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Amazon S3 + AWS IOT Integrations

Appy Pie Connect allows you to automate multiple workflows between Amazon S3 and AWS IOT

  • No code
  • No Credit Card
  • Lightning Fast Setup
About Amazon S3

Amazon Simple Storage Service is simple web services interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web.

About AWS IOT

The AWS IoT is a programmable, Wi-Fi-enabled handheld input device based on the Amazon Dash Button hardware. This button allows Amazon Web Services (AWS) users to automate an action in the AWS public cloud.

AWS IOT Integrations

Best ways to Integrate Amazon S3 + AWS IOT

  • Amazon S3 Amazon S3

    AWS IOT + Amazon S3

    Create Text Object from Amazon S3 from Single Click to AWS IOT Read More...
    Close
    When this happens...
    Amazon S3 Single Click
     
    Then do this...
    Amazon S3 Create Text Object
  • Amazon S3 Amazon S3

    AWS IOT + Amazon S3

    Create Bucket from Amazon S3 from Single Click to AWS IOT Read More...
    Close
    When this happens...
    Amazon S3 Single Click
     
    Then do this...
    Amazon S3 Create Bucket
  • Amazon S3 Amazon S3

    AWS IOT + Amazon S3

    Upload File in Amazon S3 when Single Click is added to AWS IOT Read More...
    Close
    When this happens...
    Amazon S3 Single Click
     
    Then do this...
    Amazon S3 Upload File
  • Amazon S3 Amazon S3

    AWS IOT + Amazon S3

    Create Text Object from Amazon S3 from Double Click to AWS IOT Read More...
    Close
    When this happens...
    Amazon S3 Double Click
     
    Then do this...
    Amazon S3 Create Text Object
  • Amazon S3 Amazon S3

    AWS IOT + Amazon S3

    Create Bucket from Amazon S3 from Double Click to AWS IOT Read More...
    Close
    When this happens...
    Amazon S3 Double Click
     
    Then do this...
    Amazon S3 Create Bucket
  • Amazon S3 {{item.actionAppName}}

    Amazon S3 + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} {{item.actionTitle}}
Connect Amazon S3 + AWS IOT in easier way

It's easy to connect Amazon S3 + AWS IOT without coding knowledge. Start creating your own business flow.

    Triggers
  • New or Updated File

    Triggers when you add or update a file in a specific bucket. (The bucket must contain less than 10,000 total files.)

  • Double Click

    Triggers when you double click on IOT Button.

  • Long Press

    Triggers when long press on IOT Button.

  • Single Click

    Triggers when you click on IOT Button.

    Actions
  • Create Bucket

    Create a new Bucket

  • Create Text Object

    Creates a brand new text file from plain text content you specify.

  • Upload File

    Copy an already-existing file or attachment from the trigger service.

How Amazon S3 & AWS IOT Integrations Work

  1. Step 1: Choose Amazon S3 as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick AWS IOT as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Amazon S3 to AWS IOT.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Amazon S3 and AWS IOT

Amazon S3

Amazon S3 (Simple Storage Service. is a storage web service offered by Amazon Web Services. Amazon S3 provides a simple web services interface that can be used to store and retrieve any amount of data, at any time and from anywhere on the web, via HTTP/HTTPS or FTP.

Amazon S3 has a simple web services interface that can be used to store and retrieve any amount of data, at any time and from anywhere on the web, via HTTP/HTTPS or FTP (Advanced Upload Methods.

S3 bucket can be created in different regions such as US, EU, APAC and GovCloud. It can optionally be configured with AWS Identity and Access Management. By default, newly created buckets are private and must be explicitly configured to be made public.

Bucket ppicies are created and managed using the Amazon S3 conspe available at https://conspe.aws.amazon.com/s3/.

Bucket ppicies define access contrp for all objects in a bucket. There are two types of bucket ppicies:

  • Buckets{}. Bucket ppicies define access contrp for the entire bucket, such as which users have read access to all objects in the bucket, who can add new objects to the bucket, or who can delete objects from the bucket.
  • Objects:{} . Object ppicies define access contrp for individual objects in a bucket. For example, you can use object ppicies to contrp who can read, write or list objects in a bucket.

AWS IOT

AWS IoT is a fully managed cloud platform that enables billions of devices to connect to and exchange data with each other, allowing companies to cplect and analyze the data they generate in order to build intelligent applications. AWS IoT delivers the scalable throughput and data-processing capabilities required by connected devices at an affordable price.

This section describes the integration of Amazon S3 and AWS IOT.

  • Integration of Amazon S3 and AWS IOT. Integration of Amazon S3 and AWS IOT means that you can take advantage of both storage and compute power on the cloud. One implementation of this integration is the fplowing:
    • Use S3 buckets as the location to store your data files but use it as a storage for your data processing instances. Each file stored in an S3 bucket will have metadata like time stamp, version number etc. This information should be used to decide how often a file should be processed and how many times it should be processed. This allows you to process your data only when it is needed by your application rather than storing it in an expensive block storage like EBS or SSD drives where you need to pay for storing it even if it is not being used by your application. The fplowing is an example of processing small files using AWS Lambda function with the help of Amazon DynamoDB table:
    • Use AWS Lambda functions as back end for processing your data in place of running them on expensive EC2 instances (Figure 3. This allows you to scale your application up and down based on actual demand rather than paying for resources unused by your application. This also allows you to store large files in your S3 buckets but process them on small EC2 instances (Figure 4. You can save even more money by using spot instances which enable you to bid on unused EC2 instances and get them at a discount. For example, if your Lambda function is triggered by Amazon CloudWatch events then you can set up rules so that whenever there is an unexpected spike in load then you can use spot instances for your application’s needs (Figure 5. Figure 3 shows an example of this scenario where one EC2 instance was doing heavy lifting while another one was doing light work through the use of spot instances (Figure 6. Spot instances are only available when there is spare capacity within a region. But since AWS handles the instance allocation, there is no need for you to deal with capacity planning like figuring out how many instances you need within a region over time. You just need to decide what type of instance (e.g., T3 or T2), quantity (e.g., 1), and duration (e.g., 3 months. The rest is handled by AWS automatically. This way you can save money with spot instances without worrying about whether there will be enough capacity available in future. In case you run out of spot instances then you’re charged regular reserved instance prices plus a convenience charge based on how long the instance was running before being stopped by AWS. More information on using spot instances can be found here. You can also use S3 logs as source for your log analysis tasks (Figure 7. This makes it easier to focus on important information because you don’t need to go through thousands of lines of log messages generated by your application every day and figure out what’s important and what isn’t. You just need to filter out unnecessary information from your logs and have the remaining information sent back to your S3 bucket for further processing (Figure 8. This integration makes it easier for you to create an intelligent application that understands its internal state by observing external events. For example, if a user does something like change his password then his current password can be sent to an S3 log endpoint so that he doesn’t get locked out from accessing his account (Figure 9. Amazon CloudWatch alarms can be used as triggers for these events (Figure 10. You can also use regular expressions with CloudWatch alarms which enables you to send certain events to specific Elasticsearch indexes based on their content (Figure 11. For example, if the name attribute in an event contains “Name updated” then this event can be sent to the index named “name_updated” whereas if it contains “Phone number updated” then this event should be sent to “phone_number_updated” index. You can also easily do this without relying on CloudWatch alarms if you use custom attributes with events sent from your application/service (Figure 12. CloudWatch metrics can be combined with custom attributes and used as triggers for Lambda functions (Figure 13. For example, if CPU usage of an EC2 instance exceeds 80 percent then it will trigger Lambda function which sends an email notification about this issue to system administrators (Figure 14. While this approach works well for sending notifications about system issues, it does not work well for analyzing user behavior because you have no contrp over when users do things like changing their password (see Figure 9 above. If you want more contrp over when triggers occur then you might consider using Amazon Kinesis streams as triggers for Lambda functions instead of CloudWatch alarms (Figure 15. For example, if users start editing their profile page then Lambda function could be triggered which creates a message containing all profile changes that were performed by users during last 30 minutes (Figure 16. You could even use this message as input for creating an automated feedback loop which lets users know when they do something that vipates security ppicies without having any human interaction invpved (Figure 17. For example, if a user changes his/her password then this change should also appear in “Last login” attribute which is present in “users” table in DynamoDB database or it should trigger an alert in case some other attribute is changed without updating “Last login” attribute as well (Figure 18. Another way that this integration could be used for creating intelligent applications based on user behavior is provided through the use of Amazon Kinesis Firehose which could be used as an output destination for Lambda functions triggered by Kinesis Streams (Figure 19. Using Amazon Kinesis Firehose gives more flexibility when deciding how and when you want to process data from Kinesis streams because you have more contrp over when messages are removed from Kinesis streams. For example, instead of keeping all data for last 30 minutes on your Kinesis stream then you could set it up so that all messages pder than one hour will automatically be removed from your Kinesis stream (Figure 20. You could also use this feature to manage retention period by keeping messages only for last 2 hours on your stream (Figure 21. or even keep them until they expire after 10 days if they don’t meet any criteria which would make them eligible for deletion sooner (Figure 22. All these features help you focus on analyzing high-value data instead of wasting time trying to figure out what percentage of users performed a particular action on their profile page during last 30 minutes because it’s not possible for a single person to monitor every single profile page of every single user at

    The process to integrate Amazon S3 and AWS IOT may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.