Table of Contents

1. Challenge

   Investigate the Lab Environment

   Log in to the provided AWS Account and inspect the provided resources.

   You should find a Redshift cluster with the ID users-cluster as well as an empty S3 bucket with a name that begins with users-data-. Additionally, there is an IAM role attached to users-cluster which will be used when launching a second Redshift cluster with the ID RedshiftS3.

   Note: The ARN of this role is provided in the lab interface, as is the name of the S3 bucket for easy reference.

2. Challenge

   Launch the Target Redshift Cluster

   In the Redshift web console, launch a new cluster with the same configuration as users-cluster, using the ID users-cluster-2.

   Choose the dc2.large node type and set the number of nodes to 1.

3. Challenge

   Copy the Existing Redshift Table to S3

   Utilizing either the Redshift web console query editor or the client of your choosing, use the UNLOAD command to copy the existing test table to the provided S3 bucket in parquet format.

   UNLOAD ('select * from users_data')
   TO '<users-data-bucket>'
   IAM_ROLE '<RedshiftS3 ARN>'
   FORMAT AS PARQUET;
   

   create table users_data(
     id_value varchar(64),
     name_first varchar(64),
     name_last varchar(64),
     location_country varchar(32),
     dob_age int,
     picture_large varchar(64),
     primary key(id_value)
   )
   distkey(location_country)
   compound sortkey(id_value);
   

4. Challenge

   Copy Data from S3 to the Newly Launched Redshift Cluster

   Utilizing either the Redshift web console query editor or the client of your choosing, create a new table in users-cluster-2 which matches the table provided on users-cluster. Once this table is created, use the COPY command to load the data which has been backed up in S3 to your new table.

   Note: Table definition information is provided in the lab instructions.

   COPY users_data    
   FROM '<users-data-bucket>'
   IAM_ROLE '<RedshiftS3 ARN>'
   FORMAT AS PARQUET;
   

5. Challenge

   Check Your Data

   Once the above objectives are fulfilled, you should be able to run the following query from both clusters and receive an identical response:

   select * from users_data limit 10;