Data Warehouse Engineer interview questions

A Data Warehouse is a centralized repository that stores integrated data from multiple sources, enabling easy retrieval and analysis for decision-making purposes.

The key components include the database server, ETL (Extract, Transform, Load) tools, metadata, and access tools for querying and reporting.

ETL stands for Extract, Transform, Load. It's a process that involves extracting data from various sources, transforming it into a standardized format, and loading it into the Data Warehouse for analysis and reporting.

OLTP (Online Transaction Processing) is designed for transactional tasks and real-time operations, while OLAP (Online Analytical Processing) is used for data analysis, focusing on complex queries and data warehousing.

Common data modeling techniques include Star Schema, Snowflake Schema, Fact Constellation Schema, and Data Vault.

Ensuring data quality involves data cleansing during ETL, establishing and adhering to data governance policies, and continuously monitoring data accuracy and consistency.

Metadata provides information about the data within the Data Warehouse, including its source, transformations applied, usage, meaning, and structure, facilitating easier management and utilization.

Common tools and technologies in Data Warehousing include SQL, data integration tools like Informatica or Talend, data modeling tools, and database management systems like Oracle, SQL Server, or Amazon Redshift.

Incremental loading involves identifying and processing only the new or modified data since the last load, ensuring efficiency and minimizing resource usage.

Performance optimization techniques include indexing, partitioning, materialized views, query optimization, and hardware resource optimization to ensure efficient data retrieval and processing.