Azure Data Factory for Beginners - Build Data Ingestion - Introduction

The video tutorial explains event driven ingestion, focusing on a hypothetical company, Bylot, which uses a finance system to deliver files to Azure BLOB storage. The system produces three files: two CSVs and one JSON. The tutorial outlines the requirements for a data factory pipeline to ingest these files, emphasizing the need to filter and store CSV files in separate directories within a data engineering data lake.

What is the primary purpose of the new finance system in Bylot?

• To manage employee records

• To deliver files to Azure BLOB storage

• To enhance customer service

• To improve marketing strategies

Which file types are produced by the finance system?

• One CSV file and two JSON files

• Two JSON files and one CSV file

• Three XML files

• Two CSV files and one JSON file

What triggers the data factory pipeline in the event-driven ingestion process?

• System reboot

• Scheduled time intervals

• Manual user input

• Uploading of files

What is the main requirement for the data factory pipeline regarding file types?

• Ingest all file types

• Ingest only JSON files

• Ingest only CSV files

• Ingest only XML files

How should the data be stored in the data engineering data lake?

• In a shared directory with other projects

• In separate directories for each file type

• In a single directory

• In a cloud-based database