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Describe how to pass data between tasks in Airflow using XComs.

Spark/Big Dataeasy0.4 min read

**Why XCom matters**: Cross-task communication for DAG orchestration; misuse = performance and size issues. **Mechanism**: Push via return value or `xcom_push`; pull via `xcom_pull`. Stored in Airflow metadata DB by default. **Scalability trade-offs**: Default backend has ~1MB...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Citi
Key Concepts Tested
airflow

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Citi. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
72 words

Why XCom matters: Cross-task communication for DAG orchestration; misuse = performance and size issues. Mechanism: Push via return value or xcom_push; pull via xcom_pull. Stored in Airflow metadata DB by default. Scalability trade-offs: Default backend has ~1MB limit; large payloads = DB bloat. Cost implications: XCom in DB = latency for large data. Best practice: Keep XCom small (<1MB); use custom backend or external storage (S3) for paths; pass references, not data.

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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