1.2 — On the top right corner, select the region where you want to launch the Aurora DB cluster.

1.3 — Click on “Create database” in the Amazon Aurora window.

Before continuing, switch to the new database creation flow:

1.4 — On Database engine, select “Amazon Aurora”.

1.5 — For edition, select "Amazon Aurora with PostgreSQL compatibility".

1.6 — Select "Serverless".

1.7 — Choose an identifier for your Aurora DB cluster, e.g. "database-1".

1.8 — Select a username and password for your database.

1.9 — For maximum Aurora capacity unit, select 2.

1.10 — Select the VPC where you want to create the database.

Note that once created, a database can't be migrated to a different VPC.

1.11 — Click on “Additional connectivity configuration”.

1.12 — Select the default value for Subnet group.

1.13 — On VPC security group, select “create new”. If you happen to have a security group that allows incoming TCP connections on port 3306, you can choose it instead.

1.14 — In the new VPC security group name, type “aurora-tutorial”.

1.15 — Enable the Data API.

1.16 — On “Deletion protection” uncheck “Enable deletion protection”.

The best practice is to enable the Deletion protection, but if you want to delete the database at the end of the tutorial you can leave the option unchecked.

After a quick review of all the fields in the form, you can proceed.

1.17 — Click on “Create database”.

While the instances are being created, you will see a banner explaining how to obtain your credentials. This is a good opportunity to save the credentials somewhere, as this is the only time you will be able to view this password.

1.18 — Click on the database name.

1.19 — In the “Configuration” tab, copy the Cluster ARN and keep it handy. You will need it later.

1.20 — Open the left panel and click on “Query Editor”.

1.21 — Select the “database-1”, enter “postgres” as the database username and input the database password you created earlier, then type “postgres” for the database name.

1.22 — Click on “Connect to database”.

1.23 — You can now click “Run” and execute the sample query.

1.24 — Create a database by running the following query:

CREATE DATABASE tutorial;

1.25 — Click on “Change database”.

1.26 — Change the database to the one you just created.

1.27 — Create a table with this query:

CREATE TABLE sample_table(received_at TIMESTAMP, message VARCHAR(255));

By connecting to the database with the Query Editor, a Secret is created that you will use later on in your Lambda function. Leave this tab open, as you will need to run some queries at the end of the tutorial.

Open a new tab and head to the AWS Secrets Manager. Then follow the steps below to retrieve the Secret ARN. 

1.28 — Find the secret containing the “RDS database postgres credentials for database-1”.

1.29 — After clicking on the Secret name, copy the Secret ARN and keep it handy.

2.2 — Enter a name for your new environment and click on “Next step”.

2.3 — Scroll down and click on “Next step”.

2.4 — Click on “Create environment”.

2.5 — Once the environment is ready, click on “Create Lambda Function”.

2.6 — As the name of the function, enter “auroratest”. Then click “Next”.

2.7 — Select the “empty-python” blueprint and click “Next”.

2.8 — In the following screens, click “Next”, “Next”, then “Finish”.

Now the environment is ready and you will see a blueprint for a Lambda Function.

Before you start coding you need to configure some permissions as described in Step 3. Keep this environment open and use other tabs for completing the upcoming steps.

3.2 — Click on the role name, then click on “Attach policies”.

3.3 — Select the “AmazonRDSDataFullAccess“ policy, then click on “Attach policy”.

You will see a screen with a light green banner explaining that the policy was successfully attached.

4.2 — Install the boto3 dependency by running this command in the console:

$ pip install boto3 -t boto3

4.3 — Delete the code in the lambda_function file and in its place copy this sample code.

4.4 — Replace the cluster_arn and secret_arn values with the Cluster ARN and Secret ARN values obtained in previous steps.

4.5 — Click on “File > Save”, then select you Lambda Function on the right panel.

4.6 — Deploy your Lambda Function by clicking on the up arrow.

5.2 — Leave all the default values and click on “Create topic”.

You will see a green banner indicating that the topic was successfully created.

5.3 — Copy the SNS ARN and keep it handy.

Keep this tab open, as you will use it to publish a message once the Lambda Function is configured to read from the topic you created.

6.2 — Click on “Add trigger”.

6.3 — Type “SNS” and select the “SNS” services from the dropdown menu.

6.4 — In the “SNS Topic” field, enter the SNS ARN.

6.5 — Enable the trigger and click on “Add”.

7.2 — Enter any value for the “Subject” field.

7.3 — Enter any value for the “Body” field.

7.4 — Scroll down and click on “Publish message”.

Once the message is published, your Lambda Function will consume it and process it. In the next section, you will verify how the data was written to your Aurora database.

7.5 — Go to the tab where you left the Query Editor open. If you closed it, visit the RDS Dashboard and connect to the Query Editor as explained in 1.20.

7.6 — Connect to the “tutorial” database.

7.7 — Select all the records from sample_table:

SELECT * FROM sample_table;

7.8 — Click on “Run” and scroll down to see the results.

All should be working now. You can experiment by changing the messages you send via SNS, or you can alter the lambda_handler function any way you want.

8.2 — Click on “Actions > Delete”.

8.3 — Visit the SNS Dashboard and click on “Topics” on the left panel.

8.4 — Select the topic you created in Step 5 and click on “Delete”.

8.5 — You will be asked for confirmation. Type “delete me” to confirm and click on “Delete”.

8.6 — Head to the AWS Cloud9 console and select the environment you created for this tutorial.

8.7 — Click on “Delete”.

8.8 — When asked for confirmation, type “Delete” and click the “Delete” button.

8.9 — Go to the Amazon RDS console and select the database you created for this tutorial.

8.10 — Click on “Actions > Delete”.

You will be asked if you want to create a final snapshot. That's usually a good idea, but it's not necessary for this tutorial.

8.11 — When asked for confirmation, select “No” to the question of whether to create a final snapshot. Then check “I acknowledge…” and click on “Delete DB cluster”.

8.12 — Go to the AWS Secrets Manager, find the secret containing the “RDS database admin credentials for database-1”, and click on the name of the secret.

8.13 — Click on “Actions > Delete secret”.

8.14 — The secrets can't be deleted immediately. The minimum waiting period for a scheduled deletion is 7 days. Select 7 days and click on “Schedule deletion”.

8.15 — Go to the AWS IAM console. Click “Roles” and search for a role starting with “cloud9-”.

8.16 — Select the role and click on “Delete role”.

8.17 — When asked for confirmation, click on “Yes, delete”.