Apache Spark End-To-End Data Engineering Project | Apple Data Analysis скачать в хорошем качестве

Apache Spark End-To-End Data Engineering Project | Apple Data Analysis

Dive into the world of big data processing with our PySpark Practice playlist. This series is designed for both beginners and seasoned data professionals looking to sharpen their Apache Spark skills through scenario-based questions and challenges. Each video provides step-by-step solutions to real-world problems, helping you master PySpark techniques and improve your data-handling capabilities. Whether preparing for a job interview or just learning more about Spark, this playlist is your go-to resource for practical, hands-on learning. Join us to become a PySpark expert! In this video, we used DataBricks to create multiple ETL pipelines using the Python API of Apache Spark i.e. PySpark. We have used sources like CSV, Parquet, and Delta Table then used Factory Pattern to create the reader class. Factory Pattern is one of the most used Low-Level designs in Data Engineering pipelines that involve multiple sources. Then we used PySpark DataFrame API and Spark SQL to write the business transformation logic. In the loader part, we have loaded data into two fashion one using DataLake and another by Data LakeHouse. While solving the problems, we are also demonstrating the most asked PySpark #interview problems. We have discussed and demonstrated a lot of concepts like broadcast join, partition by and bucketing, sparkSession, windows functions like LAG and LEAD, delta table and many other concepts. After watching, please let us know your thoughts, Stay tuned to all to this playlist for all upcoming videos. 𝗝𝗼𝗶𝗻 𝗺𝗲 𝗼𝗻 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮: 🔅 Topmate (For collaboration and Scheduling calls) - https://topmate.io/ankur_ranjan 🔅 LinkedIn -   / thebigdatashow   🔅 Instagram -   / ranjan_anku   DataBricks notebooks link. Extract the zip folder by downloading it and then open the HTML files as a notebook in the community version of Databricks. 🔅 Recommended Link for DataBricks community version login, after signing up: https://community.cloud.databricks.com/ 🔅 Ankur's Notebook source files https://drive.google.com/file/d/15FBg... 🔅 Input table files https://drive.google.com/drive/folder... For practising different Data Engineering interview questions, go to the community section of our YouTube page.    / @thebigdatashow   Narrow vs Wide Transformation Short Article link:    • Запись   Questions 1:    • Запись   Question 2:    • Запись   Question 3:    • Запись   Question 4:    • Запись   Question 5:    • Запись   Question 6:    • Запись   Question 7:    • Запись   Question 8:    • Запись   Question 9:    • Запись   Question 10:    • Запись   Broadcast Join in #apachespark Small article link:    • Запись   MCQs list 1.    / @thebigdatashow   2.    / @thebigdatashow   3.    / @thebigdatashow   4.    / @thebigdatashow   5.    / @thebigdatashow   Check the COMMUNITY section for a full list of questions. Chapters 00:00 - Project Introduction 12:04 - How to use Databricks for any Pyspark/Spark Project? 25:09 - Low-Level Design Code 40:39 - Job, Stages, and Action in Spark 45:22 - Designing a code base for the Spark Project 51:40 - Applying first business Logic in the transformer class 57:34 - Difference between Lag & Lead window function 01:28:42 - Broadcast Join in Apache Spark/pyspark 01:47:50 - Difference between Partitioning and Bucketing in Apache Spark/pyspark 2:07:00 - Detailed Summary of the first pipeline 2:14:00 - Second pipeline Goal 02:24:57 - collect_set() and collect_list() in Spark/pyspark 02:48:53 - Detailed Summary of the second pipeline 02:51:03 - Why is Delta Lake when we already have DataLake? 02:54:51 - Summary #databricks #delta #pyspark #practice #dataengineering #apachespark #problemsolving #spark #bigdata #interviewquestions #sql #datascience #dataanalytics