How-Tos & FAQs

Useful Grafana Dashboard [Linux]

This is an additional Grafana Dashboard that will help easier monitor services on the multi-node environments In Sisense GUI: Go to Admin -> Server&Hardware -> System Management -> Monitoring (at the top right corner) Grafana interface will open -> choose Dashboards -> New -> Import: Upload the attached JSON file In the newly imported dashboard you will see your node names which you can expand: Expand one or more nodes at once to see the scheduled pods: This dashboard will be helpful to conveniently monitor pods on the selected nodes separately without need to switch between them References/Related Content https://docs.sisense.com/main/SisenseLinux/monitoring-sisense-on-linux.htm#Grafana { "annotations": { "list": [ { "builtIn": 1, "datasource": { "uid": "-- Grafana --" }, "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": null, "links": [ { "asDropdown": true, "includeVars": true, "keepTime": true, "tags": [], "targetBlank": false, "title": "Kubernetes", "type": "dashboards" } ], "panels": [ { "id": 100, "type": "row", "title": "Node: $node", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "collapsed": false, "panels": [], "repeat": "node" }, { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisBorderShow": false, "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "barWidthFactor": 0.6, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "insertNulls": false, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "showValues": false, "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": 0 }, { "color": "red", "value": 80 } ] }, "unit": "percentunit" }, "overrides": [] }, "gridPos": { "h": 10, "w": 24, "x": 0, "y": 1 }, "id": 6, "options": { "legend": { "calcs": [], "displayMode": "table", "placement": "right", "showLegend": true }, "tooltip": { "hideZeros": false, "mode": "multi", "sort": "none" } }, "pluginVersion": "12.2.0", "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "expr": "sum by (pod) (rate(container_cpu_usage_seconds_total{job=\"kubelet\", image!=\"\",container!=\"POD\", pod=~\"$pod\",node=~\"$node\"}[1m]))", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": " {{ pod }} ", "refId": "A" } ], "title": "Cpu Usage", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisBorderShow": false, "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "barWidthFactor": 0.6, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "insertNulls": false, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "showValues": false, "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": 0 }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 8, "w": 24, "x": 0, "y": 11 }, "id": 10, "options": { "legend": { "calcs": [], "displayMode": "table", "placement": "right", "showLegend": true }, "tooltip": { "hideZeros": false, "mode": "multi", "sort": "none" } }, "pluginVersion": "12.2.0", "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "expr": "sum by(pod) (container_memory_working_set_bytes{job=\"kubelet\", namespace=\"$namespace\", pod=~\"$pod\", container!=\"\", container!=\"POD\",node=~\"$node\"})", "format": "time_series", "intervalFactor": 1, "legendFormat": " {{ pod }} ", "refId": "A" } ], "title": "Memory Usage", "type": "timeseries" }, { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisBorderShow": false, "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "barWidthFactor": 0.6, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "insertNulls": false, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "showValues": false, "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": 0 }, { "color": "red", "value": 80 } ] }, "unit": "bytes" }, "overrides": [] }, "gridPos": { "h": 9, "w": 24, "x": 0, "y": 19 }, "id": 8, "options": { "legend": { "calcs": [], "displayMode": "table", "placement": "right", "showLegend": true }, "tooltip": { "hideZeros": false, "mode": "multi", "sort": "none" } }, "pluginVersion": "12.2.0", "targets": [ { "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "expr": "sort_desc(sum by (pod) (rate(container_network_receive_bytes_total{job=\"kubelet\", pod=~\"$pod\",node=~\"$node\"}[1m])))", "format": "time_series", "hide": false, "intervalFactor": 1, "legendFormat": " {{ pod }} ", "refId": "A" } ], "title": "Network I/O", "type": "timeseries" } ], "preload": false, "refresh": "", "schemaVersion": 42, "tags": [], "templating": { "list": [ { "current": { "text": "sisense", "value": "sisense" }, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "includeAll": false, "label": "Namespace", "name": "namespace", "options": [], "query": "label_values(kube_pod_info, namespace)", "refresh": 1, "regex": "", "type": "query" }, { "current": { "text": "All", "value": [ "$__all" ] }, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "includeAll": true, "label": "Pod", "multi": true, "name": "pod", "options": [], "query": "label_values(kube_pod_info{namespace=~\"$namespace\", pod!~\"cronjob.*\"}, pod)", "refresh": 1, "regex": "", "type": "query" }, { "current": { "text": "All", "value": [ "$__all" ] }, "datasource": { "type": "prometheus", "uid": "PBFA97CFB590B2093" }, "definition": "label_values(kube_node_info, node)", "includeAll": true, "label": "Node", "multi": true, "name": "node", "options": [], "query": "label_values(kube_node_info, node)", "refresh": 1, "regex": "", "type": "query" } ] }, "time": { "from": "now-3h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Pods Resource Usage by Node", "uid": "pods-by-node", "version": 1 }

How-Tos & FAQs

Using SQL Coalesce To Handle Null Values

Question : SQL NULL is not technically a value, but rather a state. Fields are not equal or not equal to null , they are or are not null. As such, null fields can get a little tricky when attempting different kinds of transformations. For example, mathematical equations across multiple fields where any of the fields are null will result in a null value: 5 * null = null null – null = null 5 + null = null Consider a salary calculation where an employee as Base but no Commission: Solution : In this case the Total for Regina should equal 49000, not null. To transform the null into a not null value like 0 we could use SQL Case: More efficiently we can use Coalesce which checks for null values in the specified expression (i.e., Base or Commission) and does the translation to the specified value (i.e., 0): Coalesce is also useful for finding the first populated (i.e., non-null) value across multiple fields in the same record (e.g., select the first non-null phone number from home, mobile, office fields). Using SQL Case: The same logic using Coalesce results in more efficient and decipherable query: Both queries bear the same correct result:

How-Tos & FAQs

Passing Dashboard filters by URL Parameters

This article describes how the append dashboard filters as URL params to apply scope automatically to embedded dashboards. The example below shows how to apply members as filters. If you already have dashboard filters defined, you can write the following command in your development console (Ctrl+Shift+I / F12 in Google Chrome ==> console tab) : Type this line (this is encoding the parameters. Ignore the undefined comment you get): var urlParams = encodeURIComponent(JSON.stringify(prism.activeDashboard.filters Type 'urlParams' to get the result: %7B%22jaql%22%3A%7B%22dim%22%3A%22%5Btable_name.field_name%5D%22%2C%22datatype%22%3A%22text%22%2C%22filter%22%3A%7B%22members%22%3A%5B%22first%20value%22%2C%22second%20value%22%5D%7D%7D%7D Append to the end of your dashboard URL "?filter=" and paste the string you got current_dashboard_url?filter=%7B%22jaql%22:%7B%22dim%22:%22%5Barea_wise_target.Region%5D%22,%22datatype%22:%22text%22,%22filter%22:%7B%22members%22:%5B%22North%22,%22West%22%5D%7D%7D%7D Your dashboard will be opened with the values set as filters. If you wish to define a new scope (instead of prism.activeDashboard.filters.$$items) then: Open the development console of your browser and type in: var scope = [{ "jaql": { "table": "Table_Name", "column": "Field_Name", "dim": "[Table_Name.Field_Name]", "datatype": "text", "merged": true, "title": "Field_Name", "collapsed": true, "filter": { "members":["First Value", "Second Value"], "explicit": false, "multiSelection": true} } }] For multiple filters [an array of 2 or more jaql objects:]: var scope = [{jaql:{ datatype:'text', dim:"[Lead Generation.Country]", filter:{members:["Portugal"] } } }, {jaql:{ datatype:'text', dim:"[Lead Generation.City]", filter:{members:["Boston"]} } } ] To use a date (time frame - this month): "{ "dim":"[Lead Generation.Date (Calendar)]", "datatype":"datetime", "level":"months", "filter":{"last":{"count":1,"offset":0}} }" or replace months with "days". Other filter types You can easily see the Jaql of the filter by going to "advanced" tab on the selected filter. Please note that this is NOT a data-security feature and can be overridden easily by the user to view the entire data. To secure your data, please use the ElastiCube share option and data security permissions on the user/group level.

How-Tos & FAQs

Analyzing Query Success, Failures, and Performance Using the FinishQuery Table

Background Sisense provides an out of the box Usage – Query dashboard when the Usage Analytics model is enabled. This dashboard offers insights into queries created and executed by users. The goal of this article is to introduce a structured flow for Admins to review overall query performance and drill down into successful and failed queries. To support this analysis, the solution is divided into two dashboards, one focused on successful queries and one on failed queries, enabling efficient troubleshooting and performance optimization. The Dashboards Successful Query Performance Dashboard This dashboard analyzes successful queries only and helps Admins evaluate overall query performance by: Identifying performance bottlenecks and understanding when they occur Analyzing which execution phase (translation, data source execution, throttling) contributes most to total query duration Highlighting whether the source of performance impact is clearly identified or requires further investigation Reviewing query performance at the dashboard level, widget level, and individual query level Tracking query behavior and performance over time, including date and time-based patterns to identify peak hours, recurring slowdowns, or workload-related trends For best results, it is recommended to: Review the ReadMe widget included in the dashboard before getting started, to gain a better understanding of the data, definitions, and assumptions used in the analysis Analyze the dashboard by asset query source, using single-select filtering for each option to ensure focused and accurate analysis: Dashboard queries Widget queries Compose SDK queries API / JAQL queries Filter by duration to review longer-running queries and understand the factors contributing to their performance. Review performance by data model source, also using single-select filtering to isolate behavior and performance patterns: Live models Cube-based models Failed Queries Performance Dashboard This dashboard analyzes failed queries only and helps Admins investigate and understand query failures by: Identifying the root causes of failed queries using exception messages and categorized error types Pinpointing when failures occur, including exact date and time, to detect recurring patterns or peak failure windows Analyzing failures by data source and data model, helping determine whether issues are related to Live connections, Cubes, or specific providers Reviewing failed queries at the dashboard level, widget level, and individual query (Query GUID) level, enabling precise troubleshooting for specific widgets or assets Filter Out Canceled queries Implementation Steps Unzip the provided file locally. The ZIP file contains: A TXT file with the SQL used to create the Fact_finish_query table The Successful Query Performance dashboard file The Failed Queries Overview dashboard file Enable Usage Analytics (if not already enabled): Navigate to Admin → Monitoring & Support → Usage Analytics Enable Collect Usage Analytics Open the Usage Analytics model: Navigate to the Data tab Open the Usage Analytics data model Create a custom table: Add a new custom table named Fact_finish_query Paste the SQL from the provided TXT file into the table definition Parse the SQL Statement by Preview the table If Parse Successfully --> Click Save Build the data model: Run Build (Full or Changes Only) Import the dashboards: Navigate to the Analytics tab Import both dashboards from the unzipped files Implementation Troubleshooting If the Custom Table import does not parse successfully, the issue is most likely related to columns that are in a dropped state. Below is an example error. The import fails because the Custom Table references a column that is not declared in the FinishQuery table. Steps to review In the Table Columns left panel, search for the FinishQuery table. Scroll down to the dropped columns at the bottom of the column list, which are highlighted inline. Open the column menu and click Restore . Add the Custom Table and verify that it was parsed successfully. Build the data model: Run Build (Full or Changes Only) Import the dashboards: Navigate to the Analytics tab Import both dashboards from the unzipped files Disclaimer This is a custom solution provided for reference only. It may require modifications to work in different environments and could be affected by future product updates. Customers are responsible for testing and validating the solution before using it in production.

How-Tos & FAQs

Prometheus storage & retention changes for Sisense EKS monitoring [Linux]

Step-by-Step Guide: 1. Adjust the Prometheus Custom Resource (CR) The Prometheus CR controls runtime configuration, including retention and storage settings. Updating the CR applies changes immediately. View the existing Prometheus CR: kubectl -n monitoring get prometheus sisense-prom-operator-kube-prometheus -o yaml Edit the CR to update retention and PVC size: kubectl -n monitoring edit prometheus sisense-prom-operator-kube-prometheus Update the following values under spec: retention: 60d retentionSize: 50GB Increase the persistent volume claim size: storage: volumeClaimTemplate: spec: accessModes: - ReadWriteOnce storageClassName: gp3 resources: requests: storage: 100Gi Save the file. The Prometheus Operator will automatically expand the PVC, update the StatefulSet, and restart the Prometheus pod. Verify the PVC size: kubectl -n monitoring get pvc | grep prometheus 2. Make changes persistent across Sisense upgrades Sisense upgrades regenerate monitoring resources based on Helm templates. To ensure the changes are preserved, the new values must be added to the extra values file. Open the extra values file: sisense-L2025.3.0.397/installer/extra_values/helm/kube-prometheus-stack.yaml Add the following content: prometheus: prometheusSpec: retention: 60d retentionSize: 50GB storageSpec: volumeClaimTemplate: spec: accessModes: - ReadWriteOnce storageClassName: gp3 resources: requests: storage: 100Gi Save the file. These settings will now override defaults during future upgrades. Disclaimer: This post outlines a potential custom workaround for a specific use case or provides instructions regarding a specific task. The solution may not work in all scenarios or Sisense versions, so we strongly recommend testing it in your environment before deployment. If you need further assistance with this, please let us know.

How-Tos & FAQs

Working with deprecated functions in Sisense [Linux]

Step-by-Step Guide: Scenario You’ve upgraded from an older Sisense version (or migrated to Linux) that used the original Translator. In the current version, which uses the Analytical Engine, some functions may not work or may show an error message, such as: “Function not supported.” Here’s how you can address these issues. Option 1: Use Compatibility Mode Starting with L2025.2, Sisense provides a Compatibility Mode that reverts to the original Translator for unsupported queries. This is configured per model and can help restore previous functionality. Steps: Open the Data tab. Select the relevant model. Go to Analytical Engine Settings. Select Compatibility Mode – this follows the system configuration and falls back to the original translator for unsupported queries. To apply the changes: EC and B2D models: Build the model Live providers: Publish the model Option 2: Update Function Syntax Some functions may still exist but require updated syntax in Linux. Adjusting your formulas according to the current documentation can often resolve the issue. Example: In Windows, you might use the following function to return the Max Cost filtered by Category ID: SUM(([Category ID],[Max Cost])) After migrating to Linux, this may not produce the same result. Instead, use: MAX([Category ID],[Max Cost]) Notes: The logic of the function may have changed. Extra braces are no longer supported in some cases. You can still achieve the same result using the correct function for Linux. For full details, refer to the Dashboard Functions Reference . Option 3: Contact Sisense Support If issues persist with deprecated or updated functions, reach out to the Sisense Support Team. Provide your use case, and they can suggest workarounds or alternatives. Conclusion Even when functions are deprecated, multiple workarounds exist to achieve the same or similar results. Start with Compatibility Mode, check the updated syntax, and contact support if needed. References/Related Content Analytical Engine Dashboard Functions Reference

How-Tos & FAQs

Sisense failure after yum update [Linux]

Step-by-Step Guide: Run the following command to check the most recent update history: 1. yum history The list with the recent updates and timestamps will open 2. Find the transaction ID you are interested in (probably the latest one) Open the detailed information about that transaction ID (in this example, ID 9) to check what has been installed: 3. yum history info 9 Revert the needed transaction back (in this example, ID 9): 4. yum history undo 9 5. Check if Sisense is up and running again Conclusion: Pay attention to the packages that Sisense is dependent on and also security-related ones that might block some operations. Updates to the packages like Docker, SELinux, and Firewall can potentially cause issues with Sisense operations or access. Additional information about the yum history command: https://access.redhat.com/solutions/64069 https://www.cyberciti.biz/faq/yum-history-command/ Disclaimer : This post outlines a potential custom workaround for a specific use case or provides instructions regarding a specific task. The solution may not work in all scenarios or Sisense versions, so we strongly recommend testing it in your environment before deployment. If you need further assistance with this, please let us know.

How-Tos & FAQs

Working with deprecated functions in Sisense [Linux]

Step-by-Step Guide: Scenario You’ve upgraded from an older Sisense version (or migrated to Linux) that used the original Translator. In the current version, which uses the Analytical Engine, some functions may not work or may show an error message, such as: “Function not supported.” Here’s how you can address these issues. Option 1: Use Compatibility Mode Starting with L2025.2, Sisense provides a Compatibility Mode that reverts to the original Translator for unsupported queries. This is configured per model and can help restore previous functionality. Steps: Open the Data tab. Select the relevant model. Go to Analytical Engine Settings. Select Compatibility Mode – this follows the system configuration and falls back to the original translator for unsupported queries. To apply the changes: EC and B2D models: Build the model Live providers: Publish the model Option 2: Update Function Syntax Some functions may still exist but require updated syntax in Linux. Adjusting your formulas according to the current documentation can often resolve the issue. Example: In Windows, you might use the following function to return the Max Cost filtered by Category ID: SUM(([Category ID],[Max Cost])) After migrating to Linux, this may not produce the same result. Instead, use: MAX([Category ID],[Max Cost]) Notes: The logic of the function may have changed. Extra braces are no longer supported in some cases. You can still achieve the same result using the correct function for Linux. For full details, refer to the Dashboard Functions Reference . Option 3: Contact Sisense Support If issues persist with deprecated or updated functions, reach out to the Sisense Support Team. Provide your use case, and they can suggest workarounds or alternatives. Conclusion: Even when functions are deprecated, multiple workarounds exist to achieve the same or similar results. Start with Compatibility Mode, check the updated syntax, and contact support if needed. References/Related Content Analytical Engine Dashboard Functions Reference

How-Tos & FAQs

Adding downloadable links and images in a pivot table [Linux]

Reference: Sisense Community Article – “ Clickable URL Links in Pivot Rows ” Step-by-Step Guide 1. Enable HTML Rendering for Pivot Tables Navigate to Admin → System Management → Configuration. In the Configuration Manager, select Pivot Table. Enable Allow rendering Pivot Table content as HTML. Scroll down and click Save. This setting allows the Pivot widget to interpret HTML code inside table cells. 2. Host Your Files or Images You can either: Store files/images directly on your Sisense server, or Use external URLs. To host them in Sisense: Go to Admin → App Configuration → File Management → branding. Upload your files or images. They will be accessible at: http://<sisense-deployment>/branding/<file-name>.<extension> 3. Create a Custom Column with Your Download/ Image URLs In your data model, create a new custom column (e.g., DownloadHTML) within a table containing the URLs you want to display or make downloadable. 4. Add HTML for Downloadable Links To generate a clickable download link in the Pivot table, use the following example: '<a href="' + [URLs] + '" download="' + [Name] + '">' + [Name] + '</a>' [URLs] = column containing the file download URL [Name] = column containing the download filename This creates a clickable link that begins downloading the file immediately. 5. Add HTML for Clickable Images That Trigger Downloads To embed an image that downloads when clicked, use the following example: '<a href="' + [Link] + '" download="' + [Name] + '"><img src="' + [Link] + '" alt="' + [Name] + '" style="max-width:200px; height:auto;"></a>' Clicking the embedded image triggers the download. Adjust the max-width style as needed. 6. Rebuild the Cube Rebuild your data model to ensure the new custom column becomes available in widgets. 7. Add the Column to a Pivot Table Create or open a Pivot table and add the custom column you’ve created as a field. Thanks to HTML rendering, the Pivot will display clickable links or images exactly as intended. Conclusion With HTML rendering enabled, you can embed downloadable links and clickable images directly inside Pivot tables - no widget scripts required. This provides a clean and user-friendly way to give users access to files, documents, and images right from within a dashboard. Happy dashboarding! References/Related Content Clickable URL Links in Pivot Rows Disclaimer: This post outlines a potential custom workaround for a specific use case or provides instructions regarding a specific task. The solution may not work in all scenarios or Sisense versions, so we strongly recommend testing it in your environment before deployment. If you need further assistance with this, please let us know. Verified on version: L2025.4.0.258

How-Tos & FAQs

How to Use fiscal quarter drilldown with and without changing fiscal Year settings [Linux]

Step-by-Step Guide: There are two main approaches to implementing financial quarter-specific drilldowns: Option 1: Enable Custom Fiscal Year (ElastiCube or Environment Level) Step 1: Set Fiscal Year on the ElastiCube Navigate to your ElastiCube. Click the three dots icon in the top-right corner → Fiscal Year Start. Select the month that represents the start of your fiscal year. Rebuild the ElastiCube to apply changes. Or Set Fiscal Year on the Environment Level Go to Admin → App Configuration → General → Fiscal Year Start Month Choose the month that starts your fiscal year. Click Save. Step 2: Enable Fiscal Quarter Drilldowns in Dashboard Open your Sisense dashboard. Select the Drill Down feature for your date column. Click More → Quarters (FY). This approach aligns your drilldowns with the fiscal year globally or per ElastiCube. Option 2: Create a Custom Fiscal Quarter Column (Without Changing Fiscal Year Settings) If you prefer not to modify your fiscal year settings, you can create a custom column in your ElastiCube to define financial quarters manually. Step 1: Add a Custom Column Open the ElastiCube page. Create a new custom column for fiscal quarters using a formula like this (assuming fiscal year starts in April): CASE WHEN GETMONTH([Date]) IN (1, 2, 3) THEN 'Q4' WHEN GETMONTH([Date]) IN (4, 5, 6) THEN 'Q1' WHEN GETMONTH([Date]) IN (7, 8, 9) THEN 'Q2' WHEN GETMONTH([Date]) IN (10, 11, 12) THEN 'Q3' END In our example, we get the date information from the field called Date. This assigns each date to the correct fiscal quarter. Step 2: Rebuild the ElastiCube After adding the custom column, rebuild your ElastiCube to integrate the changes. Step 3: Use the Custom Column for Drilldowns In your Sisense dashboard, select the new custom column as the drilldown field. Optional: Use a Filter by Date to select a specific year. Pro Tip: You can extend the custom column to include fiscal year information, avoiding additional filters: CASE WHEN GETMONTH([Date]) IN (1, 2, 3) THEN 'FY' + CAST(GETYEAR([Date]) AS VARCHAR) + ' Q4' WHEN GETMONTH([Date]) IN (4, 5, 6) THEN 'FY' + CAST(GETYEAR([Date]) + 1 AS VARCHAR) + ' Q1' WHEN GETMONTH([Date]) IN (7, 8, 9) THEN 'FY' + CAST(GETYEAR([Date]) + 1 AS VARCHAR) + ' Q2' WHEN GETMONTH([Date]) IN (10, 11, 12) THEN 'FY' + CAST(GETYEAR([Date]) + 1 AS VARCHAR) + ' Q3' END This formula produces the same results as changing the fiscal year to April, but without modifying the environment or ElastiCube Fiscal Year settings: Conclusion: You can achieve financial quarter drilldowns in Sisense using either method: Enable a custom fiscal year: Ideal for global or ElastiCube-wide fiscal alignment. Create a custom column: Flexible and avoids modifying existing fiscal settings. Both methods allow for seamless integration of fiscal quarter-based drilldowns into your dashboards. Choose the approach that best fits your workflow and business rules. References/Related Content Changing Fiscal Year in Sisense Creating Custom Columns in Sisense Disclaimer: This post outlines a potential custom workaround for a specific use case or provides instructions regarding a specific task. The solution may not work in all scenarios or Sisense versions, so we strongly recommend testing it in your environment before deployment. If you need further assistance with this, please let us know. Verified on version: L2025.4.0.258