Real-time analytics at scale
Analyzes terabytes of data at query time without relying on precomputed aggregates or materialized views.
Multi-Dimensional Analytical Indexing
Multi-Dimensional Analytical Indexing combines high-performance search with analytical computing, enabling data to be filtered, correlated, and analyzed across multiple dimensions as questions evolve—without relying on precomputed aggregates or moving data between systems.
Benefits
Complex analysis in a single query
Correlates, filters, groups, and analyzes multiple dimensions within a single query execution.
Exploration without losing context
Maintains analytical context as queries evolve, keeping results connected to the active dataset.
The power of Multi-Dimensional Analytical Indexing
Multi-Dimensional Analytical Indexing combines indexed data access with query-time analytical computation in a unified execution layer. The index organizes data across multiple searchable and analytical dimensions, allowing queries to simultaneously perform filtering, grouping, correlation, aggregation, statistical analysis, and temporal analysis against the active dataset.
Unlike architectures that separate search from analytical processing, the index executes these operations within the same query context. This eliminates the need to transfer intermediate datasets between systems or rely exclusively on precomputed aggregates and materialized views, enabling ad-hoc analytical queries to operate directly against the underlying indexed data.
High-performance compute
The index uses vectorized execution, parallel query evaluation, cache-conscious data layouts, and compressed in-memory structures to enable query-time computation across large datasets.
Search predicates and analytical operators execute directly against the live dataset, supporting filtering, grouping, sorting, joins, and aggregations without materializing intermediate datasets.
One indexed dataset
Advanced query composition
The index supports complex query composition, allowing filters, facets, joins, segmentation, aggregations, statistical functions, anomaly detection, and time-series analysis to be combined within a single query.
Operations can be nested and layered across multiple dimensions, with each stage operating on the resulting data scope of the preceding operations. This enables increasingly complex analytical expressions to be built and executed as a unified query, without splitting the analysis across separate queries or systems.
One query · shared data scope
Built for exploratory analysis
The index is designed for exploration of larger populations, more complex questions, and emerging patterns without sacrificing responsiveness.
Dashboards and investigative workflows can remain within the same analytical context, avoiding the need to switch between search, analysis, and reporting.
One dataset · shared context
SelectionsDistributionOver time
A rich analytical toolkit
The Multi-Dimensional Analytical Index supports a broad range of analytical and search operations:
- Full-text and faceted search
- Boolean filtering with AND, OR, and NOT
- Numeric and temporal range queries
- Real-time autocomplete across facets and full-text content
- Multi-select query construction with live result updates
- Grouping and sorting, including multi-field sorting
- Cross-facet joins for distribution and relationship analysis
- Statistical functions including mean, median, frequency, histogram, minimum, and maximum
- Mathematical and logical expression evaluation
- Outlier and anomaly detection
- Time-series analysis across one or more facets
Why this is different
Traditional technologies are typically optimized around a primary task—retrieving information, accelerating queries, performing analytical processing, or finding semantic similarity.
Multi-Dimensional Analytical Indexing combines these capabilities within a single computational model. Once indexed, the same data can be searched, filtered, grouped, correlated, and analyzed at query time, allowing complex analytical questions to be answered directly against the underlying data.
This means the index is not simply a way to find data—it becomes the computational layer for exploring and analyzing it.
Scroll to compare all platforms.
| Capability | Q-Sensei | Traditional Search Solutions | Databases | OLAP / Analytics Engines |
|---|---|---|---|---|
| Primary purpose | Interactive analysis | Document retrieval | Data storage | Analytical reporting |
| Full-text search | Core capability | Core capability | Not the primary purpose | Not the primary purpose |
| Full-text autocomplete | Core capability | Not the primary purpose | Not the primary purpose | Not the primary purpose |
| Faceted search | Core capability | Core capability | Core capability | Core capability |
| Faceted autocomplete | Core capability | Limited, typically requires additional tooling | Not the primary purpose | Not the primary purpose |
| Cross-facet joins and relationship analysis | Core capability | Not the primary purpose | Core capability | Limited, typically requires additional tooling |
| Complex multi-dimensional analysis in a single query | Core capability | Not the primary purpose | Not the primary purpose | Limited, typically requires additional tooling |
| AI data exploration | Core capability | Not the primary purpose | Not the primary purpose | Not the primary purpose |
| Ad-hoc live filtering, multi-select | Core capability | Limited, typically requires additional tooling | Limited, typically requires additional tooling | Limited, typically requires additional tooling |
| Ad-hoc live relational analysis (joins, aggregations, correlations) | Core capability | Not the primary purpose | Limited, typically requires additional tooling | Limited, typically requires additional tooling |
| Ad-hoc live statistical analysis (mean, median, frequency, histogram, min/max) | Core capability | Limited, typically requires additional tooling | Limited, typically requires additional tooling | Limited, typically requires additional tooling |
| Ad-hoc live time-series analysis | Core capability | Limited, typically requires additional tooling | Not the primary purpose | Limited, typically requires additional tooling |
| Ad-hoc live anomaly / outlier detection | Core capability | Not the primary purpose | Not the primary purpose | Not the primary purpose |
| Real-time ingestion and data freshness | Core capability | Limited, typically requires additional tooling | Core capability | Limited, typically requires additional tooling |
| Agility for evolving dimensions and analytical patterns | Core capability | Limited, typically requires additional tooling | Not the primary purpose | Limited, typically requires additional tooling |
| Low operational overhead / minimal precomputation maintenance | Core capability | Limited, typically requires additional tooling | Core capability | Not the primary purpose |
Core capabilityLimited, typically requires additional toolingNot the primary purpose
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