Database vs Data Warehouse vs Data Lake | What is the Difference?
Summary
TLDRThis 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
💾 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.
🎥 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.
Mindmap
Keywords
💡Database
💡Data Warehouse
💡Data Lake
💡OLTP (Online Transactional Processing)
💡OLAP (Online Analytical Processing)
💡ETL Process
💡Schema
💡Transactional Data
💡Analytical Data
💡Structured Data
💡Unstructured Data
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]
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