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markzegarelli
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Merge branch 'master' into pwseg-patch-1
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src/_sass/components/_markdown.scss

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}
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a[target="_blank"]:after{
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a[target="_blank"]:after:not(.reference-button){
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content: url("/docs/images/external-link-alt-solid.svg");
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margin-left: 4px;
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}

src/connections/sources/catalog/libraries/server/net/index.md

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Analytics.Client.Page("019mr8mf4r", "Login", new Properties() {
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{ "path", "/login" },
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{ "title", "Initech Login" }
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}), new Options()
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}, new Options()
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.SetContext (new Context () {
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{ "userAgent", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36"},
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{ "ip", "12.212.12.49" },
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{ "clientId", User.ClientId }
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}
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}
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});
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}));
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```
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## Anonymous ID

src/connections/sources/catalog/libraries/website/shopify-littledata/index.md

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Below is a table of events that **Shopify by Littledata** sends to Segment through the analytics.js library. These events will show up as tables in your warehouse, and as regular events in your other Destinations supporting device-mode.
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You can _opt out_ of device-mode pageviews or events by setting `disableClientSideEvents: true` or `disablePageviews: true` in the `LittledataLayer` settings.
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| Event Name | Description |
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| --------------------- | ------------------------------------------------------------------- |
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| Cart Viewed | A user has viewed the /cart page |
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| Thank you Page Viewed | A user has viewed the thank you page after completing an order\* |
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> note ""
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> *This is less reliable than the de-duplicated `Order Completed` event sent from the Littledata servers, but you can use it in device-mode destinations to trigger a conversion. The `payment_method` and `shipping_method` properties are not available with this event.
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> \*This is less reliable than the de-duplicated `Order Completed` event sent from the Littledata servers, but you can use it in device-mode destinations to trigger a conversion. The `payment_method` and `shipping_method` properties are not available with this event.
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## Cloud-mode events
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| Property Name | Description | Property Type |
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| ---------------------------- | -------------------------------------------------------------------------------------------------------------------------- | ------------- |
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| `accepts_marketing` | Whether the customer has accepted marketing | Boolean |
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| `createdAt` | The date customer record was created | Date |
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| `customerLifetimeValue` | The total spend of customer on the Shopify store | Double |
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| `default_address.street` | The customer's default street address | String |
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| `userId` | Chosen user identifier, defaulting to Shopify Customer ID | String |
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> note ""
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> *`revenue` is available only with the Order Completed event, and only if the store opts in via the Littledata application. Revenue is a reserved property in many Segment destinations. Opting in will override the `total` property sent to Google Analytics.
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> \*`revenue` is available only with the Order Completed event, and only if the store opts in via the Littledata application. Revenue is a reserved property in many Segment destinations. Opting in will override the `total` property sent to Google Analytics.
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## Product properties
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src/connections/storage/warehouses/index.md

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{% include content/whats-a-warehouse.md %}
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When selecting and building a data warehouse, there are three questions to consider:
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When selecting and building a data warehouse, consider three questions:
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1. What type of data will be collected?
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2. How many data sources will there be?
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3. How will the data be used?
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Relational databases are great when you know and predefine the information collected and how it will be linked. This is usually the type of database used in the world of user analytics. For instance, a users table might be populated with the columns "name", "email address", "plan name", etc.
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Relational databases are great when you know and predefine the information collected and how it will be linked. This is usually the type of database used in the world of user analytics. For instance, a users table might be populated with the columns `name`, `email_address`, or `plan_name`.
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Examples of data warehouses include Amazon Redshift, Google BigQuery, and Postgres.
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