Database vs Data Warehouse vs Data Lake | What is the Difference?

Summary

This video explores the distinctions between databases, data warehouses, and data lakes. Databases are ideal for transactional data storage, offering real-time access and a flexible schema. Data warehouses, on the other hand, are designed for analytical processing, housing summarized historical data via ETL processes. Data lakes serve as repositories for all types of data, both structured and unstructured, offering flexibility for future analytics but requiring additional processing for use. The video highlights that each serves unique purposes and can coexist within an organization.

Takeaways

• 🗄️ A database is typically a relational database used for capturing and storing data via an OLTP (Online Transactional Process).
• 📊 A data warehouse is a type of database designed for analytical processing or OLAP (Online Analytical Processing) to analyze large amounts of data.
• 🔄 Data warehouses receive data from operational databases through an ETL (Extract, Transform, Load) process, which extracts, transforms, and loads the data for analysis.
• 📈 Data in a data warehouse is usually summarized and historical, not necessarily current, and is optimized for fast querying and reporting.
• 📑 Databases store highly detailed data in table format with columns and rows, allowing for flexible schema changes.
• 🚫 Data warehouses have a more rigid schema and require careful planning for data structure, unlike databases.
• 📉 Databases are slower for querying large amounts of data and can slow down transaction processing, whereas data warehouses are designed to be fast for querying without affecting transactions.
• 💧 A data lake is designed to store any type of data, structured or unstructured, in its raw form.
• 🤖 Data lakes are particularly useful for machine learning and AI applications where raw data is used to create models.
• 🛠️ While data in a data lake is not immediately usable for analytics, it can be cleaned and structured for use in databases or data warehouses if needed.
• 🏢 Companies may use all three - databases, data warehouses, and data lakes - to serve different data storage and processing needs.

Q & A

What is the primary function of a database?

A database is primarily used for recording transactions or capturing and storing data via an OLTP (Online Transactional Process), which is ideal for real-time data management.

How is data stored in a database?

Data in a database is stored in tables with columns and rows, and it is highly detailed, allowing users to see every single aspect of the data.

What is the difference between a database and a data warehouse?

A database is used for transactional processing and stores detailed, real-time data, while a data warehouse is used for analytical processing (OLAP) and typically contains summarized historical data.

How does data get into a data warehouse?

Data is transferred into a data warehouse from databases through an ETL (Extract, Transform, Load) process, which extracts the data, transforms it, and loads it into the data warehouse.

What is the purpose of the ETL process in a data warehouse?

The ETL process is used to prepare data for analysis by extracting it from the source, transforming it into a summarized form, and loading it into the data warehouse.

Why is a data warehouse's schema more rigid than a database's?

A data warehouse's schema is more rigid because it requires careful planning ahead for how data will be structured and analyzed, unlike a database which allows for more flexibility and schema changes on the fly.

What is the main difference between the data in a database and a data warehouse?

Data in a database is detailed and current, while data in a data warehouse is summarized and may not always be current, depending on the frequency of the ETL process.

What is a data lake and what types of data can it store?

A data lake is a system designed to capture any type of data, including structured, semi-structured, and unstructured data such as videos, images, documents, and graphs.

Who benefits most from using a data lake?

People working with machine learning and AI benefit the most from using a data lake, as they can utilize the raw, unstructured data for creating models.

Why might a company use all three - databases, data warehouses, and data lakes?

A company might use all three systems to serve different needs: databases for transactional data, data warehouses for analytical reporting, and data lakes for storing large volumes of diverse data types.

How does the performance differ between querying a database and querying a data warehouse?

Databases can be slower for querying large amounts of data and may slow down transaction processing, whereas data warehouses are designed to query large amounts of data quickly without affecting other processes.

Outlines

00:00

💾 Databases vs. Data Warehouses vs. Data Lakes

This paragraph introduces the topic of the video, which is to explain the differences between a database, a data warehouse, and a data lake. The speaker begins by sharing their personal experience of only knowing about databases and not being familiar with data warehouses or data lakes. The video aims to clarify these concepts and how they interrelate. The speaker starts by defining a database as a relational database that captures and stores data through an OLTP (Online Transactional Process). This means that every time a company completes a transaction, it's recorded in the database. The data in a database is live and real-time, and it's highly detailed, stored in tables with columns and rows. The database schema is flexible, allowing for changes as needed. The paragraph then transitions into explaining what a data warehouse is, which is also a type of database but is used for analytical processing or OLAP (Online Analytical Processing). Data warehouses are designed to analyze large amounts of data. The data in a data warehouse comes from multiple databases and is processed through an ETL (Extract, Transform, Load) process. Unlike databases, data warehouses do not always have the most current data and typically store summarized data. The schema in a data warehouse is rigid and requires careful planning. The paragraph concludes by highlighting the key differences between databases and data warehouses, such as the purpose (transaction recording vs. analytics), the freshness and detail of the data, and the speed of querying large amounts of data.

05:01

🎥 Wrapping Up the Explanation

The second paragraph serves as a conclusion to the video. The speaker thanks the viewers for watching and encourages them to like and subscribe if they found the video helpful. A brief musical outro is also mentioned, signaling the end of the video. This paragraph is less about content and more about viewer engagement and the closure of the video's topic.

Keywords

💡Database

A database is a structured collection of data, typically stored and accessed electronically. In the context of the video, databases are relational and used for capturing and storing data via an OLTP (Online Transactional Processing) process, such as recording a sale transaction. The script mentions that data in a database is highly detailed and stored in tables with columns and rows, allowing for the viewing of every aspect of the data.

💡Data Warehouse

A data warehouse is a large, centralized repository of data designed for reporting and analysis. Unlike a database, it is used for analytical processing or OLAP (Online Analytical Processing). The video explains that data warehouses aggregate data from various databases via an ETL (Extract, Transform, Load) process, summarizing the data for faster analytical processing. They are designed to handle large volumes of historical data and are optimized for querying large amounts of data quickly.

💡Data Lake

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. The video describes a data lake as a place to store any type of data, structured or unstructured, such as videos, images, documents, and graphs. Data lakes are particularly beneficial for machine learning and AI, where raw data can be used to create models. However, for analytical purposes, the data often needs to be cleaned and structured before use.

💡OLTP (Online Transactional Processing)

OLTP stands for Online Transactional Processing, which is a class of information systems that facilitates and manages transaction-oriented applications. In the video, OLTP is mentioned as the process through which databases capture and store data in real-time, such as recording a transaction when a company sells an item.

💡OLAP (Online Analytical Processing)

OLAP stands for Online Analytical Processing, which is a category of computer software that enables organizations to access and analyze large volumes of data with the speed necessary to make informed business decisions. The video script explains that data warehouses are used for OLAP, which involves analyzing huge amounts of data to support decision-making.

💡ETL Process

ETL stands for Extract, Transform, Load, which is a process in database usage that involves extracting data from one or more sources, transforming and cleansing it, and then loading it into a target database or data warehouse. The script describes how databases send their data to a data warehouse through an ETL process, where the data is extracted, transformed into the needed format, and loaded into the data warehouse.

💡Schema

In the context of databases and data warehouses, a schema is a blueprint of how the data is structured and how the database tables relate to each other through links of their data columns. The video script mentions that databases have a flexible schema, allowing changes as needed, while data warehouses have a more rigid schema that requires planning ahead for how data will be structured.

💡Transactional Data

Transactional data refers to the information generated by business processes that involve transactions, such as sales, purchases, or financial transactions. The video script uses the example of a company selling an item to illustrate how transactional data is recorded in real-time within a database.

💡Analytical Data

Analytical data is information that has been processed and summarized for the purpose of analysis. It is used to gain insights and make decisions. The video explains that data warehouses store summarized data, which is used for analytical processing and reporting, unlike transactional data in databases which is detailed and up-to-date.

💡Structured Data

Structured data is information that is organized into a specific format, making it easily readable by databases and other software systems. The video script mentions structured data in the context of data lakes, where it can be stored alongside unstructured data, and needs to be cleaned and structured before it can be used for analysis.

💡Unstructured Data

Unstructured data is information that does not follow a specific format and is not easily classified or organized. Examples include text documents, emails, and multimedia files. The video script discusses how data lakes can store unstructured data in its raw form, which can be beneficial for machine learning and AI but may require additional processing for other analytical uses.

Highlights

Exploring the differences between a database, a data warehouse, and a data lake.

A database typically refers to a relational database used for capturing and storing data via OLTP (Online Transactional Process).

Data in a database is live, real-time, and highly detailed.

Databases have a flexible schema allowing for modifications as needed.

A data warehouse is also a database but used for analytical processing or OLAP (Online Analytical Processing).

Data warehouses are designed to analyze large amounts of data.

Data is sent to a data warehouse from multiple databases via an ETL process.

Data warehouses do not always contain current data, depending on the frequency of the ETL process.

Data in a data warehouse is summarized for faster analytical processing.

Data warehouses have a rigid schema requiring careful planning for data storage.

Key differences: Databases record transactions, data warehouses are for analytics and reporting.

Databases have fresh and detailed data, while data warehouses have summarized data.

Databases can be slower for querying large amounts of data, potentially slowing transaction processing.

Data warehouses are designed to query large amounts of data quickly without slowing down processes.

A data lake is designed to capture any type of data, including unstructured and semi-structured data.

Data lakes are particularly useful for machine learning and AI applications.

Data in a data lake is in its raw form and may require cleaning for analytical purposes.

Data lakes, databases, and data warehouses serve different purposes and can coexist within a company.

There is no one-size-fits-all; all three options can be used for different data needs.

The presenter's hands-on experience with databases, data warehouses, and data lakes highlights their versatility.

Transcripts

what's going on everybody welcome back

to another video today we're gonna be

taking a look at the differences between

a database a data warehouse and a data

lake

[Music]

now when i was first starting out i'd

only ever heard of a database and i

think that's what most people are

familiar with but i had never heard of a

data warehouse or a data lake and so in

this video we're gonna be walking

through the differences between each one

of them as well as how they kind of

connect with one another so let's jump

onto my screen and get started all right

so we're gonna be taking a look at a

database a data warehouse and a data

lake but let's start with a database now

when someone says a database typically

they're referring to a relational

database now a relational database can

capture and store data via an oltp

process which stands for online

transactional process so when company

completes a transaction and sells an

item it'll record that within a database

and that data has the ability to be live

real-time data data in a database is

going to be stored in tables which has

columns and rows and this will be highly

detailed which means you're going to be

able to go in and see every single

aspect of the data and databases also

have a really flexible schema which

means you can go in there and kind of

change things as you go to make it work

for what you need now a data warehouse

is also a database just like we were

looking at before but it's going to be

used for analytical processing or olap

olap stands for online analytical

processing and it's created to basically

analyze huge amounts of data now if you

notice on the last slide there were

these three databases they were just

kind of sitting there and they were

storing the data in this visualization

that we have on the right these three

databases on the bottom are all

aggregating and sending their data to

this data warehouse via an etl process

which is where it extracts the data it

transforms it and loads it exactly how

they need it in this data warehouse and

that's how data is put into the data

warehouse it isn't getting it directly

from the source but it's being put into

a database and via the etl process is

being updated as it goes or whenever the

etl process runs a data warehouse will

always have the historical data but it

won't always have the current data

unless the etl process is running every

single day or very frequently the data

in the data warehouse is also a little

bit different because we're doing this

etl process to get the data in there

we're not actually putting every single

piece of data or every column and row in

there we're typically summarizing it and

then putting it in there which will

allow us to process that data for our

analytical purposes much faster now a

data warehouse is going to have a much

more rigid schema so you really need to

plan ahead with how you're going to put

your data into a data warehouse it's not

as flexible as just a database so now

let's look at some of the key

differences between a database and a

data warehouse a database is going to be

used for recording transactions or a

data warehouse is going to be used for

analytics and reporting a database is

going to have fresh and detailed data

where a data warehouse is going to have

summarized data it's only going to be as

fresh as the etl process is created a

database is going to be a little bit

slower for querying large amounts of

data and when you do query large amounts

of data it can actually slow down the

processing of all those transactions a

data warehouse was designed for the

exact opposite it was designed to be

very fast at querying and not slow down

any processes because it isn't part of

that transaction processing at all so

now that we've looked at a database and

a data warehouse let's take a look at a

data lake a data lake was basically

designed to capture any type of data

that you could possibly want it could be

a video a picture an image a document a

graph anything you could imagine that

you'd want to put in a database or store

in some way you can store it in a data

lake now there are a ton of use cases

for a data lake but i think people who

work with machine learning and ai get to

use it or benefit from it the most they

can use all that structured and

unstructured data and create models to

really use it in its raw form where if

you want to use it for analytical

purposes typically you're gonna have to

clean it up a little bit and do a little

bit more work to actually make it usable

and so a dale lake is just that it's

this lake where you can basically throw

any type of data in there but it's not

always super usable because you're just

putting it in there in its raw form if

you want to use it for analytical

purposes and reporting most of the time

you're going to want to clean that up

and put it into a database or a data

warehouse so now when we're looking at

all three they are all different and

they're all used for different purposes

so no one option is better than another

for your data if you're using it just to

record transactions a database is what

you should do and if you have a large

amount of data that's just too much for

your database to handle it sounds like

you might need a data warehouse and if

you have all this data they have no idea

what to do with or it's unstructured

semi-structured data that you can't fit

into a database well then i highly

recommend using a data lake there really

is no one size fits all all three of

these can be options for different uses

and in fact you can use all three within

one company for just different things

that your company needs so i hope that

that was helpful learning the

differences between a database a day

warehouse and a data lake again i had

really never used a data warehouse or a

data lake when i first got into

analytics but now that i've gotten

hands-on experience with all of them

they're all really interesting can be

used for so many different things so

thank you so much for watching this

video i really appreciate it if you like

this video be sure to like and subscribe

below and i'll see you next video

[Music]