Flashcards

Data Pipelines

Methods used to move and process data

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…

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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?

To serve as a database management system To store only processed and analyzed data To perform data integration and cleansing To store raw, unprocessed data To support only batch processing of data To provide real-time data visualization

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?

To perform exploratory data analysis on raw data To monitor and report on the health of the data pipeline To manage database schemas and indexes To serve real-time data to end users To store data in a distributed file system To extract, transform, and load data from source systems to a target system

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?

Hive Pig YARN Spark HDFS MapReduce

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.

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