Amazon Redshift Tutorial | Amazon Redshift Architecture | AWS Tutorial For Beginners | Simplilearn

Summary

This 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

• 🌐 Amazon Redshift is a data warehouse service provided by Amazon Web Services (AWS), designed for collecting and storing large amounts of data.
• 📈 AWS is a leading cloud service platform that offers secure cloud services and allows for pay-as-you-go pricing models.
• 🛠 Traditional data warehouses were often challenging to maintain due to issues with network connectivity, security, and high maintenance costs.
• 🚀 Amazon Redshift addresses these issues by offering a cloud-based solution that is scalable, cost-effective, and simplifies data management.
• 🏢 Companies like DNA, a telecommunication company, have seen a significant increase in application performance by using Amazon Redshift for data management.
• 💰 Amazon Redshift is considered cost-effective compared to other cloud data warehouse services and offers high performance.
• 🔑 The service provides advantages such as high performance, low cost, scalability, availability, security, flexibility, and ease of database migration.
• 🏭 The architecture of Amazon Redshift consists of a leader node that manages client applications and compute nodes that process data.
• 📚 Redshift utilizes column storage and compression techniques to optimize query performance and reduce storage requirements.
• 🏷️ Large enterprises such as Pfizer, McDonald's, and Philips rely on Amazon Redshift for their data warehousing needs.
• 🔍 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

00:00

🚀 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.

05:02

🔑 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.

10:04

🏢 Companies Utilizing Amazon Redshift and Upcoming Demo

The speaker lists知名企业 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.

15:05

🛠️ 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.

20:06

🔄 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.

Keywords

💡Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud provided by Amazon Web Services (AWS). It is designed to make it simple and cost-effective to efficiently analyze all data using fast and flexible analytic capabilities. In the video, Amazon Redshift is the central topic, with discussions on its advantages, architecture, and use cases, illustrating its role in modern data warehousing and analytics.

💡AWS

AWS stands for Amazon Web Services, which is one of the largest cloud service providers in the market. It offers a secure cloud service platform where users can create and deploy applications using various AWS services. In the context of the video, AWS is the provider of Amazon Redshift, emphasizing the integration of Redshift with other AWS services.

💡Data Warehouse

A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. It stores data from one or more sources and serves the purpose of data analysis. In the script, the traditional challenges of data warehouses are discussed, such as the difficulty of fetching data and high maintenance costs, which Amazon Redshift aims to solve.

💡Scalability

Scalability refers to the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. In the video, scalability is highlighted as one of the key advantages of Amazon Redshift, allowing users to scale up or down resources on the fly based on their requirements.

💡Column Storage

Column storage is a database storage approach where data is stored column-wise instead of row-wise. This method is beneficial for analytic workloads as it allows for faster query performance by reducing the amount of data that needs to be read. The script explains column storage as an essential factor in optimizing query performance in Amazon Redshift.

💡Compression

Compression in the context of databases refers to the process of reducing the size of data to save storage space and improve query performance. The script mentions column-level compression as a feature of Amazon Redshift, which decreases storage requirements and enhances the speed of data retrieval.

💡Leader Node

In Amazon Redshift, the leader node is responsible for coordinating and distributing the workload among the compute nodes. It manages client connections and directs the execution of SQL commands. The script describes the leader node as a critical component of the Redshift architecture, which sends instructions to compute nodes and aggregates results.

💡Compute Nodes

Compute nodes in Amazon Redshift are the individual database servers that perform the actual data processing. They work in parallel to handle queries and perform data operations. The script explains that these nodes are scalable and can be increased based on demand, which contributes to the high performance of Redshift.

💡JDBC

JDBC stands for Java Database Connectivity, which is a Java API that allows applications to connect to a database and execute SQL statements. In the video, JDBC is mentioned as a method for client applications to interact with the leader node in Amazon Redshift, emphasizing its role in enabling database connectivity.

💡ODBC

ODBC stands for Open Database Connectivity, which is a standard API for accessing database management systems. The script mentions ODBC as a means for the leader node to have direct interaction with Amazon Redshift, allowing for live data access and manipulation.

💡Database Migration

Database migration refers to the process of transferring data from one database system to another. In the context of the video, database migration is discussed as a straightforward process when moving from a traditional data center to Amazon Redshift, facilitated by inbuilt tools provided by AWS.

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

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