# Apache Beam & Dataflow (Data Engineering)

> PCollections, transforms (ParDo, GroupByKey), windowing, triggers, watermarks, Dataflow runner, autoscaling, templates

- 20 interview questions
- Senior
- [Interview Questions: Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions.md)

## 1. What is a PCollection in Apache Beam?

**Answer**

A PCollection is the primary data abstraction in Apache Beam. It represents a distributed, potentially unbounded dataset that can be processed in parallel. Unlike regular collections, a PCollection is immutable, meaning each transform creates a new PCollection rather than modifying the original.

## 2. What is the main difference between a bounded and unbounded PCollection?

**Answer**

A bounded PCollection has a finite, known size (like a file or table), while an unbounded one represents a potentially infinite data stream (like streaming events). This distinction affects how Beam processes data: bounded uses classic batch processing, while unbounded requires windowing and triggers to handle the continuous flow.

## 3. What is the role of the ParDo transform in Apache Beam?

**Answer**

ParDo (Parallel Do) is the most flexible transform in Apache Beam. It applies a user-defined function (DoFn) to each element of a PCollection in parallel. ParDo can produce zero, one, or multiple output elements for each input element, making it suitable for filtering, mapping, and flat-mapping.

## 17 more questions available

- How to use side inputs in a ParDo transform?
- What is the difference between GroupByKey and CoGroupByKey in Apache Beam?

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