Here's a PySpark SQL cheatsheet, covering common operations and concepts. This is designed to be a quick reference for those working with PySpark DataFrames and SQL-like operations.PySpark SQL Cheatsheet1. Initialization & Data Loadingfrom pyspark.sql import SparkSessionfrom pyspark.sql.functions import *from pyspark.sql.types import *# Initialize SparkSessionspark = SparkSession.builder \ .appName("PySparkSQLCheatsheet") \ .getOrCreate()# Load Data (e.g., CSV, Parquet)df_csv... Continue Reading →
Perfect ETL Pipeline on Azure Cloud
ETL Pipeline Implementation on AzureThis document outlines the creation of an end-to-end ETL pipeline on Microsoft Azure, utilizing Azure Data Factory for orchestration, Azure Databricks for transformation, Azure Data Lake Storage Gen2 for storage, Azure Synapse Analytics for data warehousing, and Power BI for visualization. The pipeline is designed to be scalable, secure, and efficient,... Continue Reading →
An azure pipeline usually run for 2 hrs but currently it is running for 10 hours. Find the bottleneck in pipeline.
To identify the bottleneck in an Azure Pipeline that’s running for 10 hours instead of the usual 2 hours, you need to systematically analyze the pipeline’s execution. Here’s a step-by-step approach to pinpoint the issue:### 1. **Check Pipeline Logs and Execution Details** - **Action**: Navigate to the Azure DevOps portal, open the pipeline run, and... Continue Reading →
Pyspark Intermediate Level questions and answers
### General PySpark Concepts1. **What is PySpark, and how does it differ from Apache Spark?** - **Answer**: PySpark is the Python API for Apache SparBelow is a curated list of intermediate-level PySpark interview questions designed to assess a candidate’s understanding of PySpark’s core concepts, practical applications, and optimization techniques. These questions assume familiarity with Python,... Continue Reading →
Spark SQL
#Databricks #SQL for Data Engineering ,Data Science and Machine Learning.✅ The whole SQL lesson for DataBricks is provided here.1️⃣ spark sql sessions as series.https://lnkd.in/g77DE36a2️⃣ How to register databricks community editionhttps://lnkd.in/ggAqRgKJ3️⃣ What is DataWarehouse? OLTP and OLAP?https://lnkd.in/gzSuJCBC4️⃣ how to create database in databricks?https://lnkd.in/gzHNFZrv5️⃣ databricks file system dbfs.https://lnkd.in/dHAHkqd36️⃣ Spark SQL Table , Difference between Managed table and... Continue Reading →
Data Scientist Roadmap
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 →
System Design Blogs
30 Blogs to learn 30 System Design Concepts:1) Content Delivery Network (CDN): https://lnkd.in/gjJrEJeH2) Caching: https://lnkd.in/gC9piQbJ3) Distributed Caching: https://lnkd.in/g7WKydNg4) Latency vs Throughput: https://lnkd.in/g_amhAtN5) CAP Theorem: https://lnkd.in/g3hmVamx6) Load Balancing: https://lnkd.in/gQaa8sXK7) ACID Transactions: https://lnkd.in/gMe2JqaF8) SQL vs NoSQL: https://lnkd.in/g3WC_yxn9) Consistent Hashing: https://lnkd.in/gd3eAQKA10) Database Index: https://lnkd.in/gCeshYVt11) Rate Limiting: https://lnkd.in/gWsTDR3m12) Microservices Architecture: https://lnkd.in/gFXUrz_T13) Strong vs Eventual Consistency: https://lnkd.in/gJ-uXQXZ14) REST vs RPC:... 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 →
CODEBOX 