Welcome to the Future – AI Hints Today
Keyword is AI– This is your go-to space to ask questions, share programming tips, and engage with fellow coding enthusiasts. Whether you’re a beginner or an expert, our community is here to support your journey in coding. Dive into discussions on various programming languages, solve challenges, and exchange knowledge to enhance your skills.
Date and Time Functions- Pyspark Dataframes & Pyspark Sql Queries
A quick reference for date manipulation in PySpark:– FunctionDescriptionWorks OnExample (Spark SQL)Example (DataFrame API)to_dateConverts string to date.StringTO_DATE(‘2024-01-15’, ‘yyyy-MM-dd’)to_date(col(“date_str”), “yyyy-MM-dd”)to_timestampConverts string to timestamp.StringTO_TIMESTAMP(‘2024-01-15 12:34:56’, ‘yyyy-MM-dd HH:mm:ss’)to_timestamp(col(“timestamp_str”), “yyyy-MM-dd HH:mm:ss”)date_formatFormats date or timestamp as a string.Date, TimestampDATE_FORMAT(CURRENT_DATE, ‘dd-MM-yyyy’)date_format(col(“date_col”), “dd-MM-yyyy”)current_dateReturns the current date.–CURRENT_DATEcurrent_date()current_timestampReturns the current timestamp.–CURRENT_TIMESTAMPcurrent_timestamp()date_addAdds days to a date.Date, TimestampDATE_ADD(‘2024-01-15’, 10)date_add(col(“date_col”), 10)date_subSubtracts days from a date.Date, TimestampDATE_SUB(‘2024-01-15’, 10)date_sub(col(“date_col”),…
Memory Management in PySpark- CPU Cores, executors, executor memory
To determine the optimal number of CPU cores, executors, and executor memory for a PySpark job, several factors need to be considered, including the size and complexity of the job, the resources available in the cluster, and the nature of the data being processed. Here’s a general guide: 1. Number of CPU Cores per Executor 2. Number…
Memory Management in PySpark- Scenario 1, 2
Suppose If i am given a maximum of 20 cores to run my data pipeline or ETL framework, i will need to strategically allocate and optimize resources to avoid performance issues, job failures, or SLA breaches. Here’s how you can accommodate within a 20-core limit, explained across key areas: 🔹 1. Optimize Spark Configurations Set…
Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
Here’s a complete blueprint to help you develop and maintain CI/CD pipelines using GitHub for automated deployment, version control, and DevOps best practices in data engineering — particularly for Azure + Databricks + ADF projects. 🚀 PART 1: Develop & Maintain CI/CD Pipelines Using GitHub ✅ Technologies & Tools Tool Purpose GitHub Code repo +…
Complete guide to building and managing data workflows in Azure Data Factory (ADF)
Here’s a complete guide to building and managing data workflows in Azure Data Factory (ADF) — covering pipelines, triggers, linked services, integration runtimes, and best practices for real-world deployment. 🏗️ 1. What Is Azure Data Factory (ADF)? ADF is a cloud-based ETL/ELT and orchestration service that lets you: 🔄 2. Core Components of ADF Component…
Complete guide to architecting and implementing data governance using Unity Catalog on Databricks
Here’s a complete guide to architecting and implementing data governance using Unity Catalog on Databricks — the unified governance layer designed to manage access, lineage, compliance, and auditing across all workspaces and data assets. ✅ Why Unity Catalog for Governance? Unity Catalog offers: Feature Purpose Centralized metadata Unified across all workspaces Fine-grained access control Table,…
Designing and developing scalable data pipelines using Azure Databricks and the Medallion Architecture (Bronze, Silver, Gold)
Designing and developing scalable data pipelines using Azure Databricks and the Medallion Architecture (Bronze, Silver, Gold) is a common and robust strategy for modern data engineering. Below is a complete practical guide, including: 🔷 1. What Is Medallion Architecture? The Medallion Architecture breaks a data pipeline into three stages: Layer Purpose Example Ops Bronze Raw…
Complete OOP interview questions set for Python — from basic to advanced
Here’s a complete OOP interview questions set for Python — from basic to advanced — with ✅ real-world relevance, 🧠 conceptual focus, and 🧪 coding triggers. You can practice or review these inline (Notion/blog-style ready). 🧠 Python OOP Interview Questions (With Hints) 🔹 Basic Level (Conceptual Clarity) 1. What is the difference between a class…
Classes and Objects in Python- Object Oriented Programming & A Data Engineering Project
This posts is a complete guide to Python OOP (Object-Oriented Programming) — both basic and advanced topics, interview-relevant insights, code examples, and a data engineering mini-project using Python OOP + PySpark. 🐍 Python OOP: Classes and Objects (Complete Guide) ✅ What is OOP? Object-Oriented Programming is a paradigm that organizes code into objects, which are…
Parallel processing in Python—especially in data engineering and PySpark pipelines
Great topic! Parallel processing is essential for optimizing performance in Python—especially in data engineering and PySpark pipelines where you’re often handling: Let’s break it down with ✅ why, 🚀 techniques, 🧰 use cases, and 🔧 code examples. ✅ Why Parallel Processing in Python? Problem Area Parallelism Benefit Processing large files Split across threads/processes Batch API…
All major PySpark data structures and types Discussed
Absolutely! Let’s walk through all major PySpark data structures and types that are commonly used in transformations and aggregations — especially: 🧱 1. Row — Spark’s Internal Data Holder Example: Used when creating small DataFrames manually. 🏗 2. StructType / StructField — Schema Definition Objects Example: Used with: 🧱 3. struct() — Row-like object inside…
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…
Partition & Join Strategy in Pyspark- Scenario Based Questions
Q1.–We are working with large datasets in PySpark, such as joining a 30GB table with a 1TB table or Various Transformation on 30 GB Data, we have 100 cores limit to use per user , what can be best configuration and Optimization strategy to use in pyspark ? will 100 cores are enough or should…
Data Engineer Interview Questions Set5
Spark Configuration, Monitoring, and Tuning, covering theory + code examples Here’s a comprehensive guide to Spark Configuration, Monitoring, and Tuning, covering theory + code examples. It’s especially helpful for Data Engineers working on performance optimization or preparing for interviews. ⚙️ Spark Configuration, Monitoring, and Tuning 🔧 1. Understand Components of the Spark Cluster A Spark…
SQL Tricky Conceptual Interview Questions
Data cleaning in SQL is a crucial step in data preprocessing, especially when working with real-world messy datasets. Below is a structured breakdown of SQL data cleaning steps, methods, functions, and complex use cases you can apply in real projects or interviews. ✅ Common SQL Data Cleaning Steps & Methods Step Method / Function Example…