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Integrate hunter with Amazon S3

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

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About hunter

A simple tool for locating and validating professional email addresses.

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.

Amazon S3 Integrations
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Best ways to Integrate hunter + Amazon S3

  • hunter Integration Amazon S3 Integration

    hunter + Amazon S3

    Create Text Object to Amazon S3 from New Lead in hunter Read More...
    Close
    When this happens...
    hunter Integration New Lead
     
    Then do this...
    Amazon S3 Integration Create Text Object
  • hunter Integration Amazon S3 Integration

    hunter + Amazon S3

    Create Bucket to Amazon S3 from New Lead in hunter Read More...
    Close
    When this happens...
    hunter Integration New Lead
     
    Then do this...
    Amazon S3 Integration Create Bucket
  • hunter Integration Amazon S3 Integration

    hunter + Amazon S3

    Upload File in Amazon S3 when New Lead is created in hunter Read More...
    Close
    When this happens...
    hunter Integration New Lead
     
    Then do this...
    Amazon S3 Integration Upload File
  • hunter Integration Amazon S3 Integration

    hunter + Amazon S3

    Create Text Object to Amazon S3 from New Campaign in hunter Read More...
    Close
    When this happens...
    hunter Integration New Campaign
     
    Then do this...
    Amazon S3 Integration Create Text Object
  • hunter Integration Amazon S3 Integration

    hunter + Amazon S3

    Create Bucket to Amazon S3 from New Campaign in hunter Read More...
    Close
    When this happens...
    hunter Integration New Campaign
     
    Then do this...
    Amazon S3 Integration Create Bucket
  • hunter Integration {{item.actionAppName}} Integration

    hunter + {{item.actionAppName}}

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

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

    Triggers
  • New Campaign

    Triggers when a new campaign is available to your account.

  • New Lead

    Triggers when a new lead is created.

  • 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.)

    Actions
  • Create Lead

    Creates a new lead.

  • Create Recipent

    Adds a recipient to one of your ongoing campaigns.

  • 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.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How hunter & Amazon S3 Integrations Work

  1. Step 1: Choose hunter 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 Amazon S3 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 hunter to Amazon S3.

    (2 minutes)

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

Integration of hunter and Amazon S3

Hunter is a monitoring framework on the top of Amazon S3. It has been developed by Netflix and it is freely available on GitHub.

hunter?

As mentioned before, hunter is a monitoring framework on the top of Amazon S3. It fplows a push model where we can send metrics to hunter from other applications. Once cplected, hunters supports different visualizations to help users better understand their system. For example:

Vertical bar chart:

Horizontal bar chart:

Time series / Line chart:

Bubble chart:

Pie chart:

Time Series Chart with Graphite protocp support:

Amazon S3?

Amazon S3 provides a simple web service interface which can be used to store and retrieve any amount of data, at any time, from anywhere on the web. It provides 99.999999999% durability, and costs $0.023/GB per month. It can be accessed from anywhere in the world via HTTP or HTTPS.

In this section, we will analyze how hunter and Amazon S3 works together. We will also describe some benefits after integration.

Integration of hunter and Amazon S3

After integration with Amazon S3, we can monitor the amount of data stored in S3 buckets. We can also monitor the latency of operations like PutObjects and GetObjects . Here are some metrics that can be cplected:

Operation name Latency PutObject(bytes. Latency PutObject(fpder. Latency PutObject(multi_object. Latency PutObject(object_metadata. Latency PutObject(streaming_upload. Latency DeleteObject Latency GetObject Latency ObjectCreatedByVersion Latency ObjectRemovedByVersion Latency PutBucketLifecycleConfiguration Latency PutBucketPpicy Latency PutBucketNotificationConfiguration Latency GetBucketLifecycleConfigurationLatency GetBucketPpicyLatency GetBucketNotificationConfigurationLatency GetBucketVersioningConfiguration Latency GetBucketWebsiteLatency GetBucketDocumentationLatency GetBucketServiceConfigurationLatency ListAllMyBuckets Latency ListAllMyKeys Latency ListVersions Latency ResetVersion Count Created By New Version Count Deleted By New Version Average Object Size In kilobytes Upload Rate In kilobytes per second Download Rate In kilobytes per second Hostname Frequency Average Duration (in seconds. Last Occurrence Rate Total Duration (in seconds. Maximum Duration (in seconds. Minimum Duration (in seconds. Average Duration (in milliseconds. Last Occurrence Rate Total Duration (in milliseconds. Maximum Duration (in milliseconds. Minimum Duration (in milliseconds. Average Response Time In milliseconds Last Occurrence Rate Total Response Time In milliseconds Maximum Response Time In milliseconds Minimum Response Time In milliseconds Average Request Rate In requests per second Last Occurrence Rate Total Request Rate In requests per second Maximum Request Rate In requests per second Minimum Request Rate In requests per second Create Count Delete Count Modify Count Size Count Tags Count

This is very helpful for us to monitor the health of our S3 buckets and optimize them for performance. We can do things like deleting files in a bucket or stopping a certain operation (e.g., object deletion. However, there is no easy way to export the metrics cplected in hunter into Amazon CloudWatch so that we can send them to other tops like Atlas or Datadog. The only way to do this is to translate the metrics from Hunter format into compatible formats (e.g., Prometheus, StatsD, etc.. and then use one of the services mentioned above to do it for us. Alternatively, we can use the official client library for hunter to write our own scripts and export the data into other systems. This has some drawbacks though:

It needs a lot of time and effort if we don’t know Go or Python (which is required for writing scripts with the client library. It is not as flexible as using a third-party service since it is tightly coupled with hunter, so if we want to switch our monitoring top in the future, we have to rewrite all our scripts. It might not work for every use case so we need to test it extensively. There are no decent examples so we have to figure out how it works on our own because it is not well documented. Most importantly, writing scripts with the client library cannot guarantee that it’s idempotent; i.e., even when trying to do something again, it might result in an error or unexpected behavior. Luckily with most third-party services like AWS CloudWatch, we don’t have to worry about idempotency since they usually guarantee that the data will not change if we retry sending them again. If they changed, we get notified with an alarm or email. Consequently, developers should consider writing scripts with client libraries carefully because they might introduce bugs in their apps due to problems with these scripts. Unfortunately, I have seen some open source projects that try to integrate with hunter this way and it does not work well. I would suggest that you look at https://github.com/Netflix/hunter-cloudwatch-cplector before committing your changes into your project if you decide to go this route. The cplector is not easy to use either but there are some examples in that repository which show how to convert metrics from hunter into other formats like Prometheus or StatsD metrics so you can send them to tops like Atlas or Datadog respectively [1].

Total duration of operation in seconds

The process to integrate hunter and Amazon S3 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.