In this video, you'll see how you can publish products on AWS Data Exchange.
With AWS Data Exchange, data providers can easily create data sets and revisions;
import assets from Amazon Simple Storage Service (Amazon S3), Amazon API Gateway,
or Amazon Redshift; and publish products publicly or privately to specific AWS accounts.
AWS Data Exchange is a service that makes it easy for AWS customers to find, subscribe
to, and use third-party data in the AWS Cloud.
Before registering as a data provider with AWS Data Exchange,
you must first have an AWS account and be registered to sell on AWS Marketplace,
since that is where potential subscribers will find your data products.
Once you have created an AWS account,
open this page and click the button to initiate the registration process.
Specify your business name, and then review and accept the terms and conditions.
You also must specify your business purpose,
the categories of products you’ll list on Marketplace, and the pricing model you’ll use.
If you want to submit paid products to AWS Marketplace or AWS Data Exchange,
you must provide your tax and banking information.
To do this, go to the Account Settings page, select the Provide tax and banking
information tab, and then click Start to complete the tax and banking wizard.
This submits your tax and banking information in the AWS Marketplace Management Portal.
For the purposes of this demonstration, we've completed the registration steps
and now we are going to list free Data Exchange products.
Let’s switch to the AWS Data Exchange console, where we’re logged in with our
data provider account, and navigate to the region we want to release the data from.
Before creating a product, you need to build one or more data sets to include.
A data set is a dynamic collection of file-based content.
To create our data set, we’ll choose Owned data sets from the left navigation pane,
and then click the Create Data Set button.
First, we’ll select a data set type.
As you can see, AWS Data Exchange supports Amazon S3 objects as a delivery method.
It also supports other data set types
such as Amazon Redshift datashare and Amazon API Gateway API.
For our purposes, we will deliver data via S3 objects.
Let's define our data set.
We can use markdown formatting to preview how the description will
appear to prospective subscribers on AWS Data Exchange and AWS Marketplace.
We can optionally use tags to organize and track our data resources.
Let's create the data set.
Next, we'll create a revision,
which is a container for one or more assets in the data set.
Revisions enable providers to update data sets without
having to worry about changes to historical data.
We can include a brief comment about the data contained in this revision.
Again, we can add tags to organize our revisions. Let's move on.
Next, we'll add assets to the revision we just created.
An asset in AWS Data Exchange is a piece of data--in this case, an S3 object.
The asset can be a structured data file, an image file,
or some other data file that can be stored as an S3 object.
Let's locate the data we want to put in our data set’s revision.
In this case, we'll use some public data.
Let's add and import the assets.
It will take a few moments for the data import job to complete.
Let's scroll down to view the job's progress.
Once the import is completed, we can finalize the revision.
Now we can publish our data set as a product to AWS Data Exchange.
We’ll do that from the products page.
From the Products page, choose the Publish new product button.
We can choose to list the product publicly on the AWS Marketplace catalog
or keep it private and create custom offers for specific AWS accounts.
In this case, we'll keep the product private.
After we indicate whether sensitive data is contained in our product,
we can move on to the next step.
Now we’ll select the data set we created earlier to include in our product.
Notice that this product contains one data set already.
Products can contain multiple data sets.
Let’s add the selected data set.
Next, we’ll need to define the revision access rules.
Here, we specify access rules for the product.
As a provider, you have the flexibility to price historical and future data
appropriately without having to create and maintain multiple data sets, while providing
subscribers an ability to pay for and access the revisions that they actually need.
For example, we can allow all, none, or a fixed number of historical revisions,
and all or none of the future revisions.
For this demonstration, we'll allow all pre-existing and all future revisions.
Let's move to the next step.
Here we can supply details to help prospective
subscribers decide whether to purchase the product.
Let's give the product a descriptive name.
We also have a product logo and a support
contact who subscribers can email with questions.
We can define two categories for the product.
Here, we can select a data set and then add a data dictionary to it.
A data dictionary explains what columns are included in the data set and their meaning.
Subscribers can view and download the data dictionary before they subscribe
to the product to evaluate if the product meets their needs.
We can download a blank data dictionary template, view an example,
or add a file that has already been prepared. We’ll add a prepared file.
We can preview our data dictionary.
We can see the field name, description, example data,
the data type, associated table name, and more.
We can also add up to 10 samples. In this case, we will only upload one.
Let’s save these additions.
We also have a short description, which will appear on the Products catalog
page on AWS Data Exchange and AWS Marketplace,
and a longer description for the product details page.
Again, we can use the markdown preview feature to see how the
text will appear to potential subscribers.
Let's move on to the next step.
Before we can publish our product, we need to specify a price structure
and deﬁne the data subscription agreement terms
that subscribers are agreeing to when they purchase the product.
Because our product visibility is set to private, we'll create a private offer.
Here, we need to specify the AWS Account ID of the
data subscriber we are making this private offer for.
After this Private Offer has been created,
the subscriber will be able to see and accept the private offer from their AWS account.
We must also provide a description, such as a company name.
Notice that we can remove and add more than one subscriber here.
Next, we'll outline the duration of the subscription and pricing details.
When we registered with AWS Marketplace,
we specified that we'd provide free products, so we can't edit the price here.
If tax and banking information had been provided during the registration process,
we would be able to specify the amount here, as well as a payment schedule.
For this demonstration, we will just specify the duration as 12 months.
We can adjust the date for the subscriber to accept the offer.
We'll use the default expiration of 29 days from now.
Taxes are automatically managed within the AWS
Marketplace Management Portal in the Settings tab.
Here, we can choose to supply a custom data
subscription agreement or use the default template.
All data providers who charge money for their products,
whether they provide refunds or not, must specify their refund policy.
Since our product is available for free, this requirement is not applicable.
Lastly, we'll choose whether or not to allow auto-renewal on the offer.
Let's continue.
Before we publish, we’ll review the product information one more time.
Notice that the data dictionary we added will be visible to subscribers.
We can preview the product detail page to see how the information will look.
Now that we're satisfied with the information provided,
we can publish the product.
This completes the manual portion of publishing a data product with a private offer.
Behind the scenes, AWS Data Exchange now prepares and publishes the product.
On the Product overview page, the status of the product becomes
Awaiting approval and then changes to Published after it's published.
You've just seen how you can publish products on AWS Data Exchange.
You can learn more about this topic in the description and links for this video.
Thanks for watching. Now it's your turn to try.