Update sl-partner-integration-guide.md (#3986)

Making a few more recommendations based on our API functionality

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website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md

@@ -50,7 +50,7 @@ When working with more governed data, it's essential to establish clear guardrai
 
 - **Aggregations control** — Users shouldn't generally be allowed to modify aggregations unless they perform post-processing calculations on Semantic Layer data (such as year-over-year analysis).
 
-- **Time series alignment and using metric_time** &mdash; Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes.<br /><br />When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). <br /><br /> As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the other time dimensions is clarifying so that people do not confuse how metrics should be plotted. <br /><br /> Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
+- **Time series alignment and using metric_time** &mdash; Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes.<br /><br />When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). <br /><br /> As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the other time dimensions is clarifying so that people do not confuse how metrics should be plotted. <br /><br /> Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Related to this, we also strongly recommend that you have a way to expose what dimension `metric_time` actually maps to for users who may not be familiar. Our APIs allow you to fetch the actual underlying time dimensions that makeup metric_time (such as `transaction_date`) so you can expose them to your users.
 
 - **Units consistency** &mdash; If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
 
@@ -90,7 +90,7 @@ Allow users to query either one metric alone without dimensions or multiple metr
 - Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
   * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity isn't available to the user. Our APIs have functionality that will help you surface the correct granularities
 
-- We recommend that time granularity is treated as a general time dimension-specific concept and that it can be applied to more than just the primary aggregation (or `metric_time`). Consider a situation where a user wants to look at `sales` over time by `customer signup month`; in this situation, having the ability to apply granularities to both time dimensions is crucial. Note: Initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as your solution evolves. 
+- We recommend that time granularity is treated as a general time dimension-specific concept and that it can be applied to more than just the primary aggregation (or `metric_time`). Consider a situation where a user wants to look at `sales` over time by `customer signup month`; in this situation, having the ability to apply granularities to both time dimensions is crucial. Our APIs include information to fetch the granularities for the primary (metric_time) dimensions, as well as all time dimensions. You can treat each time dimension and granularity selection independently in your application. Note: Initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as your solution evolves. 
 
 - You should allow users to filter on date ranges and expose a calendar and nice presets for filtering these. 
   * For example, last 30 days, last week, and so on.