Master SQL, Data Warehousing, Big Data, Cloud, Python, and System Design with 101 Real Interview Questions

What you'll learn

Confidently answer 101 of the most frequently asked Data Engineering interview questions across SQL, Big Data, Cloud, and System Design.

Master advanced SQL concepts such as joins, window functions, CTEs, indexing, and query optimization through real-world examples.

Understand Data Warehousing, ETL, and Data Modeling techniques (OLTP vs OLAP, Fact vs Dimension, Star vs Snowflake schema, Slowly Changing Dimensions, CDC).

Gain hands-on clarity in Big Data & Cloud technologies like Hadoop, Spark, Kafka, Snowflake, AWS, Azure, and GCP by exploring practical use cases.

Develop strong problem-solving and communication skills to tackle both technical and behavioral interview rounds with confidence.

Learn system design patterns for data pipelines (batch vs streaming, lakehouse architecture, real-time processing with Kafka + Spark).

Requirements

No prior experience as a Data Engineer is strictly required — this course is designed for both beginners and professionals preparing for interviews.

Basic knowledge of SQL (SELECT, JOIN, GROUP BY) will be helpful but not mandatory.

Familiarity with at least one programming language (Python, Java, or Scala) is a plus but not required.

Curiosity to learn and a desire to crack Data Engineering interviews at top companies.

A laptop/PC with internet connection to follow along with examples and practice queries.

Description

Are you preparing for a Data Engineering interview with top product-based companies, MNCs, or startups?This course is your one-stop preparation guide — designed to help you crack interviews at FANG/MANG and beyond.Inside, you'll find:-> 101 Most Asked Interview Questions — covering SQL, ETL, Data Warehousing, Big Data (Spark/Hadoop), Cloud Data Engineering (AWS/GCP/Azure), Python, Data Modeling, and System Design.-> Detailed Explanations with Real Examples — each question comes with slides + voiceover-style breakdowns to make concepts crystal clear.-> Scenario-Based & Behavioral Questions — learn how to answer "pipeline failure" or "wrong data in production" confidently.-> Mock Interview Simulations — practice with end-to-end rounds combining technical + behavioral questions.=> What makes this course different?Unlike theory-heavy courses, this one focuses on real-world, interview-style answers.Each answer is structured into:Slide 1: Question + Context (why it's asked)Slide 2: Key Points to Cover (must-have points)Voiceover Script: A natural, human-style explanation with real project scenariosBy the end of this course, you'll be able to:Answer any SQL, Big Data, or Cloud interview question with confidenceHandle system design and architecture questions for data platformsCommunicate behavioral answers like a proApproach real interviews with a structured preparation strategy=> Who is this course for?Aspiring Data Engineers preparing for their first job interviewsWorking professionals aiming to switch into FANG/MANG or Tier-1 companiesSoftware Developers, BI Engineers, and Analysts transitioning into Data EngineeringStudents or graduates looking to master interview skills before placements=> Why take this course?Because interviews don't test just knowledge — they test how you explain it.This course will teach you what to say, how to say it, and how to stand out from other candidates.=> Ready to land your dream job in Data Engineering?Enroll now and start your journey with Top 101 Data Engineering Interview Questions!Please Note: "This course contains the use of artificial intelligence."

Overview

Section 1: Introduction to the Course

Lecture 1 How to use this course (Slides + Voiceover transcripts + Practice approach)

Lecture 2 Why interviews focus on problem-solving, not just theory

Section 2: SQL & Database Essentials (20 Questions)

Lecture 3 Q1. What is the difference between OLTP and OLAP systems?

Lecture 4 Q2. Explain INNER JOIN vs LEFT JOIN with examples.

Lecture 5 Q3. What are Window Functions in SQL and why are they useful?

Lecture 6 Q4. How would you optimize a slow SQL query?

Lecture 7 Q5. Explain Primary Key, Foreign Key, and Unique Key differences.

Lecture 8 Q6. (CTE) and how is it different from a Subquery?

Lecture 9 Q7. Explain UNION vs UNION ALL with examples.

Lecture 10 Q8. What is the difference between Normalization and Denormalization?

Lecture 11 Q9. What are Indexes in SQL and what types exist (Clustered vs Non-Clustered)?

Lecture 12 Q10. Explain the difference between DELETE, TRUNCATE, and DROP.

Lecture 13 Q11. What are ACID properties in databases, and why are they important?

Lecture 14 Q12. Explain the difference between WHERE vs HAVING clauses.

Lecture 15 Q13. What is a Stored Procedure vs a Function in SQL?

Lecture 16 Q14. What are Views in SQL, and when would you use them?

Lecture 17 Q15. Explain Aggregate Functions vs Analytic Functions.

Lecture 18 Q16. How do you handle NULL values in SQL queries?

Lecture 19 Q17. Explain the difference between INNER JOIN vs FULL OUTER JOIN with examples.

Lecture 20 Q18. What is a Self Join and when is it useful?

Lecture 21 Q19. Second highest salary from an Employee table?

Lecture 22 Q20. concept of Transactions and how to implement them in SQL.

Section 3: Section 3: Data Warehousing & ETL (15 Questions)

Lecture 23 Q1. What is the difference between Data Warehouse, Data Lake, and Data Lakehouse

Lecture 24 Q2. Explain Star Schema vs Snowflake Schema with examples.

Lecture 25 Q3. What are Fact Tables and Dimension Tables? Give real-world examples.

Lecture 26 Q4. What are Slowly Changing Dimensions (SCDs)? Explain different types (Type 1,

Lecture 27 Q5. What is the difference between ETL and ELT processes?

Lecture 28 Q6. How do you handle schema changes in ETL pipelines?

Lecture 29 Q7. What are Incremental Load vs Full Load strategies in data pipelines?

Lecture 30 Q8. What are Data Quality checks in ETL, and why are they important?

Lecture 31 Q9. What is Data Partitioning and how does it help performance in DWH?

Lecture 32 Q10. How do you design a surrogate key vs natural key in a warehouse?

Lecture 33 Q11. What are Orchestration tools (Airflow, ADF, Glue) and how do they differ?

Lecture 34 Q12. How do you handle late arriving dimensions in ETL?

Lecture 35 Q13. What is Data Vault Modeling, and how does it compare to Kimball/Inmon?

Lecture 36 Q14. How do you handle CDC (Change Data Capture) in ETL pipelines?

Lecture 37 Q15. What are some common ETL performance optimization techniques?

Section 4: Section 4: Big Data Ecosystem (15 Questions)

Lecture 38 Q1. What is the difference between (HDFS) and traditional file systems?

Lecture 39 Q2. Explain MapReduce and why it was important in the Hadoop ecosystem.

Lecture 40 Q3. What are the differences between RDD, DF, and Dataset in Apache Spark?

Lecture 41 Q4. Explain lazy evaluation in Spark and why it's useful.

Lecture 42 Q5. What is a Shuffle in Spark, and how can you optimize shuffle operations?

Lecture 43 Q6. Compare Spark SQL vs Hive – when would you use one over the other?

Lecture 44 Q7. Explain the role of YARN vs Kubernetes in running big data jobs.

Lecture 45 Q8. What are Broadcast Joins in Spark, and when should you use them?

Lecture 46 Q9. What are Wide vs Narrow transformations in Spark?

Lecture 47 Q10. How does Checkpointing and Caching work in Spark, and why are they importan

Lecture 48 Q11. What is the difference between Batch Processing and Stream Processing?

Lecture 49 Q12. Explain Spark Structured Streaming and how it handles real-time data.

Lecture 50 Q13. What are Partitions in Spark, and how do they affect performance?

Lecture 51 Q14. What are some common Spark optimization techniques

Lecture 52 How do you handle schema evolution and semi-structured data (JSON, Avro)

Section 5: Section 5: Cloud Data Engineering (15 Questions)

Lecture 53 Q1.What is the difference between Data Lake and a Data Warehouse in the cloud?

Lecture 54 Q2. Compare AWS Glue, (ADF), and GCP Dataflow – when would you use each?

Lecture 55 Q3. Explain Serverless vs Cluster-based data processing in cloud platforms.

Lecture 56 Q4. What are best practices for designing data pipelines in the cloud?

Lecture 57 Q5. How do you implement data partitioning and clustering in cloud warehouses

Lecture 58 Q6. What is auto-scaling, and how does it benefit cloud data pipelines?

Lecture 59 Q7. Compare Snowflake vs BigQuery vs Redshift – strengths and weaknesses.

Lecture 60 Q8. How does cost optimization work in cloud data engineering?

Lecture 61 Q9. Explain IAM best practices for securing cloud data pipelines

Lecture 62 Q10. What are cross-region and cross-cloud data replication strategies?

Lecture 63 Q11. How do you implement data governance and compliance in cloud pipelines?

Lecture 64 Q12. What are managed streaming services?

Lecture 65 Q13. How does CDC (Change Data Capture) work in cloud-native tools?

Lecture 66 Q14. Explain Lakehouse architectures in the cloud ?

Lecture 67 Q15. How do you monitor, log, and troubleshoot cloud data pipelines effectively?

Section 6: Section 6: Data Modeling & Architecture (12 Questions)

Lecture 68 Q1. What is Data Vault modeling, and how does it compare to Kimball/Inmon?

Lecture 69 Q2. How do you design a schema for a real-time analytics pipeline?

Lecture 70 Q3. Difference between Normalization and Denormalization in data modeling?

Lecture 71 Q4. How do surrogate keys and natural keys differ, and when should each be used?

Lecture 72 Q5. How do you handle many-to-many relationships in data models?

Lecture 73 Q6. What is a Bridge Table, and when is it used in dimensional modeling?

Lecture 74 Q7. How do you design a schema for slowly arriving data ?

Lecture 75 Q8. What are conformed dimensions?

Lecture 76 Q9. How do you approach schema evolution in dimensional models?

Lecture 77 Q10. What is a multi-tenant data warehouse ?

Lecture 78 Q11. How would you design a hybrid architecture combining batch and streaming?

Lecture 79 Q12. What are best practices for designing metadata-driven architectures?

Section 7: Section 7: Python & Data Engineering Coding (10 Questions)

Lecture 80 Q1. How do you handle large datasets in Python without running out of memory?

Lecture 81 Q2. What is the difference between Pandas DataFrame vs PySpark DataFrame?

Lecture 82 Q3. How do you handle schema evolution in PySpark DataFrames?

Lecture 83 Q4. How do you optimize PySpark jobs written in Python?

Lecture 84 Q6. How do you implement error handling and retries in ETL pipelines?

Lecture 85 Q7. Data in different formats (CSV, JSON, Parquet, Avro) using python/PySpark?

Lecture 86 Q8. Broadcast variables and accumulators in PySpark, and when would you use them

Lecture 87 Q9. How do you implement unit testing and CI/CD for Python-based data pipelines?

Lecture 88 Q10. How do you use Python for orchestrating pipelines ?

Section 8: Section 8: System Design for Data Engineers (8 Questions)

Lecture 89 Q1. How would you design a real-time data pipeline (end-to-end architecture)?

Lecture 90 Q2. How do you design a batch data pipeline for large-scale processing?

Lecture 91 Q3. What's the difference between streaming vs batch pipelines, and when to use

Lecture 92 Q4. data ingestion system for heterogeneous sources (APIs, DBs, files, streams)?

Lecture 93 Q5. How do you ensure fault tolerance and reliability in data pipelines?

Lecture 94 Q6. Design a data lakehouse architecture for both BI and ML use cases?

Lecture 95 Q7. backpressure and scaling in streaming systems (Kafka, Spark Streaming)?

Lecture 96 Q8. data lineage, observability, and monitoring in large data platforms?

Section 9: Section 9: Behavioral & Scenario Questions

Lecture 97 Q1. Tell me about yourself (Data Engineer version).

Lecture 98 Q2. Describe a time when your data pipeline failed in production.

Lecture 99 Q3. How do you communicate with cross-functional teams (data scientists, analyst

Lecture 100 Q4. What would you do if your pipeline delivered incorrect data to stakeholders?

Lecture 101 Q5. Project where you had to optimize a slow or expensive pipeline.

Lecture 102 Q6. How do you handle conflicting priorities between business requirements ?

Lecture 103 Q7. Describe a situation where you had to learn a new tool/technology quickly ?

Section 10: Section 10: Mock Interview Simulation

Lecture 104 Round 1: SQL + Behavioral Mix

Lecture 105 Round 2: Data Modeling + System Design

Lecture 106 Round 3: Cloud + End-to-End Case Study

Aspiring Data Engineers who want to break into the field and prepare for their first job interviews.,Working Data Engineers aiming to transition into top product companies, MNCs, or startups.,Software Developers, Analysts, or BI Engineers who want to move into data engineering roles.,Students and fresh graduates looking to strengthen their interview readiness with real-world examples.,Anyone preparing for technical interviews that include SQL, data warehousing, big data, cloud, or system design questions.