Udemy - Top 101 Data Engineering Interview Questions

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• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1 - Introduction to the Course
• 1 -How to use this course (Slides + Voiceover transcripts + Practice approach).mp4 (11.6 MB)
• 2 -Why interviews focus on problem-solving, not just theory.mp4 (23.6 MB)
10 - Section 10 Mock Interview Simulation
• 1 -Round 1 SQL + Behavioral Mix.mp4 (4.0 MB)
• 2 -Round 2 Data Modeling + System Design.mp4 (5.0 MB)
• 3 -Round 3 Cloud + End-to-End Case Study.mp4 (38.7 MB)
2 - SQL & Database Essentials (20 Questions)
• 1 -Q1. What is the difference between OLTP and OLAP systems.mp4 (8.5 MB)
• 10 -Q10. Explain the difference between DELETE, TRUNCATE, and DROP.mp4 (12.4 MB)
• 11 -Q11. What are ACID properties in databases, and why are they important.mp4 (12.6 MB)
• 12 -Q12. Explain the difference between WHERE vs HAVING clauses.mp4 (8.2 MB)
• 13 -Q13. What is a Stored Procedure vs a Function in SQL.mp4 (10.1 MB)
• 14 -Q14. What are Views in SQL, and when would you use them.mp4 (11.4 MB)
• 15 -Q15. Explain Aggregate Functions vs Analytic Functions.mp4 (14.1 MB)
• 16 -Q16. How do you handle NULL values in SQL queries.mp4 (11.7 MB)
• 17 -Q17. Explain the difference between INNER JOIN vs FULL OUTER JOIN with examples.mp4 (9.7 MB)
• 18 -Q18. What is a Self Join and when is it useful.mp4 (1.4 MB)
• 19 -Q19. Second highest salary from an Employee table.mp4 (14.4 MB)
• 2 -Q2. Explain INNER JOIN vs LEFT JOIN with examples.mp4 (6.0 MB)
• 20 -Q20. concept of Transactions and how to implement them in SQL.mp4 (12.0 MB)
• 3 -Q3. What are Window Functions in SQL and why are they useful.mp4 (7.4 MB)
• 4 -Q4. How would you optimize a slow SQL query.mp4 (12.0 MB)
• 5 -Q5. Explain Primary Key, Foreign Key, and Unique Key differences.mp4 (8.3 MB)
• 6 -Q6. (CTE) and how is it different from a Subquery.mp4 (9.9 MB)
• 7 -Q7. Explain UNION vs UNION ALL with examples.mp4 (7.5 MB)
• 8 -Q8. What is the difference between Normalization and Denormalization.mp4 (10.6 MB)
• 9 -Q9. What are Indexes in SQL and what types exist (Clustered vs Non-Clustered).mp4 (15.1 MB)
3 - Section 3 Data Warehousing & ETL (15 Questions)
• 1 -Q1. What is the difference between Data Warehouse, Data Lake, and Data Lakehouse.mp4 (16.3 MB)
• 10 -Q10. How do you design a surrogate key vs natural key in a warehouse.mp4 (11.2 MB)
• 11 -Q11. What are Orchestration tools (Airflow, ADF, Glue) and how do they differ.mp4 (11.2 MB)
• 12 -Q12. How do you handle late arriving dimensions in ETL.mp4 (13.6 MB)
• 14 -Q14. How do you handle CDC (Change Data Capture) in ETL pipelines.mp4 (12.8 MB)
• 15 -Q15. What are some common ETL performance optimization techniques.mp4 (14.5 MB)
• 2 -Q2. Explain Star Schema vs Snowflake Schema with examples.mp4 (11.2 MB)
• 3 -Q3. What are Fact Tables and Dimension Tables Give real-world examples.mp4 (10.0 MB)
• 4 -Q4. What are Slowly Changing Dimensions (SCDs) Explain different types (Type 1,.mp4 (16.6 MB)
• 5 -Q5. What is the difference between ETL and ELT processes.mp4 (15.0 MB)
• 6 -Q6. How do you handle schema changes in ETL pipelines.mp4 (19.6 MB)
• 7 -Q7. What are Incremental Load vs Full Load strategies in data pipelines.mp4 (8.5 MB)
• 8 -Q8. What are Data Quality checks in ETL, and why are they important.mp4 (16.1 MB)
• 9 -Q9. What is Data Partitioning and how does it help performance in DWH.mp4 (16.6 MB)
4 - Section 4 Big Data Ecosystem (15 Questions)
• 1 -Q1. What is the difference between (HDFS) and traditional file systems.mp4 (14.1 MB)
• 10 -Q10. How does Checkpointing and Caching work in Spark, and why are they importan.mp4 (13.4 MB)
• 11 -Q11. What is the difference between Batch Processing and Stream Processing.mp4 (10.4 MB)
• 12 -Q12. Explain Spark Structured Streaming and how it handles real-time data.mp4 (24.6 MB)
• 13 -Q13. What are Partitions in Spark, and how do they affect performance.mp4 (12.7 MB)
• 14 -Q14. What are some common Spark optimization techniques.mp4 (11.7 MB)
• 15 -How do you handle schema evolution and semi-structured data (JSON, Avro).mp4 (13.7 MB)
• 2 -Q2. Explain MapReduce and why it was important in the Hadoop ecosystem.mp4 (19.9 MB)
• 3 -Q3. What are the differences between RDD, DF, and Dataset in Apache Spark.mp4 (14.2 MB)
• 4 -Q4. Explain lazy evaluation in Spark and why it’s useful.mp4 (12.9 MB)
• 5 -Q5. What is a Shuffle in Spark, and how can you optimize shuffle operations.mp4 (23.2 MB)
• 6 -Q6. Compare Spark SQL vs Hive – when would you use one over the other.mp4 (18.2 MB)
• 7 -Q7. Explain the role of YARN vs Kubernetes in running big data jobs.mp4 (11.3 MB)
• 8 -Q8. What are Broadcast Joins in Spark, and when should you use them.mp4 (13.4 MB)
• 9 -Q9. What are Wide vs Narrow transformations in Spark.mp4 (20.1 MB)
5 - Section 5 Cloud Data Engineering (15 Questions)
• 1 -Q1.What is the difference between Data Lake and a Data Warehouse in the cloud.mp4 (18.0 MB)
• 10 -Q10. What are cross-region and cross-cloud data replication strategies.mp4 (14.7 MB)
• 11 -Q11. How do you implement data governance and compliance in cloud pipelines.mp4 (14.3 MB)
• 12 -Q12. What are managed streaming services.mp4 (12.3 MB)
• 13 -Q13. How does CDC (Change Data Capture) work in cloud-native tools.mp4 (11.5 MB)
• 14 -Q14. Explain Lakehouse architectures in the cloud.mp4 (12.8 MB)
• 15 -Q15. How do you monitor, log, and troubleshoot cloud data pipelines effectively.mp4 (14.3 MB)
• 2 -Q2. Compare AWS Glue, (ADF), and GCP Dataflow – when would you use each.mp4 (14.1 MB)
• 3 -Q3. Explain Serverless vs Cluster-based data processing in cloud platforms.mp4 (13.4 MB)
• 4 -Q4. What are best practices for designing data pipelines in the cloud.mp4 (13.4 MB)
• 5 -Q5. How do you implement data partitioning and clustering in cloud warehouses.mp4 (15.3 MB)
• 6 -Q6. What is auto-scaling, and how does it benefit cloud data pipelines.mp4 (12.1 MB)
• 7 -Q7. Compare Snowflake vs BigQuery vs Redshift – strengths and weaknesses.mp4 (15.6 MB)
• 8 -Q8. How does cost optimization work in cloud data engineering.mp4 (12.5 MB)
• 9 -Q9. Explain IAM best practices for securing cloud data pipelines.mp4 (12.6 MB)
6 - Section 6 Data Modeling & Architecture (12 Questions)
• 1 -Q1. What is Data Vault modeling, and how does it compare to KimballInmon.mp4 (13.7 MB)
• 10 -Q10. What is a multi-tenant data warehouse.mp4 (11.3 MB)
• 11 -Q11. How would you design a hybrid architecture combining batch and streaming.mp4 (1.8 MB)
• 12 -Q12. What are best practices for designing metadata-driven architectures.mp4 (13.3 MB)
• 2 -Q2. How do you design a schema for a real-time analytics pipeline.mp4 (12.6 MB)
• 3 -Q3. Difference between Normalization and Denormalization in data modeling.mp4 (12.7 MB)
• 4 -Q4. How do surrogate keys and natural keys differ, and when should each be used.mp4 (12.7 MB)
• 5 -Q5. How do you handle many-to-many relationships in data models.mp4 (11.2 MB)
• 6 -Q6. What is a Bridge Table, and when is it used in dimensional modeling.mp4 (10.2 MB)
• 7 -Q7. How do you design a schema for slowly arriving data.mp4 (14.5 MB)
• 8 -Q8. What are conformed dimensions.mp4 (13.2 MB)
• 9 -Q9. How do you approach schema evolution in dimensional models.mp4 (12.0 MB)
7 - Section 7 Python & Data Engineering Coding (10 Questions)
• 1 -Q1. How do you handle large datasets in Python without running out of memory.mp4 (12.1 MB)
• 2 -Q2. What is the difference between Pandas DataFrame vs PySpark DataFrame.mp4 (11.8 MB)
• 3 -Q3. How do you handle schema evolution in PySpark DataFrames.mp4 (12.9 MB)
• 4 -Q4. How do you optimize PySpark jobs written in Python.mp4 (15.7 MB)
• 5 -Q6. How do you implement error handling and retries in ETL pipelines.mp4 (15.7 MB)
• 6 -Q7. Data in different formats (CSV, JSON, Parquet, Avro) using pythonPySpark.mp4 (15.8 MB)
• 7 -Q8. Broadcast variables and accumulators in PySpark, and when would you use them.mp4 (10.8 MB)
• 8 -Q9. How do you implement unit testing and CICD for Python-based data pipelines.mp4 (12.1 MB)
• 9 -Q10. How do you use Python for orchestrating pipelines.mp4 (10.0 MB)
8 - Section 8 System Design for Data Engineers (8 Questions)
• 1 -Q1. How would you design a real-time data pipeline (end-to-end architecture).mp4 (20.3 MB)
• 2 -Q2. How do you design a batch data pipeline for large-scale processing.mp4 (10.0 MB)
• 3 -Q3. What’s the difference between streaming vs batch pipelines, and when to use.mp4 (19.4 MB)
• 4 -Q4. data ingestion system for heterogeneous sources (APIs, DBs, files, streams).mp4 (14.5 MB)
• 5 -Q5. How do you ensure fault tolerance and reliability in data pipelines.mp4 (21.8 MB)
• 6 -Q6. Design a data lakehouse architecture for both BI and ML use cases.mp4 (5.6 MB)
• 7 -Q7. backpressure and scaling in streaming systems (Kafka, Spark Streaming).mp4 (20.5 MB)
• 8 -Q8. data lineage, observability, and monitoring in large data platforms.mp4 (7.7 MB)
9 - Section 9 Behavioral & Scenario Questions
• 1 -Q1. Tell me about yourself (Data Engineer version).mp4 (10.4 MB)
• 2 -Q2. Describe a time when your data pipeline failed in production.mp4 (4.3 MB)
• 3 -Q3. How do you communicate with cross-functional teams (data scientists, analyst.mp4 (5.1 MB)
• 4 -Q4. What would you do if your pipeline delivered incorrect data to stakeholders.mp4 (7.9 MB)
• 5 -Q5. Project where you had to optimize a slow or expensive pipeline.mp4 (7.0 MB)
• 6 -Q6. How do you handle conflicting priorities between business requirements.mp4 (11.5 MB)
• 7 -Q7. Describe a situation where you had to learn a new tooltechnology quickly.mp4 (6.5 MB)
• Bonus Resources.txt (0.1 KB)