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DP-700 · Question #29

You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the…

The correct answer is D. Data pipeline. To ingest large daily files into a Fabric lakehouse with no transformations, an event-driven trigger, and highest throughput, a Data pipeline with a Copy data activity is the most suitable item type.

Design and implement data ingestion and transformation

Question

You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements: - Trigger the process when a new file is added. - Provide the highest throughput. Which type of item should you use to ingest the data?

Options

  • AEvent stream
  • BDataflow Gen2
  • CStreaming dataset
  • DData pipeline

How the community answered

(22 responses)
  • A
    9% (2)
  • B
    5% (1)
  • C
    14% (3)
  • D
    73% (16)

Why each option

To ingest large daily files into a Fabric lakehouse with no transformations, an event-driven trigger, and highest throughput, a Data pipeline with a Copy data activity is the most suitable item type.

AEvent stream

Event stream is primarily for real-time processing of smaller, high-velocity data, not for high-throughput ingestion of 500GB daily files without transformation.

BDataflow Gen2

Dataflow Gen2 is designed for data preparation and transformation, adding unnecessary overhead for a no-transformation ingestion scenario compared to a data pipeline.

CStreaming dataset

Streaming datasets are used for real-time visualization in Power BI, not for high-volume, batch-like data ingestion into a lakehouse for storage and broader analysis.

DData pipelineCorrect

Data pipelines in Fabric (based on Azure Data Factory) are purpose-built for high-throughput, large-scale data ingestion and orchestration tasks. They support event-driven triggers, such as when a new file is added, and their Copy data activity is optimized for efficient, untransformed data movement directly into a lakehouse, fulfilling all specified requirements for performance and automation.

Concept tested: Fabric data ingestion strategies and tools

Source: https://learn.microsoft.com/en-us/fabric/data-factory/copy-data-activity

Topics

#Data Ingestion#Microsoft Fabric#Data Pipelines#Lakehouse

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