Data Warehouse: A structured repository for processed, cleaned, and aggregated data. Optimized for analytics and reporting. Uses schema-on-write — data is structured before loading.

Data Lake: A repository that stores raw data in its native format. Schema-on-read — data is structured when read. Supports structured, semi-structured, and unstructured data.

Key differences:

• Data Warehouse — structured, schema-on-write, expensive, faster queries
• Data Lake — unstructured, schema-on-read, cost-effective, more flexible

OLTP (Online Transaction Processing): Systems designed for transactional data (e.g., e-commerce, banking). Characterized by high volume of simple queries, ACID compliance, and normalized data.

OLAP (Online Analytical Processing): Systems designed for analytics and reporting. Uses denormalized data (star/snowflake schemas), supports complex queries, and is optimized for read-heavy workloads.

Key differences:

• OLTP — writes-heavy, normalized, ACID, row-oriented
• OLAP — reads-heavy, denormalized, column-oriented, historical data

A data pipeline is a series of steps that move data from source to destination, transforming it along the way.

Components:

• Data Sources: Where data comes from (databases, APIs, files, etc.)
• Ingestion: Extracting data from sources
• Transformation: Cleaning, validating, aggregating data
• Storage: Where data is stored (warehouse, lake, etc.)
• Orchestration: Managing and scheduling the pipeline
• Monitoring: Ensuring data quality and pipeline health

INNER JOIN: Returns records that have matching values in both tables.

LEFT JOIN: Returns all records from the left table, and matched records from the right table. Unmatched right records are NULL.

RIGHT JOIN: Returns all records from the right table, and matched records from the left table. Unmatched left records are NULL.

FULL OUTER JOIN: Returns all records when there is a match in either left or right table.

CROSS JOIN: Returns all combinations of rows from both tables (cartesian product).

Normalization is the process of organizing data to reduce redundancy and improve data integrity. It involves splitting tables into smaller, related tables.

Normal Forms:

• 1NF: Eliminate repeating groups, ensure atomic values
• 2NF: Remove partial dependencies
• 3NF: Remove transitive dependencies
• BCNF: Advanced normalization for complex dependencies

Benefits: Reduced redundancy, improved data integrity, easier maintenance.

Star Schema: A dimensional modeling approach with a central fact table surrounded by dimension tables. Denormalized, simple, and optimized for query performance.

Snowflake Schema: An extension of star schema where dimension tables are further normalized into sub-dimensions. More complex, but saves storage space.

Key differences:

• Star — simpler, faster queries, more storage
• Snowflake — more complex, slower queries, less storage

SCD Type 1: Overwrites old data with new data. No history is maintained. Used when historical changes are not important.

SCD Type 2: Tracks historical changes by adding new rows for each change. Uses a flag or date range to track active records. Used when historical tracking is required.

Example: Customer address changes — Type 1 overwrites, Type 2 adds a new row with effective dates.

ETL (Extract, Transform, Load): Data is extracted from sources, transformed before loading into the destination. Traditional approach, works well with data warehouses.

ELT (Extract, Load, Transform): Data is extracted, loaded into the destination, and transformed in the destination. Uses modern data lakes and cloud platforms like Snowflake, BigQuery.

Key differences:

• ETL — transformation before loading, more processing time, less storage
• ELT — transformation after loading, faster loading, more storage, more flexibility

Approach: Use streaming technologies like Apache Kafka, Apache Flink, or AWS Kinesis.

Components:

• Data Sources: Real-time sources (clickstream, IoT, logs)
• Message Queue: Kafka for buffering and decoupling
• Stream Processor: Flink or Spark Streaming for real-time processing
• Storage: Real-time data in a time-series database or data lake
• Visualization: Real-time dashboards (Tableau, Power BI, Grafana)
• Monitoring: Pipeline health and data quality

Best practices for handling failures:

• Retry Logic: Automatically retry failed steps with exponential backoff
• Checkpointing: Save progress to restart from the failure point
• Idempotency: Design pipelines that can be re-run without causing duplicates
• Monitoring & Alerts: Set up monitoring and alerting for failures
• Orchestration: Use tools like Apache Airflow for workflow management
• Data Quality Checks: Validate data at each step to catch issues early

SQL is the #1 skill for data engineers. Python programming, ETL tools, and cloud platforms are also critical.

Practice SQL on LeetCode and HackerRank. Use platforms like StrataScratch for data engineering-specific questions. Build your own data pipelines on the cloud.

Include 2-3 end-to-end data pipeline projects. Showcase your ability to extract, transform, and load data. Use tools like Airflow, Spark, and cloud platforms.

Very important. Senior roles often include system design questions. Practice designing real-time and batch pipelines.

Common mistakes include not practicing SQL enough, forgetting to ask clarifying questions, and not explaining your thought process clearly.