Top 25 Data Engineering Interview Questions and Answers

Top 25 Data Engineering Interview Questions and Answers

Data engineering is the backbone of modern analytics and machine learning. This guide explores the top 10 frequently asked interview questions with detailed answers, practical examples, and key insights.

1. Difference between OLTP and OLAP

Summary: OLTP handles transactions, OLAP handles analytics.

Description: OLTP systems are optimized for inserts/updates (e.g., e-commerce checkout), while OLAP systems are optimized for queries and aggregations (e.g., dashboards).

  • Best for: OLTP → day-to-day operations; OLAP → reporting
  • Limitations: OLTP not suitable for heavy analytics
  • Example use: Amazon checkout (OLTP) vs. Amazon sales dashboard (OLAP).

2. What is Data Partitioning?

Summary: Splits large datasets into smaller chunks.

Description: Partitioning improves performance by scanning only relevant data. Common in Hive, Spark, and BigQuery.

  • Best for: Big data queries
  • Limitations: Poor partition choice can slow queries
  • Example use: Partitioning logs by date for faster queries.

3. Data Lake vs Data Warehouse

Summary: Data lake stores raw data, warehouse stores structured data.

Description: Data lakes support schema-on-read for ML and exploration, while warehouses use schema-on-write for BI and reporting.

  • Best for: Data lake → ML; Warehouse → BI
  • Limitations: Data lakes can become “data swamps” if unmanaged
  • Example use: Raw IoT sensor data in lake, curated sales data in warehouse.

4. How does Apache Kafka ensure reliability?

Summary: Kafka uses replication and persistence.

Description: Messages are written to disk and replicated across brokers. Consumers track offsets to avoid loss.

  • Best for: Real-time pipelines
  • Limitations: Requires careful cluster management
  • Example use: Streaming clickstream data for analytics.

5. What are Slowly Changing Dimensions (SCD)?

Summary: Techniques to handle changes in dimension data.

Description: SCD Type 1 overwrites, Type 2 adds new rows, Type 3 adds new columns.

  • Best for: Customer or product history tracking
  • Limitations: Type 2 increases storage
  • Example use: Tracking customer address changes.

6. Batch vs Stream Processing

Summary: Batch = scheduled jobs, Stream = real-time.

Description: Batch handles large volumes periodically, stream handles continuous flows instantly.

  • Best for: Batch → ETL jobs; Stream → fraud detection
  • Limitations: Stream requires complex infrastructure
  • Example use: Nightly sales reports (batch) vs. live fraud alerts (stream).

7. How to Optimize Spark Jobs?

Summary: Use partitioning, caching, efficient joins.

Description: Spark performance improves with partitioning, avoiding shuffles, caching datasets, and using Parquet/ORC formats.

  • Best for: Large-scale ETL
  • Limitations: Misconfigured partitions can hurt performance
  • Example use: Optimizing joins in Spark with broadcast variables.

8. What is CAP Theorem?

Summary: Distributed systems can guarantee only 2 of 3: Consistency, Availability, Partition Tolerance.

Description: Example: Cassandra favors Availability + Partition Tolerance, while RDBMS favors Consistency.

  • Best for: Designing distributed databases
  • Limitations: Trade-offs required
  • Example use: Choosing Cassandra for high availability systems.

9. Why is Schema Evolution Important?

Summary: Allows changes without breaking pipelines.

Description: Formats like Avro and Parquet support adding/removing columns safely.

  • Best for: Long-term data storage
  • Limitations: Complex evolution can cause compatibility issues
  • Example use: Adding a phone number column to customer data.

10. Challenges in ETL Pipelines

Summary: ETL pipelines face schema, quality, and scale issues.

Description: Common challenges include schema drift, duplicates, late-arriving data, and monitoring failures.

  • Best for: Data integration projects
  • Limitations: High maintenance overhead
  • Example use: Handling late-arriving sales transactions in ETL.

11. SQL vs NoSQL

Summary: SQL is relational, NoSQL is non-relational.

Description: SQL uses structured schema, NoSQL supports flexible schema.

  • Best for: SQL → structured; NoSQL → unstructured
  • Limitations: NoSQL lacks joins
  • Example use: MongoDB for JSON docs.

12. Explain Data Sharding

Summary: Splitting data across multiple servers.

Description: Improves scalability and performance.

  • Best for: Large distributed DBs
  • Limitations: Complex joins
  • Example use: Sharding user data by region.

13. Hadoop vs Spark

Summary: Hadoop = batch, Spark = in-memory.

Description: Spark is faster due to in-memory processing.

  • Best for: Real-time analytics
  • Limitations: Spark requires more memory
  • Example use: Streaming analytics with Spark.

14. What is Data Skew in Spark?

Summary: Uneven distribution of data across partitions.

Description: Data skew causes some tasks to process far more data than others, slowing jobs.

  • Best for: Identifying bottlenecks
  • Limitations: Hard to balance automatically
  • Example use: Skewed join keys causing slow performance.

15. Difference between ETL and ELT

Summary: ETL transforms before loading, ELT transforms after loading.

Description: ETL is common in traditional warehouses, ELT is common in cloud-native systems.

  • Best for: ETL → legacy systems; ELT → cloud warehouses
  • Limitations: ETL slower for big data
  • Example use: Snowflake ELT pipelines.

16. What is a Star Schema?

Summary: Central fact table linked to dimension tables.

Description: Simplifies queries and is common in data warehouses.

  • Best for: BI reporting
  • Limitations: Can duplicate dimension data
  • Example use: Sales fact table linked to customer and product dimensions.

17. Snowflake vs Redshift

Summary: Both are cloud warehouses but differ in architecture.

Description: Snowflake separates compute/storage, Redshift tightly couples them.

  • Best for: Snowflake → elasticity; Redshift → AWS integration
  • Limitations: Redshift scaling is harder
  • Example use: Snowflake for variable workloads.

18. What is a Data Pipeline?

Summary: Automated flow of data from source to destination.

Description: Includes ingestion, transformation, and storage steps.

  • Best for: Automating ETL
  • Limitations: Requires monitoring
  • Example use: Kafka → Spark → Warehouse pipeline.

19. What is Data Governance?

Summary: Framework for managing data availability, usability, and security.

Description: Ensures compliance and quality across data systems.

  • Best for: Enterprises with sensitive data
  • Limitations: Can slow agility
  • Example use: GDPR compliance policies.

20. What is a Data Catalog?

Summary: Metadata repository for datasets.

Description: Helps discover, understand, and govern datasets.

  • Best for: Large organizations
  • Limitations: Needs regular updates
  • Example use: Using AWS Glue Data Catalog.

21. Difference between Structured and Unstructured Data

Summary: Structured = tabular, Unstructured = free-form.

Description: Structured fits RDBMS; unstructured includes text, images, video.

  • Best for: Structured → BI; Unstructured → ML
  • Limitations: Unstructured harder to query
  • Example use: Tweets as unstructured data.

22. What is Data Lineage?

Summary: Tracks data flow from source to destination.

Description: Helps debug pipelines and ensure compliance.

  • Best for: Auditing
  • Limitations: Complex in large systems
  • Example use: Tracing a report back to raw logs.

23. Difference between Data Quality and Data Integrity

Summary: Quality = accuracy, Integrity = consistency.

Description: Both ensure reliable analytics.

  • Best for: Reliable reporting
  • Limitations: Requires monitoring
  • Example use: Validating customer emails.

24. What is a Data Mart?

Summary: Subset of a data warehouse focused on a department.

Description: Provides tailored analytics for teams.

  • Best for: Department-specific reporting
  • Limitations: Can duplicate data
  • Example use: Marketing data mart.

25. What is a Fact Table?

Summary: Central table storing measurable events.

Description: Linked to dimensions in star schema.

  • Best for: BI queries
  • Limitations: Can grow very large
  • Example use: Sales transactions fact table.

Tips for Data Engineering Interviews

  • Understand fundamentals: OLTP vs OLAP, batch vs stream.
  • Know tools: Spark, Kafka, Hive, Parquet.
  • Think trade-offs: CAP theorem, partitioning strategies.

Frequently Asked Questions

Are these questions commonly asked?

Yes, they cover core concepts in most data engineering interviews.

Do I need to know tools in depth?

Understanding Spark, Kafka, and data warehouses is essential for practical interviews.

Which topics are most important?

Partitioning, schema evolution, and ETL challenges are frequently emphasized.

 

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