Build Dashboards with RadarCube ASP.NET OLAP Control for Microsoft Analysis Services

Build Dashboards with RadarCube ASP.NET OLAP Control for Microsoft Analysis ServicesCreating interactive, high-performance dashboards is a common requirement for business intelligence (BI) teams. When your data lives in Microsoft Analysis Services (MS AS), you want a UI layer that can query multidimensional cubes quickly, let users explore hierarchies and measures, and render flexible visuals inside web applications. RadarCube’s ASP.NET OLAP control is designed to do exactly that — it connects to MS AS, issues MDX queries, and exposes pivot-grid, charting, and slice-and-dice interactions optimized for web dashboards. This article covers what the control does, architecture and integration patterns, dashboard design tips, performance considerations, and a short example workflow to get you started.

What is RadarCube ASP.NET OLAP Control?

RadarCube ASP.NET OLAP control is a server-side web control (and client-side UI) that provides pivot/grid and OLAP exploration features for ASP.NET applications. It connects to Microsoft Analysis Services (both Multidimensional and Tabular via compatibility layers), translates user interactions into MDX (or DAX when supported), and renders results as pivot tables, treemaps, charts, and other dashboard widgets. The control abstracts many OLAP details so developers can focus on UX and business logic.

Key features that matter for dashboards

• Fast MDX translation and query execution against MS AS.
• Server-side caching and client-side interaction smoothing to reduce round-trips.
• Drag-and-drop pivot grid for building ad-hoc views (rows, columns, filters, measures).
• Built-in chart types (bar, line, pie, treemap) and ability to bind results to third-party charting libraries.
• Support for hierarchies, calculated members, named sets, and time intelligence common in cubes.
• Security integration with Windows/AD or custom roles to respect Analysis Services permissions.
• Export to Excel/CSV and printing support for operational reporting needs.

Architecture and integration patterns

Typical deployment patterns include:

• Direct server-to-server connection: The ASP.NET application uses a service account to connect to Analysis Services over TCP/IP. Queries are executed on the MS AS server and results returned to the web app.
• Middle-tier query service: Introduce a secure middleware (Web API) that holds connection credentials and performs queries, so web servers don’t maintain direct Analysis Services connections. Useful for microservices architectures and load balancing.
• Cached query layer: Use an application cache or Redis to store query results or aggregated tiles for frequently requested slices, reducing repeated MDX execution.
• Hybrid: Combine live querying for ad-hoc analysis with scheduled ETL/aggregations for KPI panels that must be instant.

Integration notes:

• Ensure the web application has the appropriate AD/Windows impersonation or connection string credentials to access Analysis Services.
• If using a middle tier, design your API to accept OLAP context (cube, members, filters) and return structured JSON with axis metadata so the front end can render pivot views without rebuilding semantic semantics.
• For environments with Tabular models, check control compatibility—some versions may require DAX translation or use of compatibility modes.

Dashboard UI patterns with RadarCube

1. KPI Header with Quick Filters
   • Display top-level KPIs (Revenue, Margin, Orders) as cards that query pre-defined MDX. Add a date slicer and a geography slicer to update all cards.
2. Pivot Grid Explorer
   • A central interactive pivot where users drag dimensions into rows/columns and select measures. Provide “save view” functionality so users can store favorite layouts.
3. Visualizations Panel
   • Bind pivot results to charts (trend chart, stacked bars) and a treemap for product/category share. Offer sync between selection in pivot and chart cross-filtering.
4. Time Analysis Strip
   • A compact area for time-series controls: rolling period toggles (YTD, MTD, QTD), comparison toggles (YoY, QoQ), and a sparkline gallery.
5. Export and Sharing
   • Single-click export to Excel with underlying MDX or to CSV for ad-hoc analysis; ability to share dashboard URLs that encode current filter state.

Performance best practices

• Limit returned tuples: OLAP queries that request many members across multiple axes can be slow. Use paging or top-N filters.
• Use server-side aggregations and calculated members in the cube where possible rather than computing heavy calculations at query time.
• Cache frequently used MDX results at the application or CDN layer for read-heavy dashboards.
• Prefer named sets for commonly used member groups; they’re parsed and optimized by MS AS.
• Reduce network latency by colocating web servers and Analysis Services in the same network region/VNet.
• Monitor query performance with SQL Server Profiler and Analysis Services performance counters; tune slow MDX queries by inspecting the query plan and aggregations.
• Use asynchronous loading for heavy widgets so the page renders while specific charts load in the background.

Security and user access

• Respect Analysis Services role-based security: the control should execute queries under the identity that enforces cube roles.
• For web farms, consider Kerberos delegation if you need user impersonation from the browser through the web server to MS AS.
• When using a shared service account, implement row-level filtering or custom security in the cube to restrict data access per user.
• Sanitize input used to construct MDX when allowing ad-hoc MDX to prevent injection or performance pitfalls.

Example workflow: Build a simple sales dashboard

1. Connect the control to your MS AS server and select the Sales cube.
2. Define three measures: Sales Amount, Units Sold, Gross Margin (calculated member).
3. Create named sets for Top 10 Products and Region Sales Group.
4. Add a pivot control to the page with:
   • Rows: Product Category hierarchy
   • Columns: Calendar Year > Quarter
   • Values: Sales Amount, Gross Margin %
5. Add a line chart bound to the pivot’s time axis for trend visualization.
6. Add slicers for Region and Sales Channel that rebind both pivot and chart on change.
7. Configure caching for the pivot results with a 5–15 minute TTL for dashboard KPIs.
8. Test with representative users and optimize MDX or named sets where slowness occurs.

Code-level specifics vary by RadarCube version and whether you’re using WebForms, MVC, or .NET Core. Refer to the control’s API docs for server-side binding and client event hooks to implement drag-and-drop and state persistence.

Troubleshooting common issues

• Slow initial load: enable caching, reduce default row/column depth, and prefetch KPIs.
• Incorrect totals or aggregations: confirm measure aggregation type and check calculated members’ scope in MDX.
• Security errors: verify service account permissions and, if using delegation, confirm Kerberos configuration.
• Rendering glitches on mobile: use responsive container settings or serve simplified views for small screens.

When to use RadarCube vs other approaches

Use RadarCube when you need tight OLAP features (hierarchies, MDX support, pivoting) directly in ASP.NET dashboards without building a custom MDX-to-visualization layer. If you already have a modern analytics stack (Power BI, Looker, or embedded visuals that natively support Tabular/DAX) and want cloud-managed features, evaluate whether RadarCube’s on-prem OLAP strengths outweigh the benefits of cloud-native BI tools.

Conclusion

RadarCube’s ASP.NET OLAP control brings multidimensional power to web dashboards that rely on Microsoft Analysis Services. By leveraging MDX, hierarchies, caching, and a drag-and-drop pivot UI, it lets developers build responsive, exploratory dashboards while keeping heavy computation on the server. Follow the architecture patterns, security practices, and performance tips above to create scalable dashboards that give users fast, meaningful insights.