data-engineering Interview Questions and answers

1. Data engineering Interview Questions and Answers 1. How do you perform capacity planning for a data pipeline? Steps to perform capacity planning for a data pipeline Analyze data volume, velocity, and variety. Account for peak load scenarios. Choose scalable storage solutions. Test pipelines for stress and load. 2. How do you handle Data Skew in distributed systems? Data Skew: Uneven distribution of data across partitions or nodes, causing performance bottlenecks. Solutions: Use hashing or range-based partitioning. Balance data during preprocessing. Use load balancing tools. 3. What is the difference between normalization and denormalization? Normalization is the process of organizing data to reduce redundancy and improve consistency, like breaking tables into smaller ones. Denormalization combines tables to optimize read performance, often at the cost of redundancy. 4. State the best practices for data pipeline security The best practices to achieve security in data pipeline are: Use encryption for data in transit (TLS) and at rest.

2. Implement access control and authentication mechanisms. Mask or tokenize sensitive data. Regularly audit and monitor pipelines for anomalies. 5. How do you decide between using SQL and NoSQL databases? SQL is suitable for handling structured, relational data with ACID compliance. e.g., MySQL, PostgreSQL. NoSQL is preferred for unstructured or semi-structured data, scalability, and flexibility. e.g., MongoDB, Cassandra. 6. How does Apache Kafka work? Apache Kafka is a distributed event-streaming platform used for real-time data pipelines. It uses Producers: Publish data to topics. Consumers: Subscribe to topics to process data. Brokers: Manage data storage and delivery 7. What are the differences between OLTP and OLAP? OLTP– Online Transaction Processing OLAP– Online Analytical Processing OLTP: Optimized for transactional tasks like CRUD operations. Examples: Banking, e- commerce. OLAP: Optimized for data analysis and querying large datasets. Examples: Business intelligence, reporting. 8. How does it Data Lake differ from a Data Warehouse? Data Lake stores raw, unstructured and semi-structured data. Example: Hadoop HDFS Data Warehouse stores structured and processed data optimized for querying.

3. Example: Snowflake Key Difference: Data lakes are schema-on-read; warehouses are schema-on-write. 9. What are the key components of a data pipeline? Network requests and responses in Playwright are handled by methods like: Data Ingestion: Collecting raw data from various sources. Data Processing: Cleaning, transforming, and enriching data. Data Storage: Saving data in warehouses or lakes. Data Orchestration: Automating workflows using tools like Apache Airflow. Data Monitoring: Ensuring pipeline reliability. 10. How does Apache Spark differ from Hadoop MapReduce? Apache Spark: An open-source distributed computing system for fast, in-memory processing. Difference: Spark processes data in-memory (faster), while Hadoop MapReduce uses disk-based processing. 11. How do you ensure data quality in a pipeline? Validation Rules: Ensure completeness, consistency, and accuracy. Data Profiling: Identify anomalies using tools like Apache Griffin. Automated Testing: Unit and integration tests. Monitoring Tools: Use systems like Great Expectations. 12. Explain the concept of Data Partitioning in distributed systems? Data partitioning divides a dataset into smaller parts for parallel processing, improving performance. Methods include range-based, hash-based, and list-based partitioning. 13. What are the different types of NoSQL databases? Types of NoSQL databases Key-Value Stores: Redis, DynamoDB.

4. Document Stores: MongoDB, CouchDB. Columnar Databases: Cassandra, HBase. Graph Databases: Neo4j, ArangoDB. 14. What is the CAP theorem in distributed systems? In a distributed system, the CAP theorem can achieve the following Consistency: Every read receives the most recent write. Availability: Every request receives a response. Partition Tolerance: The system continues to function during network splits. 15. State the uses of distributed cache in data pipelines A distributed cache stores frequently accessed data in-memory across multiple nodes, reducing latency. Example: Redis and Memcached. Data engineering Interview Questions and Answers 2025 https://www.credosystemz.com/data-engineering-interview-questions-and-answers/