Gowtham Madhavan

Don’t get confused while learning PySpark. Understand these core concepts step by step to become truly proficient in PySpark. 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - PySpark Architecture - SparkContext and SparkSession - RDDs (Resilient Distributed Datasets) - DataFrames - Transformations and Actions - Lazy Evaluation 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: - Creating DataFrames - Reading Data from CSV, JSON, Parquet - DataFrame Operations - Filtering, Selecting, and Aggregating Data - Joins and Merging DataFrames - Working with Null Values 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Defining and Using UDFs (User Defined Functions) - Column Operations (Select, Rename, Drop) - Handling Complex Data Types (Array, Map) - Working with Dates and Timestamps 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Understanding Partitions - Repartitioning and Coalescing - Managing Shuffle Operations - Optimizing Partition Sizes for Performance 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: - When to Cache or Persist - Memory vs Disk Caching - Checking Storage Levels 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟: - Spark SQL Introduction - Creating Temp Views - Running SQL Queries - Optimizing SQL Queries with Catalyst Optimizer - Working with Hive Tables in PySpark 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Data Cleaning and Preparation - Handling Missing Values - Data Normalization and Transformation - Working with Categorical Data 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Broadcasting Variables - Accumulators - PySpark Window Functions - PySpark with Machine Learning (MLlib) - Working with Streaming Data (Spark Streaming) 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸: - Understanding Job, Stage, and Task - Tungsten Execution Engine - Memory Management and Garbage Collection - Tuning Parallelism - Using Spark UI for Performance Monitoring Once you master these PySpark fundamentals, you won’t just write code — you’ll design efficient, scalable, and production-ready data pipelines that harness the full potential of Spark.

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A friend of mine interviewed at JPMorganChase for data engineer role (4 -5 )yrs exp3 below are combined few questions from both tech rounds. 1)How do you implement incremental data loads in ADF? 2)How do you handle failure and retries in ADF pipelines? 3)How do you parameterize pipelines in ADF? 4)How do you monitor and debug pipelines in ADF? 5)How do you orchestrate pipelines across multiple environments? 6)Have you used Azure Key Vault in ADF for securing credentials? 7)How do you optimize SQL queries for large datasets? 8)What is the difference between UNION and UNION ALL? 9)How do you handle NULL values in SQL joins and aggregations? 10)What is indexing, and how does it affect query performance? 11)How do you implement slowly changing dimensions (SCD) using SQL? 12How do you handle large data files in PySpark? 13)Explain the difference between repartition() and coalesce() in PySpark. 14)How do you perform window functions in PySpark? 15)What is lazy evaluation in Spark, and why is it important? 16)How do you optimize PySpark jobs for performance? 17)What is a broadcast join in PySpark, and when should it be used? 18)You have a daily batch pipeline that sometimes fails randomly. How would you debug and fix it? 19)How would you design a data pipeline that ingests files from ADLS Gen2 and loads them into a Delta Lake? 20)Describe how you would implement schema evolution in a PySpark-based pipeline. 21)How do you handle late-arriving data in your pipeline? 22) there was 2 codes that was on sql advance concepts like writing scd 2, a code on lead ,lag , row number use. #AzureDataEngineer #DataEngineerInterview#AzureDataFactory #PySpark#AzureDataEngineer#DataEngineerInterview#AzureDataFactory#PySpark #SQL#DataEngineering

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EY Pyspark interview questions for Data Engineers 1. Can you explain the differences between a DataFrame and an RDD in PySpark? 2. What techniques would you use to optimize the performance of PySpark code? 3. How does the Catalyst Optimizer contribute to query execution in PySpark? 4. Which serialization formats are commonly used in PySpark, and why? 5. How do you address skewed data issues in PySpark? 6. Could you describe how memory management is handled in PySpark? 7. What are the different types of joins in PySpark, and how do you implement them? 8. What is the purpose of the 'broadcast) function in PySpark, and when should it be used? 9. How do you define and use User-Defined Functions (UDFs) in PySpark? 10. What is lazy evaluation in PySpark, and how does it affect job execution? 11. What are the steps to create a DataFrame in PySpark? 12. Could you explain the concept of Resilient Distributed Datasets (RDD) in PySpark? 13. What are actions and transformations in PySpark, and how do they differ? 14. How do you manage and handle null values in PySpark DataFrames? 15. What is a partition in PySpark, and how do you control partitioning for better performance? 16. Can you explain the difference between narrow and wide transformations in PySpark? 17. How does PySpark infer schemas, and what are the implications of this? 18. What role does SparkContext play in a PySpark application? 19. How do you perform aggregations in PySpark, and what are the key considerations? 20. What strategies do you use for caching data in PySpark to improve performance?

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Spark Coding Series groupBy & agg in PySpark Key Point: groupBy().agg() is used for aggregations like avg, sum, max, count etc. You can apply multiple aggregations at once inside agg(). 💡 Interview Tip: ▪️If asked “How is groupBy different from window functions?” → ▪️groupBy collapses rows into groups (reduces number of rows), whereas window functions keep all rows and calculate values over a partition. #PySpark #SparkSQL #DataEngineering #BigData #ETL #Aggregation #SparkCodingSeries

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𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 ❓To process 10 GB of data using Spark. 👉 How many executors are required? 👉 How much memory should each executor have? 👉 How many cores are needed for maximum parallelism? ---------Step-by-Step Solution 𝟏. 𝐅𝐢𝐧𝐝 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐩𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐬 Default partition size = 128 MB Data size to process = 10 GB = 10,240 MB Number of partitions = 10,240 / 128 = 80 partitions 𝟐. 𝐅𝐢𝐧𝐝 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐜𝐨𝐫𝐞𝐬 Rule: 1 core = 1 partition parallelism Total cores required = 80 cores 𝟑. 𝐅𝐢𝐧𝐝 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫𝐬 Good practice: 5 cores per executor Number of executors = 80 / 5 = 16 executors 𝟒. 𝐅𝐢𝐧𝐝 𝐦𝐞𝐦𝐨𝐫𝐲 𝐩𝐞𝐫 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫 Formula: cores per executor × partition size = 5 × 128 MB = 640 MB (used just for execution) Recommended: multiply by 4 (to handle shuffle, overhead, caching, etc.) = 640 × 4 = ~2.6 GB per executor 𝐅𝐢𝐧𝐚𝐥 𝐀𝐧𝐬𝐰𝐞𝐫 Executors: 16, Cores per executor: 5, Total cores: 80, Memory per executor: ~2.6 GB This way, you can achieve maximum parallelism while avoiding out-of-memory issues. 💬 What’s your go-to strategy for tuning Spark jobs at scale? #BigData #Spark #DataEngineering #ApacheSpark #InterviewTips #Databricks #Azure #ETL #Pyspark #AWS #TechCareer #SparkTuning #CloudComputing ##LinkedInLearning

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Stuck in PySpark tutorial hell? I was too, until I realised the truth: You don't need more tutorials, you need smarter practice. Instead of watching another video, I used these 3 prompts to generate unlimited, targeted practice questions: 💡 Prompt 1 "Generate 40 progressively harder PySpark DataFrame practice questions, grouped by concept such as schema, filtering, joins, aggregations, windows, and optimizations." 📈 Prompt 2 "Create a practice set of PySpark problems that start simple but evolve into real-world ETL scenarios including data cleaning, transformation, quality checks, and incremental loads." 🚀 Prompt 3 "Give me PySpark challenges focused only on performance tuning including partitioning, caching, broadcast joins, and skew handling with tasks that compare naive vs optimized approaches." Copy-paste any one of these into ChatGPT/Gemini and watch it generate a fresh set of practice questions.

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