Ravi Teja Jayavarapu

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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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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ON-DEMAND TOOLS | Data Engineering (High Demand in 2025) Apache Spark ⭐   Status: ON-DEMAND   Still the go-to engine for large-scale in-memory data processing and ETL workloads.  Snowflake ⭐⭐   Status: ON-DEMAND   Cloud-native data warehouse with massive enterprise adoption, often paired with dbt & Airflow.  Databricks ⭐⭐   Status: ON-DEMAND   Unified analytics & lakehouse platform built on Spark; dominates enterprise data stack discussions.  Apache Airflow ⭐⭐   Status: ON-DEMAND   Still the backbone for data orchestration; newer tools like Prefect & Dagster complement it.  dbt (Data Build Tool) ⭐⭐   Status: ON-DEMAND   Modern transformation layer; key skill for analytics engineers and SQL-based data modeling.  Apache Kafka ⭐⭐   Status: ON-DEMAND   Backbone for event streaming; used by most large-scale, real-time data systems globally.  BigQuery ⭐   Status: ON-DEMAND   Serverless, cost-efficient analytics engine widely adopted in modern cloud data architectures.  Apache Flink ⭐   Status: ON-DEMAND   Leading real-time stream processing framework; preferred over Storm for low-latency workloads.  Delta Lake / Apache Iceberg ⭐   Status: ON-DEMAND   Open table formats powering lakehouse architectures with ACID transactions and schema evolution.  --- ⚠️ OUTDATED OR DECLINING TOOLS Hadoop MapReduce ❌   Status: OUTDATED   Superseded by Spark and cloud-based engines; minimal new deployments.  Apache Hive ⚠️   Status: LEGACY   Still present in Hadoop clusters, but replaced by SQL-on-lake engines & modern warehouses.  Apache HBase ⚠️   Status: NICHE   Limited adoption beyond legacy Hadoop use cases.  Apache Storm ⚠️   Status: OUTDATED   Replaced by Flink & Spark Streaming for real-time workloads.  HDFS ⚠️   Status: DECLINING   Gradually replaced by cloud object storage (S3, GCS, ADLS).  --- 📊 KEY INSIGHTS (2025) 🔥 What’s Hot: - Cloud-native tools (Snowflake, Databricks, BigQuery)   - Modern orchestration (Airflow, Prefect)   - Stream processing (Kafka, Flink)   - Lakehouse tech (Delta Lake, Iceberg)   - Transformation tools (dbt) ⚠️ What’s Fading: - Traditional Hadoop stack   - MapReduce batch systems   - On-prem data clusters   - Hive & HBase for new projects  🎯 Career Tip: Focus on Spark, Airflow, dbt, Kafka, and Databricks/Snowflake — these dominate modern data engineering roles in 2025. Follow Gowtham SB #DataEngineering #BigData #DataPipeline #AI #Lakehouse #Spark #Airflow #Kafka #Snowflake #Databricks #dbt

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