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Home/Questions/Spark/Big Data/Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?

Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?

Spark/Big Dataeasy0.6 min readPremium
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
Dunnhumby
Key Concepts Tested
airflowsql
Expert AnswerPremium
113 wordsInterview-ready
**Situation**: Multi-DB ingestion DAG runs sequentially; SLA at risk; DB teams report connection exhaustion. **Task**: Increase throughput without overloading source DBs or Airflow. **Action**: (1) **TaskGroup per DB**—isolate connection pools; `max_active_tasks_per_dag` limits concurrency per DB. (2) **Dynamic task mapping** (Airflow 2.3+): `@task` returns list; `expand()` creates N tasks. (3) **Connection pooling**—SQLAlchemy pool_size, max_overflow; one connection per worker, not per task....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Dunnhumby. The answer also includes follow-up discussion points that interviewers commonly explore.

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