Amazon Redshift Tutorial | Amazon Redshift Architecture | AWS Tutorial For Beginners | Simplilearn
Summary
TLDRThis video introduces Amazon Redshift, a cloud-based data warehouse service on AWS, emphasizing its high performance and cost-effectiveness. It covers the basics of AWS, the need for Redshift, its architecture, advantages, and use cases. The speaker guides viewers through creating an IAM role for Redshift, launching a cluster, and demonstrating data migration from S3 to Redshift using SQL Workbench/J. The tutorial aims to simplify the understanding of Redshift's setup and operations for data management.
Takeaways
- π Amazon Redshift is a data warehouse service provided by Amazon Web Services (AWS), designed for collecting and storing large amounts of data.
- π AWS is a leading cloud service platform that offers secure cloud services and allows for pay-as-you-go pricing models.
- π Traditional data warehouses were often challenging to maintain due to issues with network connectivity, security, and high maintenance costs.
- π Amazon Redshift addresses these issues by offering a cloud-based solution that is scalable, cost-effective, and simplifies data management.
- π’ Companies like DNA, a telecommunication company, have seen a significant increase in application performance by using Amazon Redshift for data management.
- π° Amazon Redshift is considered cost-effective compared to other cloud data warehouse services and offers high performance.
- π The service provides advantages such as high performance, low cost, scalability, availability, security, flexibility, and ease of database migration.
- π The architecture of Amazon Redshift consists of a leader node that manages client applications and compute nodes that process data.
- π Redshift utilizes column storage and compression techniques to optimize query performance and reduce storage requirements.
- π·οΈ Large enterprises such as Pfizer, McDonald's, and Philips rely on Amazon Redshift for their data warehousing needs.
- π The script includes a demo that guides viewers through creating an IAM role, launching a Redshift cluster, and using the COPY command to move data from S3 to Redshift.
Q & A
What is Amazon Redshift?
-Amazon Redshift is a cloud-based data warehouse service provided by Amazon Web Services (AWS) that is primarily used for collecting, storing, and analyzing large amounts of data using business intelligence tools.
Why was Amazon Redshift introduced?
-Amazon Redshift was introduced to solve the traditional data warehouse problems that developers faced, such as time-consuming data retrieval, high maintenance costs, and potential loss of information during data transfer.
What are some advantages of using Amazon Redshift?
-Some advantages of Amazon Redshift include high performance, low cost, scalability, availability across multiple zones, security features, flexibility in managing clusters, and ease of database migration.
How is Amazon Redshift different from traditional data warehouses?
-Amazon Redshift differs from traditional data warehouses by being a cloud-based service that offers faster performance, lower operational costs, and the ability to scale resources on-demand without the need for hardware procurement.
What is the significance of column storage in Amazon Redshift?
-Column storage in Amazon Redshift is significant because it optimizes query performance by making it easier and quicker to pull out data from specific columns when running queries.
What is compression in the context of Amazon Redshift?
-Compression in Amazon Redshift is a column-level operation that decreases storage requirements and improves query performance by reducing the amount of data that needs to be read from disk.
Can you name some companies that use Amazon Redshift?
-Some companies that use Amazon Redshift include LYA, Equinox, Pfizer, McDonald's, and Philips.
What is the purpose of creating an IAM role for Amazon Redshift?
-Creating an IAM (Identity and Access Management) role for Amazon Redshift allows the service to access other AWS services, such as S3, by granting the necessary permissions in a secure manner.
How can data be transferred from an S3 bucket to an Amazon Redshift cluster?
-Data can be transferred from an S3 bucket to an Amazon Redshift cluster using the COPY command, which allows for direct data loading into Redshift tables from S3.
What is the importance of the leader node in Amazon Redshift architecture?
-The leader node in Amazon Redshift architecture is important as it manages the interaction between client applications and compute nodes, sends out instructions for database operations, and aggregates results before delivering them to the client application.
How can users connect to an Amazon Redshift cluster to run queries?
-Users can connect to an Amazon Redshift cluster to run queries using SQL client applications like SQL Workbench/J, or directly through the AWS Management Console's query editor.
Outlines
π Introduction to Amazon Redshift
The video introduces Amazon Redshift, a data warehouse service on AWS. The speaker, Akil, encourages viewers to subscribe and begins by explaining the basics of AWS as a cloud service platform. He then discusses the traditional challenges of data warehousing, such as geographical barriers, connectivity issues, and high maintenance costs. The introduction of Amazon Redshift is presented as a solution to these problems, offering a cloud-based, scalable, and cost-effective service for managing large datasets. The video promises to cover the advantages of Redshift, its architecture, associated concepts, company use cases, and a practical demo.
π Benefits and Architecture of Amazon Redshift
This paragraph delves into the advantages of Amazon Redshift, highlighting its high performance, low cost, scalability, availability, security, and flexibility. The speaker explains that Redshift's architecture consists of a leader node and compute nodes, which together form a data warehouse cluster. The leader node manages client applications and sends instructions to the compute nodes for data processing. The compute nodes, scalable in number, are responsible for executing these instructions and returning results. The paragraph also touches on additional concepts like column storage and data compression, which contribute to Redshift's efficiency.
π’ Companies Utilizing Amazon Redshift and Upcoming Demo
The speaker listsη₯εδΌδΈ that utilize Amazon Redshift, such as LYA, Equinox, Pfizer, McDonald's, and Philips, emphasizing the service's reliability and widespread adoption. The paragraph concludes with a teaser for an upcoming demo, which will guide viewers through creating an IAM role for Redshift, launching a sample Redshift cluster, assigning security groups, and using the AWS Management Console's query editor to run queries on the cluster.
π οΈ Step-by-Step Redshift Cluster Creation and Data Upload
The paragraph outlines the process of creating an Amazon Redshift cluster, starting with the creation of an IAM role to grant Redshift access to S3 services. It details the steps to launch a cluster, configure VPC security groups, and use the query editor to run SQL commands. The speaker also explains how to copy data from an S3 bucket to a Redshift table using the COPY command, which requires specifying the table name, S3 path, and IAM role ARN. The paragraph provides a practical approach to using Redshift for data storage and querying.
π Data Migration and Query Execution in Redshift
This final paragraph focuses on the migration of data to Redshift and the execution of queries on the uploaded data. The speaker demonstrates the creation of a 'sales' table in Redshift and the use of the COPY command to transfer data from an S3 bucket to this table. After the data migration, the speaker executes a query to retrieve results from the 'sales' table, ensuring the data has been correctly uploaded. The paragraph concludes with a reminder for viewers to subscribe to the channel for more AWS-related content and a call to action for certification through Simply Learn's YouTube channel.
Mindmap
Keywords
π‘Amazon Redshift
π‘AWS
π‘Data Warehouse
π‘Scalability
π‘Column Storage
π‘Compression
π‘Leader Node
π‘Compute Nodes
π‘JDBC
π‘ODBC
π‘Database Migration
Highlights
Introduction to Amazon Redshift as a data warehouse service on AWS.
Request for subscription to the channel for more informative content.
Explanation of AWS as a secure cloud service platform provided by Amazon.
Traditional data warehouse challenges such as geographical limitations and high maintenance costs.
How Amazon Redshift overcomes the issues faced by traditional data warehouses.
Definition of Amazon Redshift as a cloud-based data warehouse service.
Use case of DNA, a telecommunication company, benefiting from Amazon Redshift with a 52% increase in application performance.
Cost-effectiveness and high performance of Amazon Redshift compared to other cloud data warehouse services.
Amazon Redshift's scalability allowing on-demand adjustment of database nodes.
High availability of Amazon Redshift across multiple availability zones.
Security features of Amazon Redshift including virtual private clouds and security groups.
Flexibility in managing Amazon Redshift clusters with snapshot and region transfer capabilities.
Simplicity of database migration to Amazon Redshift from traditional data centers.
Architecture of Amazon Redshift including leader nodes and compute nodes.
Column storage and compression techniques in Amazon Redshift for optimized query performance.
List of notable companies utilizing Amazon Redshift for their data warehousing needs.
Step-by-step demo on creating an Amazon Redshift cluster and setting up necessary permissions.
Demonstration of data migration from Amazon S3 to Redshift using SQL Workbench/J.
Final remarks encouraging subscription and highlighting the value of the presented information.
Transcripts
hi guys uh this is akil and today we are
going to discuss about amazon redshift
which is one of the data warehouse
service on the aws but uh before
starting up with the amazon redshift i
would request you guys to subscribe our
channel you can find the link just below
this video at the right side so let's
begin with amazon redshift and let's see
what we have for today's session so
what's in it for you today we'll see
what is aws
why we require amazon redshift what do
we mean by amazon redshift the
advantages of amazon redshift the
architecture of amazon redshift some of
the additional concepts associated with
the redshift and the companies that are
using the amazon redshift and finally
we'll cover up one demo which will show
you the practical example that how you
can actually use the redshift service
now what is aws as we know that aws
stands for amazon web service it's one
of the largest cloud providers in the
market and it's basically a secure cloud
service platform provided from the
amazon also on the aws you can create
and deploy the applications
using the aws service along with that
you can access the services provided by
the aws over the public network that is
over the internet they are accessible
plus you pay only whatever the service
you use for now let's understand why we
require amazon redshift so earlier
before amazon redchef what used to
happen that the people used to or the
developers used to fetch the data from
the data warehouse so data warehouse is
basically a terminology which is
basically represents the collection of
the data so a repository where the data
is stored is generally called as a data
warehouse now fetching data from the
data warehouse was a complicated task
because might be a possibility that the
developer is located at a different
geography and the data data warehouse is
at a different location and probably
there is not that much network
connectivity or
some networking challenges internet
connectivity challenges security
challenges might be and a lot of
maintenance was required to manage the
data warehouses so what were the cons of
the traditional data warehouse services
it was time consuming to download or get
the data from the data warehouse
maintenance cost was high and there was
the possibility of loss of information
in between the downloading of the data
and the data rigidity was an issue now
how these problems could overcome and
this was uh basically solved with the
introduction of amazon redshift over the
cloud platform now we say that amazon
redshift has solved traditional data
warehouse problems that the developers
were facing but how what is amazon
redshift actually is so what is amazon
redshift it is one of the services over
the aws amazon web services which is
called as a data warehouse service so
amazon redshift is a cloud-based service
or a data warehouse service that is
primarily used for collecting and
storing the large chunk of data so it
also helps you to get or extract the
data analyze the data using some of the
bi tools so business intelligence tools
you can use and get the data from the
redshift and process that and hence it
simplifies the process of handling the
large scale data sets so this is the
symbol for the amazon redshift over the
aws now let's discuss about one of the
use case so dna is basically a
telecommunication company and they were
facing an issue with managing their
website and also the amazon s3 data
which led down to slow process of their
applications now how could they overcome
this problem let's say that so they
overcome this issue by using the amazon
redshift and all the company noticed
that there was a 52 increase in the
application performance now did you know
that amazon redshift is
basically cost less to operate than any
other cloud data warehouse service
available on the cloud computing
platforms and also the performance of an
amazon redshift is the fastest data
warehouse we can say that that is
available as of now so in both cases one
is that it saves the cost as compared to
the traditional data warehouses and also
the performance of this red shift
service or a data warehouse service the
fastest available on the cloud platforms
and more than 15 000 customers primarily
presently they are using the amazon
redshift service now let's understand
some of the advantages of amazon
redshift first of all as we saw that it
is one of the fastest available data
warehouse service so it has the high
performance second is it is a low cost
service so you can have a large scale of
data warehouse or a databases combined
in a data warehouse at a very low cost
so whatever you use you pay for that
only scalability now in case if you
wanted to increase the nodes of the
databases in your redshift you can
actually increase that based on your
requirement and that is on the fly so
you don't have to wait for the
procurement of any kind of hardware or
the infrastructure it is whenever you
require you can scale up or scale down
the resources so this scalability is
again one of the advantage of the amazon
redshift availability since it's
available across multiple availability
zones so it makes this service as a
highly available service security so
whenever you create whenever you access
redshift you create a clusters in the
redshift and the clusters are created in
the you can define a specific virtual
private cloud for your cluster and you
can create your own security groups and
attach it to your cluster so you can
design the security parameters based on
your requirement and you can get your
data warehouse or the data items in a
secure place flexibility and you can
remove the clusters you can create under
clusters if you are deleting a cluster
you can take a snapshot of it and you
can move those snapshots to different
regions so that much flexibility is
available on the aws for the service and
the other advantage is that it is
basically a very simple way to do a
database migration so if you're planning
that you wanted to migrate your
databases from the traditional data
center over the cloud on the redshift it
is basically a very simple to do a
database migration you can have some of
the inbuilt tools available on the aws
access you can connect them with your
traditional data center and get that
data migrated directly to the redshift
now let's understand the architecture of
the amazon redshift so architecture of
an amazon redshift is basically it
combines of a cluster and that we call
it as a data warehouse cluster in this
picture you can see that this is a data
warehouse cluster and this is a
representation of a amazon redshift so
it has some of the compute nodes which
does the data processing and a leader
node which gives the instructions to
these compute nodes and also the leader
node basically manages the client
applications that require the data from
the redshift so let's understand about
the components of the redshift the
client application of amazon redshift
basically interact with the leader node
using jdbc or the odbc now what is jdbc
it's a java database connectivity and
the odbc stands for open database
connectivity the amazon redshift service
using a jdbc connector can monitor the
connections from the other client
applications so the leader node can
actually have a check on the client
applications using the jdbc connections
whereas the odbc allows a leader node to
have a direct interaction or to have a
live interaction with the amazon
redshift so odbc allows a user to
interact with live data of amazon
redshift so it has a direct connectivity
direct access of the applications as
well as the leader node can get the
information from the compute nodes now
what are these compute nodes these are
basically kind of our databases which
does the processing so amazon redshift
has a set of computing resources which
we call it as a nodes and the nodes when
they are combined together they are
called it as a clusters now a cluster a
set of computing resources which are
called as nodes and this gathers into a
group which we call it as a data
warehouse cluster so you can have a
compute node starting from 1 to n number
of nodes and that's why we call that the
redshift is a scalable service because
we can scale up the compute nodes
whenever we require now the data
warehouse cluster or the each cluster
has one or more databases in the form of
a nodes now what is a leader node this
node basically manages the interaction
between the client application and the
compute node so it acts as a bridge
between the client application and the
compute nodes also
it analyzes and develop designs in order
to carry out any kind of a database
operations so leader node basically
sends out the instructions to the
compute nodes basically perform or
execute that instructions and give that
output to the leader node so that is
what we are going to see in the next
slide that the leader node runs the
program and assign the code to
individual compute nodes and the compute
nodes execute the program and share the
result back to the leader node for the
final aggregation and then it is
delivered to the client application for
analytics or whatever the client
application is created for so compute
nodes are basically categorized into
slices and each node slice is alerted
with specific memory space or you can
say a storage space where the data is
processed these node slices works in
parallel in order to finish their work
and hence when we talk about a redshift
as a fast faster processing capability
as compared to other data warehouses or
traditional data warehouses this is
because that these node slices work in a
parallel operation that makes it more
faster now the additional concept
associated with amazon redshift is there
are two additional concepts associated
with the redshift one is called as the
column storage and the other one is
called as the compression let's see what
is the column storage as the name
suggests column storage is basically
kind of a data storage in the form of a
column so that whenever we run a query
it becomes easier to pull out the data
from the columns so column storage is an
essential factor in optimizing query
performance and resulting in quicker
output so one of the examples are
mentioned here so below example show how
database tables store record into disk
block by row so here you can see that if
we wanted to pull out some kind of an
information based on the city address
age we can basically create a filter and
from there we can put out the details
that we require and that is going to
fetch out the details based on the
column storage so that makes data more
structured more streamlined and it
becomes very easier to run a query and
get that output now the compression is
basically to save the column storage we
can use a compression as an attribute so
compression is a column level operation
which decreases the storage requirement
and hence it improves the query
performance and this is one of the
syntax for the column compression now
the companies that are using amazon
redshift one is lya the other one is
equinox the third one is the pfizer
which is one of the famous
pharmaceuticals company mcdonald's one
of the burger chains across the globe
and philips it's an electronic company
so these are one of the biggest
companies that are basically relying and
they are putting their data on the
redshift data warehouse service now in
another video we'll see the demo for
using the amazon redshift let's look
into the amazon redshift demo so these
are the steps that we need to follow for
creating the amazon redshift cluster and
in this demo what we will be doing is
that we will be creating an im role for
the redshift so that the redshift can
call the services and specifically we
will be using the s3 service so the role
that we will be creating will be giving
the permission to redshift to have an
access of an s3 in the read-only format
so in the step one what we require we'll
check the prerequisites and what you
need to have is the aws credentials uh
if you don't have that you need to
create your own credentials and you can
use your credit and the debit card and
then in the step two we'll proceed with
the im roll for the amazon redshift once
the role is created we'll launch a
sample amazon redshift cluster mentioned
in the step 3 and then we'll assign a
vpc security groups to our cluster now
you can create it in the default vpc
also you can create a default security
groups also otherwise you can customize
the security groups based on your
requirement now to connect to the sample
cluster you need to run the queries and
you can connect to your cluster and run
queries on the aws management console
query editor which you will find it in
the redshift only or if you use the
query editor you don't have to download
and set up a sequel client application
separately and in the step 6 what you
can do is you can copy the data from the
s3 and upload that in the redshift
because the redshift would have an
access
read-only access
for the s3 as that will be created in
the im role so let's see how we can
actually use the redshift uh on the aws
so i am already logged in into my
account i am in north virginia region
i'll search for redshift service and
here i find amazon redshift so just
click on it
let's wait for the redshift to come now
this is a redshift dashboard and from
here itself you have to run the cluster
so to launch a cluster you just have to
click on this launch cluster and once
the cluster is created and if you wanted
to run queries you can open query editor
or you can basically create queries and
access the data from the red shift so
that's what it was mentioned in the
steps also that you don't require a
separate sql client application to get
the queries run on the data warehouse
now before creating a cluster we need to
create the role so what we'll do is
we'll click on the services and we'll
move to im role section so im rule i can
find here under the security identity
and compliance so just click on the
identity access management and then
click on create roles so let's wait for
i am page to open so here in the im
dashboard you just have to click on the
rules i already have the role created so
what i'll do is i'll delete this role
and i'll create it separately so just
click on create role and under the aws
services you have to select for the
redshift because now the redshift will
be calling the other services and that's
why we are creating the role now which
other services that the redshift will be
having an access of s3 why because we'll
be putting up the data on the s3 and
that is something which needs to be
uploaded on the redshift so we'll just
search for the redshift service and
we can find it here so just click on it
and then click on red shift customizable
in the use case now click on next
permissions and here in the permissions
give the access to this role assign the
permissions to this role in the form of
an s3 read-only access so you can search
here for the s3 also let's wait for the
policies to come in here it is let's
type s3 and here we can find amazon s3
read-only access so just click on it and
assign the permissions to this role tags
you can leave them blank click on next
review put a name to your role let's put
my redshift role and click on a create
role now you can see that your role has
been created now the next step is that
we'll move to redshift service and we'll
create one cluster so click on the
services click on amazon redshift you
can find that in the history section
since we browsed it just now and from
here we are going to create a sample
cluster now to launch a cluster you just
have to click on launch this cluster
whatever the uncompressed data size you
want in the form of a gigabyte terabyte
or petabyte you can select that and
let's say if you select in the form of
gb how much db memory you want you can
define it here itself this also gives
you the information about the costing on
demand is basically pay as you use so
they are going to charge you 0.5 dollars
per r for using the two node slices so
let's click on launch this cluster and
this will be a dc2 dot large kind of an
instance that will be given to you it
would be in the form of a solid state
drive ssds which is one of the fastest
way of storing the information and
the nodes two are mentioned by default
that means there will be two node slices
and that will be created in a cluster
you can increase them also let's say if
i put three node slices so it is going
to give us 3 into 0.16 db per node
storage now here you have to define the
master username password for your
redshift cluster and you have to follow
the password instructions so i would put
a password to this cluster and if it
accepts that means it does not give you
any kind of a warning otherwise it is
going to tell you about you have to use
the ascii characters and all and here
you have to assign this cluster the role
that we created recently so in the
available im rules you just have to
click on my red shift roll and then you
have to launch the cluster if you wanted
to change something in with respect to
the default settings let's say if you
wanted to change the vpc from default
vpc to your custom vpc and you wanted to
change the default security groups to
your own security group so you can
switch to advanced settings and do that
modification now let's launch the
cluster and here you can see the
redshift cluster is being created now if
you wanted to run queries on this
redshift cluster so you don't require a
separate sql client you just have to
follow
the simple steps to run a query editor
and the query editor you will find it on
the dashboard so let's click on the
cluster and here you would see that the
redshift cluster would be created with
the three nodes in the us east1b
availability zone so we have created the
redshift cluster
in the ohio region and now what we'll do
is we'll
see how we can create the tables inside
the redshift and we'll see how we can
use the copy command so that we can
directly move the data uploaded on the
s3 bucket to the redshift database
tables and then we'll query the results
of a table as well so how we can do that
first of all after creating the redshift
cluster we have to install sql workbench
j
this is not a mysql which is managed by
oracle and you can find this
on the google you can download it from
there and
then you have to connect this client
with the redshift database how you can
do click on file click on connect window
and after connecting a window uh you
have to paste the url which is a jdbc
driver this driver link you can find it
onto the aws console so if you open up a
redshift cluster there you would find
the jdbc driver link let's wait for it
so this is our cluster created let's
open it and here you can find this jdbc
url and also make sure that in the
security groups of a redshift you have
the port 5439 open for the traffic
incoming traffic you also need to have
the amazon redshift driver and this is
the link where you can download the
driver and specify the path once you are
done with that you provide the username
and the password that you created while
creating the redshift cluster click on
ok so this connects with the database
and now the database connection is
almost completed now what we will be
doing in the sql workbench we'll be
first creating the sales table and then
in the sales table we'll be adding up
the entries copied from the s3 bucket
and then move it to the redshift
database and after that we'll query the
results in the sales table now whatever
the values you are creating in the table
the same values needs to be in the data
file and
i have taken up this sample data file
from this link which is
docs.aws.amazon.com redshift sample
database creation and here you can find
a download file ticketdb.zip file this
folder has basically multiple
data files sample data files which you
can actually use it
to practice uploading the data on the
redshift cluster so i have extracted one
of the files
from this folder and then i have
uploaded that file in the s3 bucket now
we'll move into the s3 bucket let's look
for the file that has been uploaded on
the s3 bucket so this is the bucket
sample and sales underscore tab dot text
is the file that i have uploaded this
has the entries data entries that will
be uploaded using a copy command onto
the redshift cluster now after executing
after putting up the command for
creating up the table then
we'll use a copy command and
copy command we have to define the table
name the table name is sales and we have
to define the path from where the data
would be copied over to the sales table
in the red shift now path is the s3
bucket and this is the redshift bucket
sample and
it has to look for the data inside the
sales underscore tab.txt file also we
have to define the
role arn that was created previously and
once it is done then the third step is
to query the results inside the sales
table to check whether our data has been
uploaded correctly on the table or not
now what we'll do is we'll execute all
these three syntax
it gives us the error because we have to
connect it again to the database let's
wait for it
execute it
it's again gives us the error let's look
into the name of the bucket it's
redshiftbucketsample so we have two t's
mentioned here right
let's connect with the database again
and now execute it so table sales
created and we got the error the
specified bucket does not exist uh
redshift bucket sample let's view the
bucket name redshift bucket sample let's
copy that put it here connect to the
window connect back to the database
right and now execute it so table sales
created
the data in the table has been copied
from the s3 bucket
to sales underscore tab.text to the
redshift and then the query of the
results now the results
from the table has been queried so
that's it with respect to the redshift
perspective and i hope you liked our
video just don't forget to subscribe and
like our channel watch out for our
channel for the upcoming videos on the
aws itself bye for now thank
you hi there if you like this video
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