Apache Beam

Unified model for batch and streaming data processing

Apache Beam is a unified model for batch and streaming data processing that provides a way to define and execute data processing pipelines across a variety of distributed processing backends.

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Big Data Engineer - Apache Beam Example Questions

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Question 1

What is the difference between a pipeline and a PTransform in Apache Beam?

A PTransform is a collection of pipelines that are executed to produce a result A PTransform is a NoSQL database A pipeline is a distributed file system A pipeline is a collection of PTransforms that are executed to produce a result A pipeline is a web server A pipeline is a type of PTransform

Correct Answer: A pipeline is a collection of PTransforms that are executed to produce a result

A pipeline is a collection of PTransforms that are executed to produce a result. A PTransform represents a data processing operation that transforms one or more input PCollections into one or more output PCollections.

Question 2

What is the purpose of the Distinct transform in Apache Beam?

To aggregate elements of a PCollection into a single value To filter elements from a PCollection based on a given condition To retrieve a fixed number of elements from a PCollection To transform each element of a PCollection into a new one with a different type To remove duplicate elements from a PCollection To perform a parallel computation on multiple PCollections

Correct Answer: To remove duplicate elements from a PCollection

The Distinct transform is used to remove duplicate elements from a PCollection. This can be useful when you want to perform aggregation or remove noise from the data.

Question 3

What are the key concepts in Apache Beam?

MapReduce, Spark, Flink Pipelines, PTransforms, PCollections TensorFlow, Keras, PyTorch Hadoop, Hive, Pig Kafka, RabbitMQ, ActiveMQ Pipelines, SQL, DataFrames

Correct Answer: Pipelines, PTransforms, PCollections

Pipelines are the top-level object that encapsulates a data processing workflow. PTransforms represent a data processing operation that transforms one or more PCollections into one or more output PCollections. PCollections represent a distributed data set that can be processed in parallel.

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