Data Warehouse (IS 422) Lecture 1 Introduction Quiz

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Questions and Answers

What is the main difference between a data lake and a data warehouse in terms of data storage?

Data lakes store only structured data, while data warehouses store semi-structured and unstructured data.
Data lakes store only unstructured data, while data warehouses store structured and semi-structured data.
Data lakes store all kinds of data in its raw format, while data warehouses store only modeled/aggregated/structured data. (correct)
Data lakes store data in a star or snowflake schema, while data warehouses store data as-is.

What is the processing approach for loading data into a data warehouse?

Schema-on-read
Loading raw data as-is
Giving a shape or structure when ready to use
Modeling into a star or snowflake schema on write (correct)

How does the retrieval speed from data warehouses differ from that of data lakes?

Data warehouses take less time due to schema-on-read processing.
Data lakes retrieve unstructured text faster than structured data.
Data warehouses have faster retrieval speed due to in-database processing. (correct)
Data lakes are faster because of triggers and columnar data representation.

Which term describes the process of giving shape or structure to raw data when ready to use it in a data lake?

Schema-on-read (D) Signup and view all the answers

What role do algorithms play in the retrieval speed from data warehouses?

Algorithms are developed to enhance the speed of retrieving large and feature-rich data. (C) Signup and view all the answers

Why are data lakes not considered a replacement for data warehouses?

Data lakes and data warehouses serve different purposes and are complementary. (B) Signup and view all the answers

What is the main purpose of a data warehouse?

To process and analyze structured data for business intelligence (B) Signup and view all the answers

Which one of the following is NOT a component of the data warehouse framework?

Real-time data streaming (C) Signup and view all the answers

What is the primary difference between a data warehouse and a data lake?

A data warehouse stores structured data, while a data lake stores unstructured data (B) Signup and view all the answers

Which process is responsible for extracting data from various sources, transforming it, and loading it into the data warehouse?

Extract, Transform, Load (ETL) (B) Signup and view all the answers

What is the purpose of dimensional modeling in the context of a data warehouse?

To create a logical model for organizing and presenting data in a multidimensional way (C) Signup and view all the answers

Which of the following statements about big data and data warehouses is correct?

Data warehouses and big data technologies can coexist and complement each other (A) Signup and view all the answers

What is one of the primary features of Big Data technologies like Hadoop in terms of data storage costs?

They are open-source, reducing the cost of storing data. (D) Signup and view all the answers

What differentiates the structure of a data lake from a data warehouse?

A data lake allows easy configuration and reconfiguration, unlike a data warehouse. (A) Signup and view all the answers

Why are data lakes considered to have more novelty and innovation compared to data warehouses?

Data warehousing technologies have been around for a long time with few recent innovations. (C) Signup and view all the answers

What advantage do data warehouses have over data lakes in terms of security?

Data warehouses have more mature security capabilities. (D) Signup and view all the answers

Which key reason contributes to the low cost of storing data in Hadoop compared to traditional data warehousing?

Hadoop leverages low-cost commodity hardware and is open-source. (C) Signup and view all the answers

What is a distinguishing characteristic of the underlying technologies of data warehousing compared to those of data lakes?

The technologies underlying data warehousing have been around for a much longer period. (B) Signup and view all the answers

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Study Notes

Data Lakes vs Data Warehouses

A data lake is not a replacement for a data warehouse; they are complementary to one another.

Data Storage

A data warehouse stores structured data that has been modeled/aggregated, whereas a data lake stores all kinds of data (structured, semi-structured, and unstructured) in its native/raw format.

Processing

Data warehousing requires data to be modeled into a star or snowflake schema before loading, known as schema-on-write.
Data lakes load raw data and give it a shape or structure when ready to use, known as schema-on-read.

Retrieval Speed

Data warehouses have developed algorithms to improve retrieval speed, including triggers and columnar data representation.
Retrieving data from a data lake can be time-demanding due to the variety of data formats.

Storage

Data warehouses store structured data, whereas data lakes store vast quantities of data in its native/raw format for future analytics consumption.

Agility

Data warehouses are highly structured repositories, making changes time-consuming due to tied business processes.
Data lakes lack structure, allowing for easy configuration and reconfiguration of models, queries, and apps.

Novelty

Data warehousing technologies have been around for a long time, with little innovation in recent years.
Data lakes are new and undergoing innovation to become a mainstream data storage technology.

Security

Securing data in a data warehouse is more mature than securing data in a data lake due to decades of development.

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