| 1 -How to use this course (Slides + Voiceover transcripts + Practice approach).mp4 | 11.6 MB | ||
| 1 -Q1. How do you handle large datasets in Python without running out of memory.mp4 | 12.1 MB | ||
| 1 -Q1. How would you design a real-time data pipeline (end-to-end architecture).mp4 | 20.3 MB | ||
| 1 -Q1. Tell me about yourself (Data Engineer version).mp4 | 10.4 MB | ||
| 1 -Q1. What is Data Vault modeling, and how does it compare to KimballInmon.mp4 | 13.7 MB | ||
| 1 -Q1. What is the difference between (HDFS) and traditional file systems.mp4 | 14.1 MB | ||
| 1 -Q1. What is the difference between Data Warehouse, Data Lake, and Data Lakehouse.mp4 | 16.3 MB | ||
| 1 -Q1. What is the difference between OLTP and OLAP systems.mp4 | 8.5 MB | ||
| 1 -Q1.What is the difference between Data Lake and a Data Warehouse in the cloud.mp4 | 18 MB | ||
| 1 -Round 1 SQL + Behavioral Mix.mp4 | 4 MB | ||
| 10 -Q10. Explain the difference between DELETE, TRUNCATE, and DROP.mp4 | 12.4 MB | ||
| 10 -Q10. How do you design a surrogate key vs natural key in a warehouse.mp4 | 11.2 MB | ||
| 10 -Q10. How does Checkpointing and Caching work in Spark, and why are they importan.mp4 | 13.4 MB | ||
| 10 -Q10. What are cross-region and cross-cloud data replication strategies.mp4 | 14.7 MB | ||
| 10 -Q10. What is a multi-tenant data warehouse.mp4 | 11.3 MB | ||
| 11 -Q11. How do you implement data governance and compliance in cloud pipelines.mp4 | 14.3 MB | ||
| 11 -Q11. How would you design a hybrid architecture combining batch and streaming.mp4 | 1.8 MB | ||
| 11 -Q11. What are ACID properties in databases, and why are they important.mp4 | 12.6 MB | ||
| 11 -Q11. What are Orchestration tools (Airflow, ADF, Glue) and how do they differ.mp4 | 11.2 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 | ||
| 12 -Q12. Explain the difference between WHERE vs HAVING clauses.mp4 | 8.2 MB | ||
| 12 -Q12. How do you handle late arriving dimensions in ETL.mp4 | 13.6 MB | ||
| 12 -Q12. What are best practices for designing metadata-driven architectures.mp4 | 13.3 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 | ||
| 13 -Q13. What are Partitions in Spark, and how do they affect performance.mp4 | 12.7 MB | ||
| 13 -Q13. What is a Stored Procedure vs a Function in SQL.mp4 | 10.1 MB | ||
| 14 -Q14. Explain Lakehouse architectures in the cloud.mp4 | 12.8 MB | ||
| 14 -Q14. How do you handle CDC (Change Data Capture) in ETL pipelines.mp4 | 12.8 MB | ||
| 14 -Q14. What are Views in SQL, and when would you use them.mp4 | 11.4 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 | ||
| 15 -Q15. Explain Aggregate Functions vs Analytic Functions.mp4 | 14.1 MB | ||
| 15 -Q15. How do you monitor, log, and troubleshoot cloud data pipelines effectively.mp4 | 14.3 MB | ||
| 15 -Q15. What are some common ETL performance optimization techniques.mp4 | 14.5 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. Compare AWS Glue, (ADF), and GCP Dataflow – when would you use each.mp4 | 14.1 MB | ||
| 2 -Q2. Describe a time when your data pipeline failed in production.mp4 | 4.3 MB | ||
| 2 -Q2. Explain INNER JOIN vs LEFT JOIN with examples.mp4 | 6 MB | ||
| 2 -Q2. Explain MapReduce and why it was important in the Hadoop ecosystem.mp4 | 19.9 MB | ||
| 2 -Q2. Explain Star Schema vs Snowflake Schema with examples.mp4 | 11.2 MB | ||
| 2 -Q2. How do you design a batch data pipeline for large-scale processing.mp4 | 10 MB | ||
| 2 -Q2. How do you design a schema for a real-time analytics pipeline.mp4 | 12.6 MB | ||
| 2 -Q2. What is the difference between Pandas DataFrame vs PySpark DataFrame.mp4 | 11.8 MB | ||
| 2 -Round 2 Data Modeling + System Design.mp4 | 5 MB | ||
| 2 -Why interviews focus on problem-solving, not just theory.mp4 | 23.6 MB | ||
| 20 -Q20. concept of Transactions and how to implement them in SQL.mp4 | 12 MB | ||
| 3 -Q3. Difference between Normalization and Denormalization in data modeling.mp4 | 12.7 MB | ||
| 3 -Q3. Explain Serverless vs Cluster-based data processing in cloud platforms.mp4 | 13.4 MB | ||
| 3 -Q3. How do you communicate with cross-functional teams (data scientists, analyst.mp4 | 5.1 MB | ||
| 3 -Q3. How do you handle schema evolution in PySpark DataFrames.mp4 | 12.9 MB | ||
| 3 -Q3. What are Fact Tables and Dimension Tables Give real-world examples.mp4 | 10 MB | ||
| 3 -Q3. What are Window Functions in SQL and why are they useful.mp4 | 7.4 MB | ||
| 3 -Q3. What are the differences between RDD, DF, and Dataset in Apache Spark.mp4 | 14.2 MB | ||
| 3 -Q3. What’s the difference between streaming vs batch pipelines, and when to use.mp4 | 19.4 MB | ||
| 3 -Round 3 Cloud + End-to-End Case Study.mp4 | 38.7 MB | ||
| 4 -Q4. Explain lazy evaluation in Spark and why it’s useful.mp4 | 12.9 MB | ||
| 4 -Q4. How do surrogate keys and natural keys differ, and when should each be used.mp4 | 12.7 MB | ||
| 4 -Q4. How do you optimize PySpark jobs written in Python.mp4 | 15.7 MB | ||
| 4 -Q4. How would you optimize a slow SQL query.mp4 | 12 MB | ||
| 4 -Q4. What are Slowly Changing Dimensions (SCDs) Explain different types (Type 1,.mp4 | 16.6 MB | ||
| 4 -Q4. What are best practices for designing data pipelines in the cloud.mp4 | 13.4 MB | ||
| 4 -Q4. What would you do if your pipeline delivered incorrect data to stakeholders.mp4 | 7.9 MB | ||
| 4 -Q4. data ingestion system for heterogeneous sources (APIs, DBs, files, streams).mp4 | 14.5 MB | ||
| 5 -Q5. Explain Primary Key, Foreign Key, and Unique Key differences.mp4 | 8.3 MB | ||
| 5 -Q5. How do you ensure fault tolerance and reliability in data pipelines.mp4 | 21.8 MB | ||
| 5 -Q5. How do you handle many-to-many relationships in data models.mp4 | 11.2 MB | ||
| 5 -Q5. How do you implement data partitioning and clustering in cloud warehouses.mp4 | 15.3 MB | ||
| 5 -Q5. Project where you had to optimize a slow or expensive pipeline.mp4 | 7 MB | ||
| 5 -Q5. What is a Shuffle in Spark, and how can you optimize shuffle operations.mp4 | 23.2 MB | ||
| 5 -Q5. What is the difference between ETL and ELT processes.mp4 | 15 MB | ||
| 5 -Q6. How do you implement error handling and retries in ETL pipelines.mp4 | 15.7 MB | ||
| 6 -Q6. (CTE) and how is it different from a Subquery.mp4 | 9.9 MB | ||
| 6 -Q6. Compare Spark SQL vs Hive – when would you use one over the other.mp4 | 18.2 MB | ||
| 6 -Q6. Design a data lakehouse architecture for both BI and ML use cases.mp4 | 5.6 MB | ||
| 6 -Q6. How do you handle conflicting priorities between business requirements.mp4 | 11.5 MB | ||
| 6 -Q6. How do you handle schema changes in ETL pipelines.mp4 | 19.6 MB | ||
| 6 -Q6. What is a Bridge Table, and when is it used in dimensional modeling.mp4 | 10.2 MB | ||
| 6 -Q6. What is auto-scaling, and how does it benefit cloud data pipelines.mp4 | 12.1 MB | ||
| 6 -Q7. Data in different formats (CSV, JSON, Parquet, Avro) using pythonPySpark.mp4 | 15.8 MB | ||
| 7 -Q7. Compare Snowflake vs BigQuery vs Redshift – strengths and weaknesses.mp4 | 15.6 MB | ||
| 7 -Q7. Describe a situation where you had to learn a new tooltechnology quickly.mp4 | 6.5 MB | ||
| 7 -Q7. Explain UNION vs UNION ALL with examples.mp4 | 7.5 MB | ||
| 7 -Q7. Explain the role of YARN vs Kubernetes in running big data jobs.mp4 | 11.3 MB | ||
| 7 -Q7. How do you design a schema for slowly arriving data.mp4 | 14.5 MB | ||
| 7 -Q7. What are Incremental Load vs Full Load strategies in data pipelines.mp4 | 8.5 MB | ||
| 7 -Q7. backpressure and scaling in streaming systems (Kafka, Spark Streaming).mp4 | 20.5 MB | ||
| 7 -Q8. Broadcast variables and accumulators in PySpark, and when would you use them.mp4 | 10.8 MB | ||
| 8 -Q8. How does cost optimization work in cloud data engineering.mp4 | 12.5 MB | ||
| 8 -Q8. What are Broadcast Joins in Spark, and when should you use them.mp4 | 13.4 MB | ||
| 8 -Q8. What are Data Quality checks in ETL, and why are they important.mp4 | 16.1 MB | ||
| 8 -Q8. What are conformed dimensions.mp4 | 13.2 MB | ||
| 8 -Q8. What is the difference between Normalization and Denormalization.mp4 | 10.6 MB | ||
| 8 -Q8. data lineage, observability, and monitoring in large data platforms.mp4 | 7.7 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 MB | ||
| 9 -Q9. Explain IAM best practices for securing cloud data pipelines.mp4 | 12.6 MB | ||
| 9 -Q9. How do you approach schema evolution in dimensional models.mp4 | 12 MB | ||
| 9 -Q9. What are Indexes in SQL and what types exist (Clustered vs Non-Clustered).mp4 | 15.1 MB | ||
| 9 -Q9. What are Wide vs Narrow transformations in Spark.mp4 | 20.1 MB | ||
| 9 -Q9. What is Data Partitioning and how does it help performance in DWH.mp4 | 16.6 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 107 total files | |||
Top 101 Data Engineering Interview Questions
https://WebToolTip.com
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 53m | Size: 1.31 GB
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.
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| 3.8 GB | freecoursewb | 5 months | 12 | 2 |
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