HintsToday
Hints and Answers for Everything
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- what APIs are, why they exist, and how we use them in Python?
- Python Strings- complete notes + interview Q&A
- Memory Management in PySpark- CPU Cores, executors, executor memory
- Memory Management in PySpark- Scenario 1, 2
- Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
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Author: lochan2014
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…
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…
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…
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…
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…
Question:-“What really happens inside the Spark engine when I run a simple .read() or .join() on a file?” Let me break this down in a clear, interview-ready, cluster-level Spark execution flow, step-by-step: 🔍 Spark Cluster Background Process (Example: spark.read.csv(…)) Imagine this code: Let’s analyze it in chronological order: ✅ 1. Driver Program Starts the Spark…
This is a fantastic deep-dive! Let’s answer your question clearly and technically: ✅ Question Recap: If I read a 1 GB CSV file or a 1 GB Hive table into a DataFrame —❓ Does defaultParallelism apply?❓ How are tasks created and executed in this case? 🔧 Short Answer: No, defaultParallelism does not directly control how…
Advanced-level PySpark, Big Data systems, and backend engineering—here’s a breakdown of what questions you can expect, based on industry trends. ✅ Topic-wise Breakdown of Likely Questions 🔹 PySpark & Big Data (Core Focus) Area Sample Questions PySpark DataFrame APIs – How is selectExpr different from select?- Use withColumn, explode, filter in one chain.- Convert nested…
Here’s a clearer, interactive, and logically structured version of your Oracle SQL Query Flow explanation with real-world analogies, step-by-step breakdowns, diagrams (as text), and a cross-engine comparison with MySQL and SQL Server (MSSQL). We’ve also added a crisp SQL optimization guide. 🧠 How an SQL Query Flows Through Oracle Engine (with Comparison and Optimization Tips)…