Online Analytical Processing (OLAP)

In depth

OLAP sits at the heart of modern analytics. Unlike traditional row-based databases, OLAP stores data in multidimensional structures—often called cubes—that pre-aggregate metrics along various dimensions (for example, time, geography, or product).

There are three main OLAP architectures:

• MOLAP (Multidimensional OLAP): Data is stored in proprietary, optimized cube files for rapid query performance.

• ROLAP (Relational OLAP): Leverages a relational database to compute aggregates on the fly, offering flexibility at scale.

• HOLAP (Hybrid OLAP): Combines MOLAP and ROLAP to balance storage efficiency and query speed.

When you run an OLAP query, the engine accesses pre-calculated aggregates or dynamically computes them, depending on the architecture. This design supports complex analyses—like year-over-year growth comparisons or top-performing regions—in milliseconds rather than minutes.

Pro tip

When designing OLAP cubes, start with your key business metrics and choose dimensions that align with decision-making priorities. Too many dimensions can slow performance; focus on the most critical slices and add others as needed.

Why it matters

For business leaders and data teams, OLAP delivers:

• Faster insights: Pre-aggregated data means queries return results almost instantly, empowering timely decisions.

• Consistent metrics: A single cube enforces uniform definitions (for example, what counts as “revenue”), reducing confusion across reports.

• Self-serve analytics: Business users can explore data without writing SQL, freeing data teams to focus on strategy.

OLAP - In practice

Imagine a retail manager who wants to monitor weekly sales by store, product category, and customer segment. With OLAP:

1. The data team builds a sales cube with measures (units sold, total revenue) and dimensions (region, date, category).

2. The manager uses a drag-and-drop interface to slice by region, filter on high-value customers, or drill down from quarterly to daily figures.

3. New insights—like a surge in accessory sales after a promotion—appear in seconds, enabling quick action.

Product-specific notes

In PowerMetrics, the semantic layer and Metric Catalog act like an OLAP engine: they store definitions, handle aggregations, and let you explore data across dimensions without complex queries. Use PowerMetrics AI to ask natural-language questions against your OLAP-style metrics for faster, self-serve insights.