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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 →
PySpark is a powerful framework for distributed data processing and analysis. If you're an experienced PySpark developer preparing for a job interview, it's essential to be ready for scenario-based questions that test your practical knowledge. In this article, we present 30 scenario-based interview questions along with their solutions to help you confidently tackle your next... Continue Reading →
Step by Step Plan to learn Big Data (All Free resources Included)1. Learn SQL Basics - https://lnkd.in/g9NEJMVE SQL will be used at a lot of places - Hive/Spark SQL/RDBMS queriesJoins & windowing functions are very important2. Learn Programming/Python for Data Engineering - https://lnkd.in/gr6fFPdU Learn Python to an extent required for Data Engineers.3. Learn the Fundamentals... 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. ✅ 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 →
How I would relearn Data Science In 2024 to get a job: Getting Started: ⬇️ - Data Science Intro: DataCamp- Anaconda Setup: Anaconda Documentation Programming: - Python Basics: Real Python- R Basics: R-bloggers- SQL Fundamentals: SQLZoo- 六 Java for Data Science: Udemy - Java Programming and Software Engineering Fundamentals Mathematics:... Continue Reading →
𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐚𝐧𝐝 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐬𝐤𝐢𝐥𝐥𝐬 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. 𝐀𝐥𝐦𝐨𝐬𝐭 𝐚𝐥𝐥 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐫𝐞 𝐦𝐨𝐯𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐇𝐚𝐝𝐨𝐨𝐩 𝐭𝐨 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤. 𝐈 𝐡𝐚𝐯𝐞 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐚𝐥𝐦𝐨𝐬𝐭 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐦𝐲 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭. 𝐓𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 70 𝐯𝐢𝐝𝐞𝐨𝐬 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐟𝐨𝐫 𝐟𝐫𝐞𝐞.0. Introduction to How to setup Account 1. How to read CSV file in PySpark 2. How to... Continue Reading →
📕how 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 →
⏫ Incremental Loading technique with Change Data Capture (CDC): ➡️ 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 →
‐----------Spark Interview Questions-------------📍Important Note : This scenario is bit complex I would suggest go through it multiple times. (code implementation is in #databricks )📕how 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 →
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