by Saurabh Shrivastava, Sameer Goel, Pratik Patel Please follow the complete blog

Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and Internet of Things (IoT) data so you can develop insights on sensor activity across various industries, including smart spaces, connect factories, smart packing, fitness monitoring, and more. It’s important to store these data points in a centralized data lake in real time, where it can be transformed, analyzed, and combined with diverse organizational datasets to derive meaningful insights and make predictions.

A popular use case in the wind energy sector is to protect wind turbines from wind speed. As per National Wind Watch, every wind turbine has a range of wind speeds, typically around 30–55 mph, in which it produces maximum capacity. When wind speed is over 70 mph, it’s important to start shutdown in order to protect the turbine from a high wind storm. Customers often store high-velocity IoT data in DynamoDB and use Amazon Kinesis streaming to extract data and store it in a centralized data lake built on Amazon Simple Storage Service (Amazon S3). To facilitate this ingestion pipeline, you can deploy AWS Lambda functions or write custom code to build a bridge between DynamoDB Streams and Kinesis streaming.

Today, Amazon Kinesis Data Streams for DynamoDB enables you to publish item-level changes in any DynamoDB table to a Kinesis data stream of your choice. Additionally, you can leverage this feature for use cases that require longer data retention on the stream and fan out to multiple concurrent stream readers. You can also integrate with Amazon Kinesis Data Analytics or Amazon Kinesis Data Firehose to publish data to downstream destinations such as Amazon Elasticsearch Service (Amazon ES), Amazon Redshift, or Amazon S3.

In this post, you use Kinesis Data Analytics Flink (KDA Flink) and Amazon Simple Notification Service (Amazon SNS) to send a real-time notification when wind speed is more than 60 mph, so that the operator can take action to protect the turbine. You use the Kinesis Data Streams for DynamoDB feature and take advantage of managed streaming delivery of DynamoDB data to other AWS services without having to use Lambda or write and maintain complex code. To process DynamoDB events from Kinesis, you have multiple options: Amazon Kinesis Client Library (KCL) applications, AWS Lambda, and KDA Flink. For this post, we showcase KDA Flink, but this is just one of many available options.

The following architecture diagram illustrates the wind turbine protection system.

In the architecture, you have high-velocity wind speed data coming from the wind turbine and stored in DynamoDB. To send an instant notification, you need to query the data in real time and send a notification when the wind speed is beyond the required limit. To achieve this goal, you enable Kinesis Data Streams for DynamoDB, then use KDA Flink to query real-time data in a 60-second tumbling window. This aggregated data is stored in another data stream, which triggers an email notification via Amazon SNS using Lambda when the wind speed more than 60 mph. You can build this entire data pipeline in a serverless manner.

To replicate a real-life scenario, you need a wind turbine data simulator. We use Amazon Amplify to deploy a user-friendly web application that can generate the required data and store it in DynamoDB. You need to have a HGitub account to fork the Amplify app code and deploy it in your AWS account. Complete the following steps to deploy the data simulator web app:

1. Choose the following AWS Amplify link to launch the wind turbine data simulator web app: To Do: move code to AWS samples repo

2. Choose Connect to GitHub and provide credentials if needed.

3. In the Deploy App section, under Select service role, choose Create new role.

4. Follow the instructions to create the role amplifyconsole-backend-role.

5. When the role is created, choose it from the drop-down menu.

6. Choose Save and deploy.

On the next page, you see that the app dynamodb-streaming is ready to deploy.

7. Choose Continue.

On the next page, you can see the app build and deploy progress, which may take up to 10 minutes to complete.

8. When the process is complete, choose the URL on the left to access the data generator UI.

9. Make sure to save this URL to use in later steps.

You also get an email during the build process related to your SSH key. This email indicates that the build process created an SSH key on your behalf to connect to the Amplify application with GitHub.

10. On the login page, choose Create account.

11. Provide a user name, password, and valid email where the app can send you a one-time passcode to access the UI.

12. After you log in, choose Generate Data to generate wind speed data.

13. Choose the Refresh icon to show the data in the graph.

You can generate a variety of data by changing the range of minimum and maximum speeds and number of values.

To see the data in DynamoDB, choose the DynamoDB icon, note the table name that starts with windspeed-, and navigate to that table on the DynamoDB console.

Now that the wind speed data simulator is ready, let’s deploy the rest of the data pipeline.

You use AWS CloudFormation templates to create all the necessary resources. This removes opportunities for manual error, increases efficiency, and ensures consistent configurations over time. You can view template in the GitHub repository.

1. Choose Launch with CloudFormation Console:

template.yml to be hosted in blog bucket during final staging. Further, hyperlink to be changed on the ‘Launch with CloudFormation Console’ (https://console.aws.amazon.com/cloudformation/home?region=us-west-2\#/stacks/create/template?stackName=kds-ddb-blog&templateURL=**https://s3-us-west-2.amazonaws.com/bucket.aws/ddbstreaming-blog/template.yaml)**

2. Choose the US West (Oregon) Region (us-west-2).

3. For pEmail, enter a valid email that the analytics pipeline can send notifications to.

4. Choose Next.

5. Acknowledge that the template may create AWS Identity and Access Management (IAM) resources.

6. Choose Create stack.

This CloudFormation template creates the following resources in your AWS account:

• An IAM role to provide a trust relationship between Kinesis and DynamoDB to replicate data from DynamoDB to the data stream

• Two data streams:

  • An input stream to replicate data from DynamoDB

  • An output stream to store aggregated data from the KDA Flink app

• A Lambda function

• An SNS topic to send an email notification of high wind speeds

7. When the stack is ready, on the Outputs tab, note the values of both data streams.

Check your email and confirm your subscription to receive notifications.

Make sure check your junk folder if you don’t see the email in your inbox.

We can now use the Kinesis streaming for DynamoDB feature, which enables you to have your data in both DynamoDB and Kinesis without having to use Lambda or write custom code.

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