฅ๐ฅ๐ฌ ๐ข๐ง ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ . ๐๐ฅ๐ฆ๐จ๐ฌ๐ญ ๐๐ฅ๐ฅ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ ๐๐ซ๐ ๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐๐ซ๐จ๐ฆ ๐๐๐๐จ๐จ๐ฉ ๐ญ๐จ ๐๐ฉ๐๐๐ก๐ ๐๐ฉ๐๐ซ๐ค. ๐ ๐ก๐๐ฏ๐ ๐๐จ๐ฏ๐๐ซ๐๐ ๐๐ฅ๐ฆ๐จ๐ฌ๐ญ ๐๐ฏ๐๐ซ๐ฒ๐ญ๐ก๐ข๐ง๐ ๐ข๐ง ๐ฆ๐ฒ ๐ ๐ซ๐๐ ๐๐จ๐ฎ๐๐ฎ๐๐ ๐ฉ๐ฅ๐๐ฒ๐ฅ๐ข๐ฌ๐ญ. ๐๐ก๐๐ซ๐ ๐๐ซ๐ 70 ๐ฏ๐ข๐๐๐จ๐ฌ ๐๐ฏ๐๐ข๐ฅ๐๐๐ฅ๐ ๐๐จ๐ซ ๐๐ซ๐๐.0. Introduction to How to setup Account 1. How to read CSV file in PySpark 2. How to... Continue Reading →
๐how to create partitions based on year and month ?Data partitioning is critical to data processing performance especially for large volume of data processing in spark.Most of the traditional databases will be having default date format DD-MM-YYYY.But cloud storage (spark delta lake/databricks tables) will be using YYYY-MM-DD format.So here we will be see how to... Continue Reading →
โซ Incremental Loading technique with Change Data Capture (CDC): โก๏ธ Incremental Load with Change Data Capture (CDC) is a strategy in data warehousing and ETL (Extract, Transform, Load) processes where only the changed or newly added data is loaded from source systems to the target system. CDC is particularly useful in scenarios where processing the... Continue Reading →
โ----------Spark Interview Questions-------------๐Important Note : This scenario is bit complex I would suggest go through it multiple times. (code implementation is in #databricks )๐how to handle or how to read variable/dynamic number of columns details?id,name,location,emaild,phone1, aman2,abhi,Delhi3,john,chennai,sample123@gmail.com,688080in a scenario we are geeting not complete columnar information but vary from row to row.pyspark code :===============dbutils.fs.put("/dbfs/tmp/dynamic_columns.csv","""id,name,location,emaild,phone1, aman2,abhi,Delhi3,john,chennai,sample123@gmail.com,688080""")now lets... Continue Reading →
List of All azure / data / devops /ML Interview Q& ASave & Share.1. ๐๐๐๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ฐ๐๐ผ๐ฟ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dVzCmzcZ2. ๐๐๐๐ฟ๐ฒ ๐๐ฎ๐๐ฎ๐ฏ๐ฟ๐ถ๐ฐ๐ธ๐ ๐ฆ๐ฐ๐ฒ๐ป๐ฎ๐ฟ๐ถ๐ผ ๐ฏ๐ฎ๐๐ฒ๐ฑ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dUCf8qf8๐ฏ. ๐ฅ๐ฒ๐ฎ๐น๐๐ถ๐บ๐ฒ ๐๐๐๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ฐ๐๐ผ๐ฟ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/ex_Vixh๐ฐ.๐๐ฎ๐๐ฒ๐๐ ๐๐๐๐ฟ๐ฒ ๐๐ฒ๐๐ข๐ฝ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/g7PdATm๐ฑ. ๐๐๐๐ฟ๐ฒ ๐๐ฐ๐๐ถ๐๐ฒ ๐๐ถ๐ฟ๐ฒ๐ฐ๐๐ผ๐ฟ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dtWYXTKN๐ฒ. ๐๐๐๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ธ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dgr-uGQB๐ณ. ๐๐๐๐ฟ๐ฒ ๐๐ฝ๐ฝ ๐ฆ๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dP4Afqkb๐ด. ๐๐๐๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค&๐https://lnkd.in/dj_m2yeQ๐ต.... Continue Reading →
Internals of Caching in PysparkCaching DataFrames in PySpark is a powerful technique to improve query performance. However, there's a subtle difference in how you can cache DataFrames in PySpark.cached_df = orders_df.cache() and orders_df.cache() are two common approaches & they serve different purposes.The choice between these two depends on your specific use case and whether you... Continue Reading →
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