Prepare for data engineering interviews with practical explanations of dimensional modeling, star and snowflake schemas, SCD Type 2, late-arriving data, many-to-many relationships, fact table grain, and denormalization. How to use this guide: Start with the concise interview answer, walk through the example, and explain the tradeoffs. Real interviewers want to hear how you would apply the concepts, not just definitions. 1. How would you design a dimensional model for an e-commerce business? Interview answer: Begin with the business processes and analytical questions, not the table names. For e-commerce, typical processes are order placement, payment, shipment, return, and inventory snapshots. Define a separate fact table at the appropriate grain for each process and connect it to conformed dimensions. Example dimensional model Table Grain Example columns fact_order_line One line item on an order order_id, line_id, customer_key, product_key, order_date_key, quantity, net_sales fact_...
Introduction: Data engineering is a critical aspect of modern businesses that rely on data-driven decision-making. However, the effectiveness of data engineering depends on the quality of data it produces. Poor data quality can lead to incorrect decisions, wasted resources, and lost opportunities. Therefore, it's important to implement best practices for data quality in data engineering. In this blog post, we will discuss the tips and strategies for ensuring data quality in data engineering. 1. Establish Data Governance: Data governance refers to the process of defining policies, procedures, and standards for data management. By establishing data governance, you can ensure that data is accurate, complete, and consistent across the organization. This can be achieved through the use of data quality rules, data validation, and data cleansing techniques. 2. Define Data Architecture: Data architecture is the blueprint that outlines the structure of data within an organization. By defini...