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.

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Big Data Engineer - Data Pipelines Example Questions

Test your knowledge of Amazon Simple Storage Service (S3)

Question 1

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

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

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

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?

YARN Pig Hive HDFS MapReduce Spark

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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