SlicingDice Destination


SlicingDice is an all-in-one data warehouse. It’s a fully managed cloud data warehouse with optional built-in tools for data integration, exploration, visualization and machine learning.

This destination is maintained by SlicingDice. For any issues with the destination, contact their team.

NOTE: The SlicingDice Destination is currently in beta, which means that they are still actively developing the destination. This doc was last updated on May 8, 2019. If you are interested in joining their beta program or have any feedback to help improve the SlicingDice Destination and its documentation, let their team know!

Getting Started

Before you start, make sure SlicingDice supports the source type and connection mode you’ve chosen to implement. You can learn more about connection modes here.

Web Mobile Server
📱 Device-mode
☁️ Cloud-mode
  1. Login into your SlicingDice’s Control Panel account to create a connection between SlicingDice and Segment.
  2. Follow the Connecting to Segment guide available in SlicingDice documentation to create and allow this connection.

Page

If you’re not familiar with the Segment Specs, take a look to understand what the Page method does. An example call would look like:

analytics.page()

Page calls will save your data in SlicingDice using the following columns:

  • {source_name}-pages-page-event: contains the name attribute of the call
  • {source_name}-pages-{attribute_name}: contains the properties attributes of the call, saving each one as an individual column
  • {source_name}-pages-context-{attribute_name}: contains the context attributes of the call, saving each one as an individual column

Screen

If you’re not familiar with the Segment Specs, take a look to understand what the Screen method does. An example call would look like:

[[SEGAnalytics sharedAnalytics] screen:@"Home"];

Screen calls will be sent to SlicingDice using the following columns:

  • {source_name}-screens-screen-event: contains the name attribute of a Screen call
  • {source_name}-screens-{attribute_name}: contains the properties attributes of a Screen call, saving each one as an individual column
  • {source_name}-screens-context-{attribute_name}: contains the context attributes of a Screen call, saving each one as an individual column

Identify

If you’re not familiar with the Segment Specs, take a look to understand what the Identify method does. An example call would look like:

analytics.identify('userId123', {
  email: 'john.doe@example.com'
});

Identify calls will be sent to SlicingDice using the following columns:

  • {source_name}-identifies-user-id: contains the “userId” attribute of an Identify call
  • {source_name}-identifies-{attribute_name}: contains the “traits” attributes of an Identify call, saving each one as an individual column
  • {source_name}-identifies-context-{attribute_name}: contains the “context” attributes of an Identify call, saving each one as an individual column

Track

If you’re not familiar with the Segment Specs, take a look to understand what the Track method does. An example call would look like:

analytics.track('Clicked Login Button')

Track calls will be sent to SlicingDice using the following columns:

  • {source_name}-track-event: contains the “event” attribute of a Track call
  • {source_name}-track-{attribute_name}: contains the “properties” attributes of a Track call, saving each one as an individual column
  • {source_name}-track-context-{attribute_name}: contains the “context” attributes of a Track call, saving each one as an individual column

Observation: if your context attribute has nested attributes, your column names will follow this pattern: {source_name}-track-context-{attribute_name}{nested_attribute_name}

Groups

If you’re not familiar with the Segment Specs, take a look to understand what the Group method does. An example call would look like:

analytics.group("0e8c78ea9d97a7b8185e8632", {
  name: "Initech",
  industry: "Technology",
  employees: 329,
  plan: "enterprise",
  "total billed": 830
});

Group calls will be sent to SlicingDice using the following columns:

  • {source_name}-groups-group-id: contains the groupID attribute of a Group call
  • {source_name}-groups-{attribute_name}: contains the traits attributes of a Group call, saving each one as an individual column
  • {source_name}-groups-context-{attribute_name}: contains the “context” attributes of a Group call, saving each one as an individual column

    Observation: Group calls will also be sent to SlicingDice using the following Account columns that holds the latest state of a group.

    • {source_name}-accounts-group-id: contains the groupId attribute of a Group call
    • {source_name}-accounts-{attribute_name}: contains the traits attributes of a Track call, saving each one as an individual column
    • {source_name}-accounts-context-{attribute_name}: contains the context attributes of a Track call, saving each one as an individual column

Personas

You can send computed traits and audiences generated using Segment Personas to this destination as a user property. To learn more about Personas, contact us for a demo.

For user-property destinations, an identify call is sent to the destination for each user being added and removed. The property name is the snake_cased version of the audience name, with a true/false value to indicade membership. For example, when a user first completes an order in the last 30 days, Personas sends an Identify call with the property order_completed_last_30days: true. When the user no longer satisfies this condition (for example, it’s been more than 30 days since their last order), Personas sets that value to false.

When you first create an audience, Personas sends an Identify call for every user in that audience. Later audience syncs only send updates for users whose membership has changed since the last sync.

Settings

Segment lets you change these destination settings from the Segment app without having to touch any code.

API Key

Access the list of databases on SlicingDice’s control panel and click on “Show Key” button for the corresponding database.

This page was last modified: 09 Jun 2020

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