merge master

markzegarelli · markzegarelli · commit f59a6eae15e7 · 2021-07-01T17:21:06.000-07:00
diff --git a/scripts/catalog_papi.js b/scripts/catalog_papi.js
@@ -386,6 +386,18 @@ const updateDestinations = async () => {
       return 0;
     })
 
+    const clone = (obj) => Object.assign({}, obj)
+    const renameKey = (object, key, newKey) => {
+      const clonedObj = clone(object);
+      const targetKey = clonedObj[key];
+      delete clonedObj[key];
+    
+      clonedObj[newKey] = targetKey;
+      return clonedObj;
+    };
+
+    destination.supportedMethods = renameKey(destination.supportedMethods, 'pageview', 'page')
+
     let updatedDestination = {
       display_name: destination.name,
       name: destination.name,
@@ -502,5 +514,5 @@ const updateWarehouses = async () => {
 }
 
 updateDestinations()
-updateSources()
-updateWarehouses()
+//updateSources()
+//updateWarehouses()
diff --git a/src/_data/catalog/destination_categories.yml b/src/_data/catalog/destination_categories.yml
@@ -1,5 +1,6 @@
-# AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
-# destination categories last updated 2021-06-30 
+# AUTOGENERATED FROM PLATFORM API. DO NOT EDIT
+# destination categories last updated 2021-07-01 
+
 items:
 - display_name: A/B Testing
   slug: a-b-testing
diff --git a/src/_data/catalog/destinations.yml b/src/_data/catalog/destinations.yml
@@ -1,4 +1,4 @@
-# AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
+# AUTOGENERATED FROM PLATFORM API. DO NOT EDIT
 # destination data last updated 2021-07-01 
 items:
 - display_name: ActiveCampaign
diff --git a/src/_data/catalog/source_categories.yml b/src/_data/catalog/source_categories.yml
@@ -1,5 +1,10 @@
+<<<<<<< HEAD
 # AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
 # source cateogries last updated 2021-06-30 
+=======
+# AUTOGENERATED FROM PLATFORM API. DO NOT EDIT
+# source cateogries last updated 2021-07-01 
+>>>>>>> master
 items:
   - display_name: A/B Testing
     slug: a-b-testing
diff --git a/src/_data/catalog/sources.yml b/src/_data/catalog/sources.yml
@@ -1,4 +1,8 @@
+<<<<<<< HEAD
 # AUTOGENERATED FROM PUBLIC API. DO NOT EDIT
+=======
+# AUTOGENERATED FROM PLATFORM API. DO NOT EDIT
+>>>>>>> master
 # sources last updated 2021-07-01 
 items:
   - display_name: .NET
diff --git a/src/connections/destinations/catalog/onesignal-new/images/audiences.jpg b/src/connections/destinations/catalog/onesignal-new/images/audiences.jpg
diff --git a/src/connections/destinations/catalog/onesignal-new/images/demo.png b/src/connections/destinations/catalog/onesignal-new/images/demo.png
diff --git a/src/connections/destinations/catalog/onesignal-new/images/track-example.png b/src/connections/destinations/catalog/onesignal-new/images/track-example.png
diff --git a/src/connections/destinations/catalog/onesignal-new/index.md b/src/connections/destinations/catalog/onesignal-new/index.md
@@ -0,0 +1,104 @@
+---
+rewrite: true
+title: OneSignal New Destination
+hide-personas-partial: true
+---
+
+[OneSignal](https://onesignal.com/?utm_source=segmentio&utm_medium=docs&utm_campaign=partners) is the market leader in driving customer engagement with multi-channel messaging across Web and Mobile Push, In-App Messages, SMS, and Email subscribers.
+This destination is maintained by OneSignal. For any issues with the destination, [contact OneSignal Support team](mailto:support@onesignal.com).
+
+{% include content/beta-note.md %}
+
+## Getting Started
+
+{% include content/connection-modes.md %}
+
+1. From the Destinations catalog page in the Segment App, click **Add Destination**.
+1. Search for OneSignal in the Destinations Catalog, and select the OneSignal destination.
+1. Choose which Source should send data to the OneSignal destination.
+1. Go to the [OneSignal dashboard](https://app.onesignal.com/apps/){:target="_blank"}, select the App and go to the *Settings > Keys & IDs*. Copy the **App ID** and the **API key**.
+1. Enter the OneSignal **App ID** and the **API key** in the OneSignal destination settings in Segment and Enable the destination.
+1. Make sure you have an **[External User ID](https://documentation.onesignal.com/docs/onboarding-with-onesignal#step-3-connect-user-data-to-onesignal)** set for your users on OneSignal. OneSignal uses the **External User ID** to match with the **User ID** value from Segment.
+
+> info ""
+> The OneSignal Destination is available customers on OneSignal Professional and Enterprise plans.
+
+## Supported methods
+
+OneSignal supports the following methods, as specified in the [Segment Spec](/docs/connections/spec).
+
+### Identify
+
+Send [Identify](/docs/connections/spec/identify) calls to OneSignal. For example:
+
+```js
+analytics.identify('userId123', {
+  firstName: 'John',
+  lastName: 'Doe',
+     country: ‘USA’
+});
+```
+
+These user traits appear as player [data tags](https://documentation.onesignal.com/docs/add-user-data-tags) in OneSignal.
+
+> warning ""
+> OneSignal does not accept nested objects or arrays as user properties.
+
+![](images/demo.png)
+
+
+
+### Track
+
+Send [Track](/docs/connections/spec/track) calls to OneSignal. For example:
+
+```js
+analytics.track('Add to Cart', {
+    product_name: 'Sunglasses',
+    brand: 'Rayban'
+})
+```
+
+For events and associated properties sent using a Track call, OneSignal drops the event name (for example, `Add to Cart`), but stores all the properties (for example, `productname` and `brand`) as data tags.
+
+![](images/track-example.png)
+
+
+
+
+## Personas
+
+You can send computed traits and audiences generated using [Segment Personas](/docs/personas) to OneSignal. To learn more about Personas, contact Segment for a [demo](https://segment.com/contact/demo){:target="_blank"}.
+
+### Audiences
+
+Persona Audiences appear as a [segment](https://documentation.onesignal.com/docs/segmentation) in OneSignal.
+
+Audiences sent as part of a Track call create a OneSignal [segment](https://documentation.onesignal.com/docs/segmentation) with the Audience Name.
+
+Audiences sent as part of an Identify call:
+
+- Create a OneSignal segment with the Audience Name
+- add data tags (if there are additional properties in the Identify call) on all the matching user records.
+
+![](images/audiences.jpg)
+
+The Identify and Track calls are sent to OneSignal whenever a user enters or exits the Audience.
+
+### Computed Traits
+
+Personas Computed Traits are stored as [Data Tags](https://documentation.onesignal.com/docs/add-user-data-tags) on the OneSignal user (player) records whether passed to OneSignal as an Identify call or a Track call. You can then use these data tags to manually create OneSignal segments and automate your messaging workflows.
+
+# OneSignal Destination FAQ
+
+**Managing Segment’s Reserved and Custom Traits**
+
+* All Segment user traits are sent to OneSignal as data tags. The number of data tags allowed on OneSignal depends on your OneSignal pricing plan. Tags over the entitled number will be dropped.
+
+* OneSignal always updates the `firstName` and the `lastName` properties for matching users. All other traits are added/updated on a firstcome basis. `firstName` and `lastName` tags are stored as `first_name` and `last_name`.
+
+* User properties sent to OneSignal with blank/null values are removed from the OneSignal user record. This is done to make sure you are within your data tag limits.
+
+* OneSignal doesn’t store email and phone properties as these key identifiers are stored as separate player records in OneSignal. To update user traits for these records in OneSignal
+  * Create a player record with the email address and/or a phone number and map those records with the External_User_ID.
+  * Additional properties sent from Segment are mapped across all your matching records, including email and phone number records.
diff --git a/src/connections/destinations/catalog/onesignal/index.md b/src/connections/destinations/catalog/onesignal/index.md
@@ -1,5 +1,6 @@
 ---
 title: OneSignal Destination
+hidden: true
 ---
 
 ## Getting Started
diff --git a/src/connections/destinations/catalog/variance/index.md b/src/connections/destinations/catalog/variance/index.md
@@ -0,0 +1,97 @@
+---
+rewrite: true
+title: Variance Destination
+---
+
+[Variance](https://variance.com?utm_source=segmentio&utm_medium=docs&utm_campaign=partners) hooks into your customer data and makes it easy to access growth signals across product, marketing, and sales. The platform provides your growth team with clear, intent-based signals, from all stages of a customer's journey.
+
+This destination is maintained by Variance. For any issues with the destination, [contact the Variance Support team](mailto:support@variance.com).
+
+## Getting Started
+
+
+1. From the Destinations catalog page in the Segment App, click **Add Destination**.
+2. Search for "Variance" in the Destinations Catalog, and select the Variance destination.
+3. Choose which Source should send data to the Variance destination.
+4. Go to the [Variance Integrations page](http://app.variance.com/integrations){:target="_blank"} (you'll see it in the main nav under your company name).
+5. Click **Add Connection**.
+6. Give your connection a name. If you use a descriptive name, you can use it as an event filter in Variance.
+7. Choose a method for account matching. You can read more about these methods in the [Account Mapping](#account-mapping) section.
+8. Find and copy the "Secret" and "Webhook URL" fields associated with this project.
+9. Back in the Segment App, enter these as the **API Key** and **Webhook URL** fields in the Variance Destination settings.
+
+## Supported methods
+
+Variance supports the following methods of the [Segment Spec](/docs/connections/spec).
+
+### Page
+
+If you aren't familiar with the Segment Spec, take a look at the [Page method documentation](https://segment.com/docs/connections/spec/page/) to learn about what it does. An example call would look like:
+
+```js
+analytics.page()
+```
+
+Segment sends Page calls to Variance as a `Page` Event Type. 
+
+
+### Screen
+
+If you aren't familiar with the Segment Spec, take a look at the [Screen method documentation](https://segment.com/docs/connections/spec/screen/) to learn about what it does. An example call would look like:
+
+```obj-c
+[[SEGAnalytics sharedAnalytics] screen:@"Home"];
+```
+
+Segment sends Screen calls to Variance as a `Screen` Event Type. 
+
+
+### Identify
+
+If you aren't familiar with the Segment Spec, take a look at the [Identify method documentation](https://segment.com/docs/connections/spec/identify/) to learn about what it does. An example call would look like:
+
+```js
+analytics.identify('userId123', {
+  email: 'john.doe@example.com'
+});
+```
+
+Segment sends Identify calls to Variance as a `Contact`.
+
+
+### Track
+
+If you aren't familiar with the Segment Spec, take a look at the [Track method documentation](https://segment.com/docs/connections/spec/track/) to learn about what it does. An example call would look like:
+
+```js
+analytics.track('Login Button Clicked')
+```
+
+Segment sends Track calls to Variance as an `Action` Event Type.
+
+
+### Group
+
+Send [Group](/docs/connections/spec/group) to Variance. For example:
+
+```js
+analytics.group("groupId123", {
+  name: "Initech",
+  industry: "Technology",
+  employees: 329,
+  plan: "enterprise"
+});
+```
+
+Segment sends Group calls to Variance as an `Account` if you've chosen the "Group" Account Mapping method during setup.
+
+## Account Mapping
+
+Variance offers several ways to map your users to accounts or companies, including the following.
+
+ - **Group**: If you already use the Group call to indicate the Account, then you don’t need to change anything as Segment automatically extracts the Account name.
+ - **Identify with custom traits** (for example `company.id` and `company.name`): Choose this option if you include some information about the Account, Company, or Organization as a trait in each Identify call. When you choose this option, add the name of the trait you use. For example, if you configure the call with the data `{'company':{'id':1,'name':'Awesome Inc.'}}`, add `company.id` as the Account ID trait and `company.name` as the Account Name trait.
+ - **Identify email trait domain extraction** (Fallback option): If you don't use the methods above, Segment extracts the domain name from the `email` trait, and uses that value as the Account name.
+
+> info ""
+> If none of these methods work for your configuration, [contact Variance support](mailto:support@variance.com) to discuss alternatives.
diff --git a/src/connections/storage/warehouses/choose-warehouse.md b/src/connections/storage/warehouses/choose-warehouse.md
@@ -7,36 +7,36 @@ redirect_from: '/connections/warehouses/choose-warehouse/'
 
 In most cases, you will get a much better price-to-performance ratio with Redshift for typical analyses.
 
-Redshift lacks some [features](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-features.html), [datatypes](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-datatypes.html), and [functions](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-functions.html) supported by Postgres and also implements [some features](http://docs.aws.amazon.com/redshift/latest/dg/c_redshift-sql-implementated-differently.html) differently. If you absolutely need any of the features or functions missing in Redshift and BigQuery, choose Postgres. If not (or you're not sure), we recommend Redshift.
+Redshift lacks some [features](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-features.html), [datatypes](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-datatypes.html), and [functions](http://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-functions.html) supported by Postgres and also implements [some features](http://docs.aws.amazon.com/redshift/latest/dg/c_redshift-sql-implementated-differently.html) differently. If you need any of the features or functions missing in Redshift and BigQuery, choose Postgres. If not (or you're not sure), Segment recommends choosing Redshift.
 
 If you'd like more information, Amazon wrote [about this in their documentation](http://docs.aws.amazon.com/redshift/latest/dg/c_redshift-and-postgres-sql.html).
 
 ## Comparing Redshift and BigQuery
 
-Both Redshift and BigQuery are attractive cloud-hosted, relatively cheap, and performant analytical databases. The differences between the two are largely around their architecture and pricing.
+Both Redshift and BigQuery are attractive cloud-hosted, affordable, and performant analytical databases. The differences between the two are around their architecture and pricing.
 
 ## Architecture
 
-When you provision a Redshift cluster, you're renting a server from Amazon Web Services. Your cluster is comprised of [nodes](http://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html), each with dedicated memory, CPU, and disk storage. These nodes handle data storage, query execution, and - if your cluster contains multiple nodes - a leader node will handle coordination across the cluster.
+When you provision a Redshift cluster, you're renting a server from Amazon Web Services. Your cluster consists of [nodes](http://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html), each with dedicated memory, CPU, and disk storage. These nodes handle data storage, query execution, and - if your cluster contains multiple nodes - a leader node will handle coordination across the cluster.
 
 Redshift performance and storage capacity is a function of cluster size and cluster type. As your storage or performance requirements change, you can scale up or down your cluster as needed.
 
 With BigQuery, you're not constrained by the storage capacity or compute resources of a given cluster. Instead, you can load large amounts of data into BigQuery without running out of memory, and execute complex queries without maxing out CPU.
 
-This is possible because BigQuery takes advantage of distributed storage and networking to separate data storage from compute power. Data is distributed across many servers in the Google cloud using their [Colossus distributed file system](https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood). When you execute a query, the [Dremel query engine](https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood) splits the query into smaller sub-tasks, distributes the sub-tasks to many computers across Google data centers, and then re-assembles them into your results.
+This is possible because BigQuery takes advantage of distributed storage and networking to separate data storage from compute power. Google's[Colossus distributed file system](https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood) distributes data across many servers in the Google cloud. When you execute a query, the [Dremel query engine](https://cloud.google.com/blog/big-data/2016/01/bigquery-under-the-hood) splits the query into smaller sub-tasks, distributes the sub-tasks to computers across Google data centers, and then re-assembles them into your results.
 
 ## Pricing
 
 The difference in architecture translates into differences in pricing.
 
-[Redshift prices](https://aws.amazon.com/redshift/pricing/) based on an hourly rate determined by the number and types of nodes in your cluster. They offer dense storage - optimized for storage - and dense compute nodes - optimized for query performance.
+[Redshift prices](https://aws.amazon.com/redshift/pricing/) are based on an hourly rate determined by the number and types of nodes in your cluster. They offer dense storage - optimized for storage - and dense compute nodes - optimized for query performance.
 
-BigQuery has two [pricing options](https://cloud.google.com/bigquery/pricing): variable and fixed pricing. With the variable, pay-as-you-go plan, you pay for the data you load into BigQuery, and then pay only for the amount of data you query. BigQuery allows you to set up [Cost Controls and Alerts](https://cloud.google.com/bigquery/cost-controls) to help control and monitor costs.
+BigQuery has two [pricing options](https://cloud.google.com/bigquery/pricing): variable and fixed pricing. With the variable, pay-as-you-go plan, you pay for the data you load into BigQuery, and then pay for the amount of data you query. BigQuery allows you to set up [Cost Controls and Alerts](https://cloud.google.com/bigquery/cost-controls) to help control and monitor costs.
 
-Fixed-price plans are geared toward high-volume customers and allow you to rent a fixed amount of compute power.
+Fixed-price plans are more for high-volume customers and allow you to rent a fixed amount of compute power.
 
 ## Resource Management
 
-Redshift does require you to create a cluster, schedule vacuums, choose sort and distribution keys, and resize your cluster as storage and performance needs change over time.
+Redshift does require you to create a cluster, choose sort and distribution keys, and resize your cluster as storage and performance needs change over time.
 
-BigQuery is "fully-managed", which means that you'll never have to resize, vacuum, or adjust distribution or sort keys. All of that is handled by BigQuery.
+BigQuery is "fully-managed", which means that you'll never have to resize or adjust distribution or sort keys. BigQuery handles all of that.
diff --git a/src/connections/storage/warehouses/redshift-tuning.md b/src/connections/storage/warehouses/redshift-tuning.md

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-# AUTOGENERATED FROM PUBLIC API. DO NOT EDIT`
	`1`	`+# AUTOGENERATED FROM PLATFORM API. DO NOT EDIT`
`2`	`2`	`# destination data last updated 2021-07-01`
`3`	`3`	`items:`
`4`	`4`	`- display_name: ActiveCampaign`