Data Warehousing Vs Data Lakes

Question 1

Start

Answer 1

The first point of contention between data warehouses and data lakes is the structure and organization

Question 2

Data warehouses excel (?)

Answer 2

In this area because:
- data is cleaned, transformed and organized into a predefined schema before storage
- snowflake and star schema

Question 3

This careful organization makes it easy (?)

Answer 3

• To run queries
• Create reports efficiently
• business users easily access data they need without wading through irrelevant information
• enabling quick, and informed decision making

Question 4

The structured nature of data warehouses (?)

Answer 4

makes for data that is
- consistent
- reliable
- ready for analysis at anytime

Question 5

In stark contrast:

Answer 5

Data lakes store data in its raw, unstructured form.
Where users find themselves spending excessive amounts of time cleaning and preparing the data before analysis can begin.

Question 6

This inefficiency

Answer 6

Not only hampers productivity
- can result in costly delays in getting critical insights

Question 7

The chaotic environment of a data lake

Answer 7

• can easily transform it into a DATA SWAMP
• where orgs risk accumulating vast amounts of low quality, irrelevant and poorly secured data.

Question 8

This lack of organization and ill-managed complexity

Answer 8

• confused
• frustrated users
• ultimately undermining the very purpose of data analysis

Question 9

This lack of organization and ill-managed complexity

Answer 9

• confused
• frustrated users
• ultimately undermining the very purpose of data analysis

Question 10

So when it comes to data structure and organization

Answer 10

Data warehouses provide a clear advantage. They offer a
- reliable
- efficient
- user-friendly environment
That empowers businesses to harness their data effectively,
driving better decision-making and fostering a culture of data-driven success.