Reviewed by Aditya Kumar · Last reviewed 2026-08-08
The transformation involves filtering the dataset, calculating a discounted sales value, and then aggregating by region to determine total revenue. This is typically achieved by applying a WHERE…
This easy-level SQL question appears frequently in data engineering interviews at companies like Warner Bros Discovery. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example. The expert answer includes a code example that demonstrates the implementation pattern.
The transformation involves filtering the dataset, calculating a discounted sales value, and then aggregating by region to determine total revenue. This is typically achieved by applying a WHERE clause for filtering, performing arithmetic in the SELECT list, and using GROUP BY with an aggregate function.
The optimal order of operations for these transformations is crucial for performance: Filter → Transform → Group.
WHERE sales > 1000 clause is applied first. This significantly reduces the number of rows processed in subsequent steps, minimizing I/O and computational overhead.sales 0.9. This calculation is performed on the filtered dataset. While an explicit discount column could be created using a subquery or Common Table Expression (CTE), directly calculating SUM(sales 0.9) is often more efficient when the intermediate column isn't needed for other purposes.GROUP BY region combines rows with the same region, and SUM(sales * 0.9) calculates the total revenue for each region from the post-discount sales.Here's the SQL solution demonstrating the logical flow:
SELECT
region,
SUM(sales * 0.9) AS total_revenue
FROM
sales
WHERE
sales > 1000
GROUP BY
region;
Filtering early is a best practice in data engineering. In cloud data warehouses like Snowflake or BigQuery, this leverages micro-partitions or clustering keys to reduce the amount of data scanned from storage. In distributed processing frameworks like Apache Spark, filtering before wide transformations (such as GROUP BY, which causes a data shuffle) minimizes the data transferred across the network and processed by executors, leading to substantial performance gains. The existing PySpark example df.filter(col('sales')>1000).withColumn('discount', col('sales')0.1).groupBy('region').agg(sum(col('sales')0.9).alias('revenue')) demonstrates this logical flow. While withColumn explicitly adds a discount column, the final aggregation sum(col('sales')*0.9) directly calculates the post-discount revenue. For this specific problem, an explicit discount column isn't strictly necessary for the final aggregate, but it could be useful for debugging or subsequent transformations.
Discuss how these transformations fit into an ELT pipeline, potentially orchestrated by tools like dbt, and the importance of handling data types and NULL values during calculations to prevent unexpected results.
Red Flag: Grouping before filtering—wrong totals. Pro-Move: 'We pushed filter to source (predicate pushdown) and used DECIMAL for revenue—no float rounding in financial reports.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.