Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why It Matters (Architectural Logic)**: Excel is common for business users; pipelines must handle it reliably. Define schema—inference is slow and inconsistent. Excel to Delta in Databricks: Read Excel via `pd.read_excel()` (small files) or `openpyxl`/`xlrd`, then convert:...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Nihilent. 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.
Why It Matters (Architectural Logic): Excel is common for business users; pipelines must handle it reliably. Define schema—inference is slow and inconsistent.
Excel to Delta in Databricks: Read Excel via pd.read_excel() (small files) or openpyxl/xlrd, then convert: spark_df = spark.createDataFrame(pd_df). Or use com.crealytics.spark.excel package: spark.read.format("com.crealytics.spark.excel").option("header", "true").load("dbfs:/path/file.xlsx"). Write Delta: spark_df.write.format("delta").mode("overwrite").save("/mnt/delta/table"). Best practices: define schema for consistency; handle multiple sheets; use Autoloader for recurring uploads; store in cloud object storage; run OPTIMIZE and VACUUM post-load.
Scalability Trade-offs: Pandas for <100MB; Spark Excel for larger. Use Autoloader for recurring. OPTIMIZE/VACUUM post-load.
Cost Implications: Excel parsing is CPU-bound. Avoid pd.read_excel on large files—OOM risk.
Pro-Move: Define schema; use Autoloader for recurring; OPTIMIZE post-load. Red Flag: pd.read_excel on 100MB+—OOM.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.