Back to Big Data Engineer (Big Data Engineer)

Data Pipelines

Methods used to move and process data

Data Pipelines are the methods and technologies used to move and process data from its raw form to its intended destination, while maintaining its quality, structure, and format.
5 minutes 5 Questions

Data pipelines are systematic workflows that orchestrate the movement and transformation of data from various sources to destination systems where it can be analyzed and utilized. For a Big Data Engineer, data pipelines represent the backbone of data processing architecture. A typical data pipelin…

Test mode:
Start practice test
Big Data Engineer - Data Pipelines Example Questions

Test your knowledge of Data Pipelines

Question 1

What is the purpose of a data lake in a data architecture?

Correct Answer: To store raw, unprocessed data

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is used to store raw, unprocessed data for later processing and analysis.

Question 2

What is the purpose of ETL in a data pipeline?

Correct Answer: To extract, transform, and load data from source systems to a target system

ETL stands for Extract, Transform, Load. It is used to extract data from source systems, transform it into a format that can be used for analysis, and then load it into a target system such as a data warehouse.

Question 3

Which Hadoop component provides a distributed file system?

Correct Answer: HDFS

Hadoop Distributed File System (HDFS) is a distributed file system that enables data to be stored across multiple commodity servers, and is a key component of the Hadoop ecosystem.

More Data Pipelines questions
25 questions (total)
Start 25 question test