51ºÚÁϲ»´òìÈ

1. In the Data pane:

   1. Select daterange.
   2. Select product_name.
   3. Select ∑ occurrences.

2. In the Filters pane:

   1. Select daterange is (All) from Filters on this visual.
   2. Select Relative date as the Filter type.
   3. Define the filter to Show items when the value is in the last 1 calendar years.
   4. Select Apply filter.
   5. Select product_name is (All) from Filters on this visual.
   6. Select Top N as the Filter type.
   7. Select Show Items Top 5 By value.
   8. Drag and drop ∑ occurrences from the Data pane and drop it on Add data fields here.
   9. Select Apply filter.

3. In the Visualization pane:

   • Select to remove daterange from Columns.

   Your Power BI Desktop should look like below.

   {modal="regular"}

The query executed by Power BI Desktop using the BI extension is including a limit statement but not the one expected. The limit to the top 5 occurrences is enforced by Power BI Desktop using explicit product name results.

select "_"."product_name",
    "_"."a0"
from
(
    select "rows"."product_name" as "product_name",
        sum("rows"."occurrences") as "a0"
    from
    (
        select "_"."daterangeName",
            "_"."daterange",
            "_"."filterId",
            "_"."filterName",
            "_"."timestamp",
            "_"."affiliate_name",
            "_"."affiliate_url",
            "_"."commerce.order.priceTotal",
            "_"."customer_city",
            "_"."customer_region",
            "_"."daterangeday",
            "_"."daterangefifteenminute",
            "_"."daterangefiveminute",
            "_"."daterangehour",
            "_"."daterangeminute",
            "_"."daterangemonth",
            "_"."daterangequarter",
            "_"."daterangesecond",
            "_"."daterangethirtyminute",
            "_"."daterangeweek",
            "_"."daterangeyear",
            "_"."hitdatetime",
            "_"."page_name",
            "_"."page_url",
            "_"."product_category",
            "_"."product_name",
            "_"."product_short_review",
            "_"."product_subCategory",
            "_"."referrer_url",
            "_"."search_engine",
            "_"."search_keywords",
            "_"."store_city",
            "_"."store_name",
            "_"."store_region",
            "_"."store_type",
            "_"."timepartdayofmonth",
            "_"."timepartdayofweek",
            "_"."timepartdayofyear",
            "_"."timeparthourofday",
            "_"."timepartminuteofhour",
            "_"."timepartmonthofyear",
            "_"."timepartquarterofyear",
            "_"."timepartweekofyear",
            "_"."cm_session_end_rate_defaultmetric",
            "_"."cm_session_person_defaultmetric",
            "_"."cm_session_start_rate_defaultmetric",
            "_"."cm_timespent_person_defaultmetric",
            "_"."cm_timespent_session_defaultmetric",
            "_"."cm_product_name_count_distinct",
            "_"."ad_views",
            "_"."adobe_sessionends",
            "_"."adobe_sessionstarts",
            "_"."adobe_timespent",
            "_"."exchange_buybacks",
            "_"."exchange_cost",
            "_"."exchange_purchases",
            "_"."exchange_revenue",
            "_"."occurrences",
            "_"."page_views",
            "_"."product_quantity",
            "_"."product_reviews",
            "_"."product_views",
            "_"."purchase_revenue",
            "_"."purchases",
            "_"."visitors",
            "_"."visits"
        from "public"."cc_data_view" "_"
        where (("_"."product_name" in ('Saltwater Monofilament Line', 'Pop-Up Beach Tent', 'Instant Pop-Up Tent', 'Envelop Sleeping Bag', 'Waterproof Tackle Bag')) and "_"."daterange" < date '2024-01-01') and "_"."daterange" >= date '2023-01-01'
    ) "rows"
    group by "product_name"
) "_"
where not "_"."a0" is null
limit 1000001

1. Select the Sheet 1 tab at the bottom to switch from Data source. In the Sheet 1 view:

   1. Drag Daterange entry from the Tables list in the Filters shelf.

   2. In the Filter Field [Daterange] dialog, select Range of Dates and select Next >.

   3. In the Filter [Daterange] dialog, select Relative dates, select Years, and select Previous years. Select Apply and OK.

   4. Drag Product Name from the Tables list to Rows.

   5. Drag Occurrences entry from the Tables list and drop the entry in the field next to Columns. The value changes to SUM(Occurrences).

   6. Select Text Table from Show Me.

   7. Select Fit Width from the Fit dropdown menu.

   8. Select Product Name in Rows. Select Filter from the dropdown menu.

      1. In the Filter [Product Name] dialog, select the Top tab.

      2. Select By field: Top 5 by Occurrences Sum.

      3. Select Apply and OK.

         You notice that the table disappears. Selecting the top 5 product names by occurrences does not work properly using this filter.

      4. Select the Product Name in the Filter shelf and from the dropdown menu select Remove. The table reappears.

   9. Select SUM(Occurrences) in the Marks shelf. Select Filter from the dropdown menu.

      1. In the Filter [Occurrences] dialog, select At least.

      2. Enter 47.799 as the value. This value ensures that only the top 5 items are shown in the table. Select Apply and OK.

         Your Tableau Desktop should look like below.

         {modal="regular"}

As shown above, this query executed by Tableau Desktop, when defining a Top 5 occurrences filter on product names, fails.

SELECT CAST("cc_data_view"."product_name" AS TEXT) AS "product_name",
  SUM("cc_data_view"."occurrences") AS "sum:occurrences:ok"
FROM "public"."cc_data_view" "cc_data_view"
  INNER JOIN (
  SELECT CAST("cc_data_view"."product_name" AS TEXT) AS "product_name",
    SUM("cc_data_view"."occurrences") AS "$__alias__0"
  FROM "public"."cc_data_view" "cc_data_view"
  GROUP BY 1
  ORDER BY 2 DESC,
    1 ASC
  LIMIT 5
) "t0" ON (CAST("cc_data_view"."product_name" AS TEXT) = "t0"."product_name")
WHERE (("cc_data_view"."daterange" >= (TIMESTAMP '2023-01-01 00:00:00.000')) AND ("cc_data_view"."daterange" < (TIMESTAMP '2024-01-01 00:00:00.000')))
GROUP BY 1

The query executed by Tableau Desktop, when defining a Top 5 filter on occurrences, is shown below. The limit is not visible in the query and applied client side.

SELECT CAST("cc_data_view"."product_name" AS TEXT) AS "product_name",
  SUM("cc_data_view"."occurrences") AS "sum:occurrences:ok"
FROM "public"."cc_data_view" "cc_data_view"
WHERE (("cc_data_view"."daterange" >= (TIMESTAMP '2023-01-01 00:00:00.000')) AND ("cc_data_view"."daterange" < (TIMESTAMP '2024-01-01 00:00:00.000')))
GROUP BY 1

1. In the Explore interface of Looker, refresh your connection. Select Clear cache and refresh.

2. In the Explore interface of Looker, ensure you do have a clean setup. If not, select Remove fields and filters.

3. Select + Filter underneath Filters.

4. In the Add Filter dialog:

   1. Select ‣ Cc Data View

   2. From the list of fields, select ‣ Daterange Date then Daterange Date.

      {modal="regular"}

5. Specify the Cc Data View Daterange Date filter as is in range 2023/01/01 until (before) 2024/01/01.

6. From the ‣ Cc Data View section in the left rail:

   1. Select Product Name.
   2. Select Count underneath MEASURES in the left rail (at the bottom).

7. Ensure you select ↓ (Descending, Sort Order: 1) on the Purchase Revenue column.

8. Ensure you select ↓ (Descending, Sort Order: 1) on the Purchase Revenue column.

9. Select Run.

10. Select ‣ Visualization.

You should see a visualization and table similar as shown below.

{modal="regular"}

The query generated by Looker using the BI extension is including FETCH NEXT 5 ROWS ONLY, which implies that the limit is executed through Looker and the BI extension.

-- Looker Query Context '{"user_id":6,"history_slug":"a8f3b1ebd5712413ca1ae695090f70db","instance_slug":"71d4667f0b76c0011463658f45c3f7a3"}'
SELECT
    cc_data_view."product_name"  AS "cc_data_view.product_name",
    COUNT(*) AS "cc_data_view.count"
FROM
    "public"."cc_data_view" AS "cc_data_view"
WHERE ((( cc_data_view."daterange"  ) >= (DATE_TRUNC('day', DATE '2023-01-31')) AND ( cc_data_view."daterange"  ) < (DATE_TRUNC('day', DATE '2024-01-01'))))
GROUP BY
    1
ORDER BY
    2 DESC
FETCH NEXT 5 ROWS ONLY

1. Enter the following statements in a new cell.

   code language-python
   data = %sql SELECT product_name AS `Product Name`, COUNT(*) AS Events \ FROM cc_data_view \ WHERE daterange BETWEEN '2023-01-01' AND '2023-02-01' \ GROUP BY 1 \ ORDER BY `Events` DESC \ LIMIT 5; display(data)

2. Execute the cell. You should see output similar to the screenshot below.

   

The query is excuted by the BI extension as defined in Jupyter Notebook.

1. Enter the following statements between ```{r} and ``` in a new chunk.

   code language-r
   ## Dimension 1 Limited df <- dv %>% filter(daterange >= "2023-01-01" & daterange < "2024-01-01") %>% group_by(product_name) %>% count() %>% arrange(desc(n), .by_group = FALSE) %>% head(5) print(df)

2. Run the chunk. You should see output similar to the screenshot below.

   

The query generated by RStudio using the BI extension is including LIMIT 5, which implies that the limit is applied through RStudio and the BI extension.

SELECT "product_name", COUNT(*) AS "n"
FROM (
  SELECT "cc_data_view".*
  FROM "cc_data_view"
  WHERE ("daterange" >= '2023-01-01' AND "daterange" < '2024-01-01')
) AS "q01"
GROUP BY "product_name"
ORDER BY "n" DESC
LIMIT 5

The Customer Journey Analytics objects are available in the Data pane and are retrieved from the table you have selected in Power BI Desktop. For example, public.cc_data_view. The name of the table is the same as the External ID that you have defined for your data view in Customer Journey Analytics. For example, data view with Title C&C - Data View and External ID cc_data_view.

Dimensions
Dimensions from Customer Journey Analytics are identified by the Component ID. The Component ID is defined in your Customer Journey Analytics data view. For example, dimension Product Name in Customer Journey Analytics has a Component ID product_name, which is the name for the dimension in Power BI Desktop.
Date range dimensions from Customer Journey Analytics, like Day, Week, Month, and more are available as daterangeday, daterangeweek, daterangemonth, and more.

Metrics
Metrics from Customer Journey Analytics are identified by the Component ID. The Component ID is defined in your Customer Journey Analytics data view. For example, metric Purchase Revenue in Customer Journey Analytics has a Component ID purchase_revenue, which is the name for the metric in Power BI Desktop. A ∑ indicates metrics. When you use a metric in any visualization, the metric is renamed to Sum of metric.

Filters
Filters that you define in Customer Journey Analytics are available as part of the filterName field. When you use a filterName field in Power BI Desktop, you can specify which filter to use.

Calculated metrics
Calculated metrics that you define in Customer Journey Analytics are identified by the External ID you have defined for the calculated metric. For example, calculated metric Product Name (Count Distinct) has External ID product_name_count_distinct and is shown as cm_product_name_count_distinc t in Power BI Desktop.

Date ranges
Date ranges that you define in Customer Journey Analytics are available as part of the daterangeName field. When you use a daterangeName field, you can specify which date range to use.

Custom transformations
Power BI Desktop provides custom transformation functionality using . As an example, you want to execute the Single dimension ranked use case with product names in lower case.

1. In the report view, select the bar visualization.

2. Select product_name in the Data pane.

3. Select New column in the toolbar.

4. In the formula editor, define a new column named product_name_lower, like product_name_lower = LOWER('public.cc_data_view[product_name]).
   {modal="regular"}

5. Ensure you select the new product_name_lower column in the Data pane instead of the product_name column.

6. Select Report as Table from in the table visualization.

   Your Power BI Desktop should look like below.
   {modal="regular"}

The custom transformation result in an update to SQL queries. See the use of the lower function in the SQL example below:

select "_"."product_name_lower",
    "_"."a0",
    "_"."a1"
from
(
    select "rows"."product_name_lower" as "product_name_lower",
        sum("rows"."purchases") as "a0",
        sum("rows"."purchase_revenue") as "a1"
    from
    (
        select "_"."daterange" as "daterange",
            "_"."product_name" as "product_name",
            "_"."purchase_revenue" as "purchase_revenue",
            "_"."purchases" as "purchases",
            lower("_"."product_name") as "product_name_lower"
        from
        (
            select "_"."daterange",
                "_"."product_name",
                "_"."purchase_revenue",
                "_"."purchases"
            from
            (
                select "daterange",
                    "product_name",
                    "purchase_revenue",
                    "purchases"
                from "public"."cc_data_view" "$Table"
            ) "_"
            where ("_"."daterange" < date '2024-01-01' and "_"."daterange" >= date '2023-01-01') and ("_"."product_name" in ('4G Cellular Trail Camera', '4K Wildlife Trail Camera', 'Wireless Trail Camera', '8-Person Cabin Tent', '20MP No-Glow Trail Camera', 'HD Wildlife Camera', '4-Season Mountaineering Tent', 'Trail Camera', '16MP Trail Camera with Solar Panel', '10-Person Family Tent'))
        ) "_"
    ) "rows"
    group by "product_name_lower"
) "_"
where not "_"."a0" is null or not "_"."a1" is null
limit 1000001

The Customer Journey Analytics objects are available in the Data side bar whenever you work in a sheet. And are retrieved from the table that you have selected as part of the Data source page in Tableau. For example, cc_data_view. The name of the table is the same as the External ID that you have defined for your data view in Customer Journey Analytics. For example, data view with Title C&C - Data View and External ID cc_data_view.

Dimensions
Dimensions from Customer Journey Analytics are identified by the Component name. The Component name is defined in your Customer Journey Analytics data view. For example, dimension Product Name in Customer Journey Analytics has a Component name Product Name, which is the name for the dimension in Tableau. All dimensions are identified by Abc.
Date range dimensions from Customer Journey Analytics, like Day, Week, Month, and more are available as Daterangeday, Daterangeweek, Daterangemonth, and more. When you use a date range dimension, you have to select an appropriate definition of date or time to apply to that date range dimension from the dropdown menu. For example, Year, Quarter, Month, Day.

Metrics
Metrics from Customer Journey Analytics are identified by the Component Name. The Component Name is defined in your Customer Journey Analytics data view. For example, metric Purchase Revenue in Customer Journey Analytics has a Component Name Purchase Revenue, which is the name for the metric in Tableau. All metrics are identified by #. When you use a metric in any visualization, the metric is renamed to Sum(metric).

Filters
Filters that you define in Customer Journey Analytics are available as part of the Filter Name field. When you use a Filter Name field in Tableau, you can specify which filter to use.

Calculated metrics
Calculated metrics that you define in Customer Journey Analytics are identified by the Title you have defined for the calculated metric. For example, calculated metric Product Name (Count Distinct) has Title Product Name (Count Distinct) and is shown as Cm Product Name Count Distinct in Tableau.

Date ranges
Date ranges that you define in Customer Journey Analytics are available as part of the Daterange Name field. When you use a Daterange Name field, you can specify which date range to use.

Custom transformations
Tableau Desktop provides custom transformation functionality using . As an example, you want to execute the Single dimension ranked use case with product names in lower case.

1. Select Analysis > Create Calculated Field from the main menu.

   1. Define Lowercase Product Name using the function LOWER([Product Name]).

      {modal="regular"}

   2. Select OK.

2. Select the Data sheet.

   1. Drag Lowercase Product Name from Tables and drop the entry in the field next to Rows.
   2. Remove Product Name from Rows.

3. Select Dashboard 1 view.

Your Tableau Desktop should look like below.

{modal="regular"}

The custom transformation result in an updates to SQL queries. See the use of the LOWER function in the SQL example below:

SELECT LOWER(CAST(CAST("cc_data_view"."product_name" AS TEXT) AS TEXT)) AS "Calculation_1562467608097775616",
  SUM("cc_data_view"."purchase_revenue") AS "sum:purchase_revenue:ok",
  SUM("cc_data_view"."purchases") AS "sum:purchases:ok"
FROM "public"."cc_data_view" "cc_data_view"
WHERE (("cc_data_view"."daterange" >= (DATE '2023-01-01')) AND ("cc_data_view"."daterange" <= (DATE '2023-12-31')))
GROUP BY 1
HAVING ((SUM("cc_data_view"."purchase_revenue") >= 999999.99999998999) AND (SUM("cc_data_view"."purchase_revenue") <= 2000000.00000002))

The Customer Journey Analytics objects are available in the Explore interface. And are retrieved as part of setting up your connection, project, and model in Looker. For example, cc_data_view. The name of the view is the same as the External ID that you have defined for your data view in Customer Journey Analytics. For example, data view with Title C&C - Data View and External ID cc_data_view.

Dimensions
Dimensions from Customer Journey Analytics are listed as DIMENSION in the Cc Data View left rail. The dimension is defined in your Customer Journey Analytics data view. For example, dimension Product Name in Customer Journey Analytics has a DIMENSION Product Name, which is the name for the dimension in Looker.
Date range dimensions from Customer Journey Analytics, like Day, Week, Month, and more are available as Daterangeday Date, Daterangeweek Date, Daterangemonth Date, and more. When you use a date range dimension, you have to select an appropriate definition of date or time. For example, Year, Quarter, Month, Date.

Metrics
Metrics from Customer Journey Analytics are listed as DIMENSION in in the Cc Data View left rail. For example, metric Purchase Revenue in Customer Journey Analytics has a DIMENSION Purchase Revenue. To actually use as a metric, create a custom measure field as shown in the examples above, or use the shortcut on a dimension. For example, ⋮, select Aggregate, and then select Sum…

Filters
Filters that you define in Customer Journey Analytics are available as part of the Filter Name field. When you use a Filter Name field in Looker, you can specify which filter to use.

Calculated metrics
Calculated metrics that you define in Customer Journey Analytics are identified by the Title you have defined for the calculated metric. For example, calculated metric Product Name (Count Distinct) has Title Product Name (Count Distinct) and is shown as Cm Product Name Count Distinct in Looker.

Date ranges
Date ranges that you define in Customer Journey Analytics are available as part of the Daterange Name field. When you use a Daterange Name field, you can specify which date range to use.

Custom transformations
Looker provides custom transformation functionality using custom field builders, as shown above. As an example, you want to execute the Single dimension ranked use case with product names in lower case.

1. From the ‣ Custom Fields section in the left rail:

   1. Select Custom Dimension from the + Add dropdown menu.

   2. Enter lower(${cc_data_view.product_name}) in the Expression text area. You are assisted with the correct syntax when you start to type Product Name.

      {modal="regular"}

   3. Enter product name as the Name.

   4. Select Save.

You should see a similar table as shown below.

{modal="regular"}

The custom transformation result in an updates to SQL queries. See the use of the LOWER function in the SQL example below:

SELECT
    LOWER((cc_data_view."product_name")) AS "product_name",
    COALESCE(SUM(CAST(( cc_data_view."purchase_revenue"  ) AS DOUBLE PRECISION)), 0) AS "sum_of_purchase_revenue",
    COALESCE(SUM(CAST(( cc_data_view."purchases"  ) AS DOUBLE PRECISION)), 0) AS "sum_of_purchases"
FROM public.cc_data_view  AS cc_data_view
WHERE ((( cc_data_view."daterange"  ) >= (DATE_TRUNC('day', DATE '2023-01-01')) AND ( cc_data_view."daterange"  ) < (DATE_TRUNC('day', DATE '2024-01-01'))))
GROUP BY
    1
ORDER BY
    2 DESC
FETCH NEXT 500 ROWS ONLY

The Customer Journey Analytics objects (dimensions, metrics, filters, calculated metrics, and date ranges) are available as part of the embedded SQL queries you construct. See earlier examples.

Custom transformations

1. Enter the following statements in a new cell.

   code language-python
   data = %sql SELECT LOWER(product_category) AS `Product Category`, COUNT(*) AS EVENTS \ FROM cc_data_view \ WHERE daterange BETWEEN '2023-01-01' AND '2024-01-01' \ GROUP BY 1 \ ORDER BY `Events` DESC \ LIMIT 5; display(data)

2. Execute the cell. You should see output similar to the screenshot below.

   

The query is excuted by the BI extension as defined in Jupyter Notebook.

The Customer Journey Analytics components (dimensions, metrics, filters, calculated metrics, and date ranges) are available as similar named objects in the R language. Refer to the components using the component See earlier examples.

Custom transformations

1. Enter the following statements between ```{r} and ``` in a new chunk.

   code language-r
   df <- dv %>% filter(daterange >= "2023-01-01" & daterange <= "2024-01-01") %>% mutate(d2=lower(product_category)) %>% group_by(d2) %>% count() %>% arrange(d2, .by_group = FALSE) print(df)

2. Run the chunk. You should see output similar to the screenshot below.

   

The query generated by RStudio using the BI extension is including lower, which implies that the custom transformation is executed by RStudio and the BI extension.

SELECT "d2", COUNT(*) AS "n"
FROM (
  SELECT "cc_data_view".*, lower("product_category") AS "d2"
  FROM "cc_data_view"
  WHERE ("daterange" >= '2023-01-01' AND "daterange" <= '2024-01-01')
) AS "q01"
GROUP BY "d2"
ORDER BY "d2"
LIMIT 1000

Comparison

For most Customer Journey Analytics visualizations, Power BI Desktop offers equivalent experiences. See the table below.

table 0-row-3 1-row-3 2-row-3 3-row-3 4-row-3 5-row-3 6-row-3 7-row-3 8-row-3 9-row-3 10-row-3 11-row-3 12-row-3 13-row-3 14-row-3 15-row-3 16-row-3 17-row-3 18-row-3 19-row-3 20-row-3 21-row-3 22-row-3 23-row-3 1-align-center 5-align-center 9-align-center 13-align-center 17-align-center 21-align-center 25-align-center 29-align-center 33-align-center 37-align-center 41-align-center 45-align-center 49-align-center 53-align-center 57-align-center 61-align-center 65-align-center 69-align-center 73-align-center 77-align-center 81-align-center 85-align-center 89-align-center 93-align-center
Icon	Customer Journey Analytics visualization	Power BI Desktop visualization
	Area
	Bar
	Bar stacked
	Bullet
	Cohort table
	Combo
	Donut
	Fallout	.
	Flow	Decomposition tree?
	Freeform table	and
	Histogram
	Horizontal bar
	Horizontal bar stacked
	Journey canvas
	Key metric summary
	Line
	Scatter
	Section header
	Summary change
	Summary number
	Text
	Treemap
	Venn

Drill down

Power BI supports a to explore in-depth details on certain visualizations. In the example below, you analyze purchase revenue for product categories. From the context menu of a bar representing a product category, you can select Drill down.

{modal="regular"}

Drill down updates the visualization with purchase revenue for products within the selected product category.

{modal="regular"}

The drill down results in the following SQL query that uses a WHERE clause:

select "_"."product_category" as "c25",
    "_"."product_name" as "c26",
    "_"."a0" as "a0"
from
(
    select "_"."product_category",
        "_"."product_name",
        "_"."a0"
    from
    (
        select "_"."product_category",
            "_"."product_name",
            "_"."a0"
        from
        (
            select "rows"."product_category" as "product_category",
                "rows"."product_name" as "product_name",
                sum("rows"."purchase_revenue") as "a0"
            from
            (
                select "_"."product_category",
                    "_"."product_name",
                    "_"."purchase_revenue"
                from "public"."cc_data_view" "_"
                where ("_"."daterange" >= date '2023-01-01' and "_"."product_category" = 'Fishing') and "_"."daterange" < date '2024-01-01'
            ) "rows"
            group by "product_category",
                "product_name"
        ) "_"
        where not "_"."a0" is null
    ) "_"
) "_"
order by "_"."product_category",
        "_"."product_name"
limit 1001

Comparison

For most Customer Journey Analytics visualizations, Tableau Desktop offers equivalent experiences. See the table below.

table 0-row-3 1-row-3 2-row-3 3-row-3 4-row-3 5-row-3 6-row-3 7-row-3 8-row-3 9-row-3 10-row-3 11-row-3 12-row-3 13-row-3 14-row-3 15-row-3 16-row-3 17-row-3 18-row-3 19-row-3 20-row-3 21-row-3 22-row-3 23-row-3 1-align-center 5-align-center 9-align-center 13-align-center 17-align-center 21-align-center 25-align-center 29-align-center 33-align-center 37-align-center 41-align-center 45-align-center 49-align-center 53-align-center 57-align-center 61-align-center 65-align-center 69-align-center 73-align-center 77-align-center 81-align-center 85-align-center 89-align-center 93-align-center
Icon	Customer Journey Analytics visualization	Power BI Desktop visualization
	Area
	Bar
	Bar stacked
	Bullet
	Cohort table
	Combo
	Donut
	Fallout
	Flow
	Freeform table
	Histogram
	Horizontal bar
	Horizontal bar stacked
	Journey canvas
	Key metric summary
	Line
	Scatter
	Section header
	Summary change
	Summary number
	Text
	Treemap
	Venn

Drill down

Tableau supports through . In the example below, you create a hierarchy when you select the Product Name field within Tables and drag it on top of Product Category. Then, from the context menu of a bar representing a product category, you can select + Drill down.

{modal="regular"}

Drill down updates the visualization with purchase revenue for products within the selected product category.

{modal="regular"}

The drill down results in the following SQL query that is using a GROUP BY clause:

SELECT CAST("cc_data_view"."product_category" AS TEXT) AS "product_category",
  CAST("cc_data_view"."product_name" AS TEXT) AS "product_name",
  SUM("cc_data_view"."purchase_revenue") AS "sum:purchase_revenue:ok"
FROM "public"."cc_data_view" "cc_data_view"
WHERE (("cc_data_view"."daterange" >= (TIMESTAMP '2023-01-01 00:00:00.000')) AND ("cc_data_view"."daterange" < (TIMESTAMP '2024-01-01 00:00:00.000')))
GROUP BY 1,
  2

The query does not limit the results to the selected product category; only the visualization shows the selected product category.

{modal="regular"}

Alternatively, you can create a drill down dashboard where one visual is the result of the selection in another visual. In the example below, the Product Categories visualization is used as a filter to update the Product Names table. This visualization filter is client-only and does not result in an additional SQL query.

{modal="regular"}

Comparison

For most Customer Journey Analytics visualizations, Looker offers equivalent experiences. See the table below.

table 0-row-3 1-row-3 2-row-3 3-row-3 4-row-3 5-row-3 6-row-3 7-row-3 8-row-3 9-row-3 10-row-3 11-row-3 12-row-3 13-row-3 14-row-3 15-row-3 16-row-3 17-row-3 18-row-3 19-row-3 20-row-3 21-row-3 22-row-3 23-row-3 1-align-center 5-align-center 9-align-center 13-align-center 17-align-center 21-align-center 25-align-center 29-align-center 33-align-center 37-align-center 41-align-center 45-align-center 49-align-center 53-align-center 57-align-center 61-align-center 65-align-center 69-align-center 73-align-center 77-align-center 81-align-center 85-align-center 89-align-center 93-align-center
Icon	Customer Journey Analytics visualization	Power BI Desktop visualization
	Area
	Bar
	Bar stacked
	Bullet
	Cohort table
	Combo
	Donut
	Fallout
	Flow
	Freeform table
	Histogram
	Horizontal bar
	Horizontal bar stacked
	Journey canvas
	Key metric summary
	Line
	Scatter
	Section header
	Summary change
	Summary number
	Text
	Treemap
	Venn Diagram

• Power BI Desktop Advanced date range filtering is exclusive. For your end date, you need to select one past the day you want to report on. For example, is on or after 1/1/2023 and before 1/2/2023.

• Power BI Desktop defaults to Import when you create a connection. Please ensure you use Direct Query.

• Power BI Desktop exposes data transformations through Power Query. Power Query primarily works with Import type connections so a many transformations that you apply like date or string functions throw an error saying you need to switch to an Import type connection. If you need to transform data at query time, you should use derived dimensions and metrics so Power BI doesn’t need to do the transforms itself.

• Power BI Desktop does not understand how to handle date-time type columns so the daterange X dimensions like daterangehour and daterangeminute are not supported.

• Power BI Desktop by default tries to make multiple connections using up more Query Service sessions. Go in to the Power BI settings for your project and disable parallel queries.

• Power BI Desktop does all the sorting and limiting client-side. Power BI Desktop also has different semantics for top X filtering that includes tied values. So, you cannot create the same sorting and limiting as you can do in Analysis Workspace.

• Earlier versions of the Power BI Desktop October 2024 release break PostgreSQL data sources. Ensure you use the version mentioned in this article.

• Tableau Desktop Range of Dates filtering is exclusive. For your end date, you need to select one past the day you want to report on.

• By default, when you add a date or date-time dimension like Daterangemonth to the rows of a sheet, Tableau Desktop wraps the field in a YEAR() function. To get what you want, you need to select that dimension and from the dropdown menu select the date function you want to use. For example, change Year to Month when you are trying to use Daterangemonth.

• Limiting results to the Top X is not obvious in Tableau Desktop. You can limit the results explicitly or using a calculated-field and the INDEX() function. Adding a Top X filter to a dimension generates complex SQL using an inner-join that is not supported.

• Looker has a maximum number of connections per node setting that is required to be between 5-100. You cannot set this value to 1. This setting implies that a Looker connection always uses at a minimum 5 of the available Query Service sessions.

• Looker lets you create a project with a view based on a Customer Journey Analytics Data view. Looker then creates a model based on the dimensions and metrics, available in the Data view, using LookerML. This Project View does not automatically update to match the source. If you make changes or additions to the CJA Data View dimensions, metrics, calculated-metrics, or filters, then these changes do not automatically show up in Looker. You have to manually update the Project View or create a new Project.

• Looker’s user-experience on date or date-time fields like Daterange Date or Daterangeday Date is confusing.

• Looker’s date range is exclusive instead of inclusive. The until (before) is in gray so you may miss that aspect. For your end day, you need to select one past the day you want to report on.

• Looker doesn’t automatically treat your metrics as metrics. When you select a metric, by default Looker tries to treat the metric as a dimension in the query. To treat a metrics as a metric, you need to create a custom field as illustrated above. As a shortcut you can select â‹®, select Aggregate, and then select Sum.

• The main caveat for Jupyter Notebook is that the tool does not a drag-and-drop user interface like other BI tools. You can create good visuals, but you have to write code to accomplish this.

• R dplyr works with a flat schema so the FLATTEN option is required.

• The main caveat for RStudio is that the tool does not a drag-and-drop user interface like other BI tools. You can create good visuals, but you have to write code to accomplish this.