Author: Parmod Kumar, Certified Solution Architect, and Financial Planning & Supply Chain Specialist at TCS.
Reading time: approximately 8-10 minutes.
Why the Polaris engine Is powerful and beautiful
Polaris is Anaplan’s next‐generation calculation engine, built from the ground up to handle high dimensionality, sparse data, and very large-scale planning use-cases.
Designed to enable business users to model “at natural dimensionality” (i.e. without flattening dimensions, aggregating too early, or using workarounds due to performance constraints). Supports “quintillions” of addressable cells per line item.
Feature description:
- Native storage and calculation: only “populated” cells consume memory & compute. Empty intersections don’t occupy the space in the memory and not increase the model size. This improves performance when data is sparse across many dimensions.
- On-Demand Calculation in Polaris computes line items’ values only when users view/request them not eagerly across the entire module each time. This reduces unnecessary calculation over non-viewed or non-used parts.
- High dimensionality and more dimensions/hierarchies can be included without flattening; hierarchical depth, combinations, long time scales etc. are easier to support.
- Real-time updates the data and users see updates more quickly, less waiting time. Real-time responsiveness is improved especially for dashboards / what-if scenarios.
Differences vs Classic Engine
Classic Engine | Polaris Engine |
|---|
Data sparsity handling less efficient: many empty intersections still consume resource; modeling often includes aggregations or flattening to reduce sparsity. | Natively sparse: only populated data matters; performance depends more on populated vs empty space rather than raw potential intersections. |
Dimensionality / scale limited by performance constraints when adding many dimensions, hierarchies, or long time slices. | Much higher; supports very large combinations, deeper hierarchies, large time ranges. |
Modeling complexity more workarounds required (flattening, summarization, combining dimensions to stay under limits). | Enables simpler, more natural model structures. Fewer compromises. |
Calculation strategy eager / global recalculation more often. | On-demand, smarter recalculation only where needed. |
Migration impact existing models may need rework / optimization to scale. | New models should be built in Polaris. Migration from Classic possible but needs planning. |
Best practices and considerations for model builders
To make best use of Polaris, these are best practices and modeling patterns you should consider:
- Plan for natural dimensionality
- Include all relevant dimensions, hierarchies, time ranges, etc., instead of aggregating prematurely.
- Define dimension combinations, sparsity to balance usability and performance.
- Optimize for populated space
- Focus on measuring populated cells (cells which have values) vs total potential cells. Many operations depend on “populated count.”
- Use blueprint insights tools to understand where sparsity or unnecessary density is hurting the model size.
- Efficient formula writing and scoping
- Only compute what is needed
- Avoid generic formulas that cause unnecessary computation over empty areas.
- Hierarchy and time management
- Deep hierarchies can increase sparsity; manage them carefully.
- Longer time ranges (future, historical) increase dimensionality — ensure that unused time periods are handled efficiently.
- Monitor and tune performance
- Use built-in option like cell counts, population, calculation effort
to find bottlenecks.
- Incrementally build and test; performance surprises are easier to manage if model structure is modular.
- Migration strategies (Classic → Polaris)
- Evaluate existing models for pain points (where complexity, flattening, or slow performance are present).
- Re-architect models rather than simply lifting into Polaris. Revisit dimension structures, hierarchies, formula scoping.
- Perform the testing and validation of results (i.e. functional validation, calculation accuracy) particularly when behavior differs slightly.
Strategic and business benefits
In addition to the technical advantages, here are some business benefits:
- Reduced Total Cost of Ownership (TCO): fewer workarounds, less maintenance, less complexity.
- Faster time to insights: scenario planning, what-if analyses, changes propagate quickly.
- Greater agility: ability to adapt the model as business requirements change (new dimensions, new KPIs).
- Scalability: more users, more scenarios, larger data volumes without performance collapse.
Cell calculations example
Total Lists
-Region-11
-Customer- 1000
-Product- 1400
-Channel -8
-Time- 3 years- 52
-Lineitem -3
Total cells- 19219200000
Per Line item- 6406400000
In the example provided below, I have input data solely related to sales for three years concerning a few products, customers, and regions. This illustrates the advantage of the Polaris engine, which calculates and allocates space for the values entered by the user.
All other cells remain empty and do not occupy any memory space. Furthermore, it offers me the flexibility to utilize extensive lists without any complications, thereby allowing for an increase in the model size.
Test modules with five dimensions:
Lists information:
Module blueprint view:
Conclusion
Polaris lets you model the world the way it really is — dimensional, complex, and sparse — without sacrificing speed.
It removes limits, reduces workarounds, and makes large-scale planning beautifully simple and aligned with real business logic.
See More. Plan Better. Move Faster. ➡️ Infinite Dimension. Zero Compromise.
