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.
Apache Hive- Overview, Components, Architecture, Step by Step Execution Via Apache Tez or Spark
Continuing… ✅ Module 2: 🧾 Hive Query Language (HQL) 🔹 Basic HiveQL Syntax 📌 Use Case: Load CSV employee data and compute department-wise average salaries. 🔹 Joins in Hive 1. Default Shuffle Join (slow for large datasets) 2. Map Join (faster when one table is small) Force broadcast join. 📌 Use Case: Joining large sales…
Hadoop Tutorial: Components, Architecture, Data Processing, Interview Questions
Here’s a detailed expansion of your Hadoop Core Concepts – Interview Q&A, now with conceptual answers, examples, commands, and added advanced questions covering HDFS CLI, Python-Hadoop integration, and Hive interaction. ✅ Hadoop Core Interview Questions with Answers & Examples 1. What is Hadoop? Answer: Hadoop is an open-source framework that allows for distributed storage and…
How to train for Generative AI considering you have basic knowledge in Python. What should be the Learning path?
Data Structures in Python: Linked Lists
Python Regex complete tutorial with usecases of email search inside whole dbms or code search inside a code repository
PySpark Projects:- Scenario Based Complex ETL projects Part1
String Manipulation on PySpark DataFrames
In PySpark, regex functions let you extract, match, or replace parts of strings using powerful regular expression patterns. These are essential for parsing messy data, extracting tokens, or cleaning text. 🔧 Common PySpark Regex Functions (from pyspark.sql.functions) Function Description regexp_extract() Extracts first matching group using a regex regexp_replace() Replaces all substrings matching regex rlike() Returns…
Pyspark Dataframe programming – operations, functions, all statements, syntax with Examples
Creating DataFrames in PySpark Creating DataFrames in PySpark is essential for processing large-scale data efficiently. PySpark allows DataFrames to be created from various sources, ranging from manual data entry to structured storage systems. Below are different ways to create PySpark DataFrames, along with interesting examples. 1. Creating DataFrames from List of Tuples (Manual Entry) This…
Python Project Alert:- Dynamic list of variables Creation
Python Code Execution- Behind the Door- What happens?
Temporary Functions in PL/Sql Vs Spark Sql
How PySpark automatically optimizes the job execution by breaking it down into stages and tasks based on data dependencies. can explain with an example
Understanding Pyspark execution with the help of Logs in Detail
Pyspark RDDs a Wonder -Transformations, actions and execution operations- please explain and list them
Great question — understanding SparkSession vs SparkContext is essential, especially when dealing with RDDs, DataFrames, or any Spark internals. 🔍 TL;DR Difference Feature SparkContext SparkSession (since Spark 2.0+) Purpose Low-level entry point to Spark functionality Unified entry point to Spark: SQL, Streaming, Hive, RDD API Focus RDDs only DataFrames, Datasets, SQL, RDDs Usage (Modern) Used…
Are Dataframes in PySpark Lazy evaluated?