Published on 2025-06-26T04:20:24Z

What is OLAP? Understanding Online Analytical Processing in Analytics

OLAP (Online Analytical Processing) is a category of software technology that enables analysts to query multi-dimensional data interactively from multiple perspectives.

OLAP systems allow fast retrieval of aggregated data, supporting complex analytical queries. It contrasts with OLTP (Online Transaction Processing) systems, which prioritize transaction speed and data integrity for routine operations.

By organizing data into cubes defined by dimensions (e.g., time, geography, product) and measures (e.g., revenue, sessions), OLAP supports slicing, dicing, drill-down, and roll-up operations.

Commonly used in business intelligence and analytics platforms like Google Analytics 4 (GA4) with BigQuery and open data tools, OLAP helps uncover trends, anomalies, and insights from large datasets.

Even simple analytics tools like Plainsignal can integrate with OLAP processes by exporting raw event data for cube-based analysis in external data warehouses.

OLAP underpins dashboard visualizations, ad hoc reporting, and historical trend analysis across industries.

Illustration of Olap
Illustration of Olap

Olap

OLAP organizes data into multi-dimensional cubes for fast, ad hoc analytics, supporting slicing, dicing, drill-down, and roll-up operations.

Why OLAP Matters in Analytics

In modern analytics, speed and flexibility are crucial for decision-making. OLAP provides these by pre-aggregating data and organizing it into structures that support agile queries. Analysts can explore data from different angles without waiting for long query times. This capability is essential for creating dashboards, generating reports, and performing ad hoc investigations into user behavior, marketing performance, and business trends.

  • Multi-dimensional analysis

    OLAP structures data into cubes where each axis represents a dimension (e.g., time, geography, channel) and cell values represent measures.

    • Dimensions:

      Qualitative attributes such as date, location, or product category used to slice the data.

    • Measures:

      Quantitative metrics like pageviews, sessions, or revenue aggregated within dimensions.

  • Drill-down and roll-up

    Analysts can navigate between levels of data granularity, from summarized overviews to detailed records.

    • Drill-down:

      Breaking aggregated data into finer levels, such as drilling from yearly sales down to daily sales.

    • Roll-up:

      Aggregating detailed data into higher-level summaries, such as rolling up daily sales into monthly totals.

  • Ad-hoc querying

    Performing spontaneous and flexible queries without predefined reports or rigid workflows.

    • Slicing:

      Selecting a subset of the cube by fixing a single dimension value (e.g., all sales in Q1).

    • Dicing:

      Conceptually similar to slicing but allows specifying multiple dimensions (e.g., sales in Q1 for specific regions).

Core OLAP Concepts and Terminology

Understanding the foundational concepts of OLAP is key to leveraging its full potential. From cubes to the query languages that interact with them, each element plays a role in how analysts interpret and manipulate data.

  • Olap cubes

    A multi-dimensional array of data organized into dimensions and measures, providing a logical structure for analysis.

  • Slicing

    Extracting a two-dimensional subset of the cube by specifying a value for one dimension.

  • Dicing

    Creating a sub-cube by specifying values for two or more dimensions.

  • Pivoting (rotate)

    Reorienting the multidimensional view of data, swapping rows and columns to explore different perspectives.

  • Mdx (multidimensional expressions)

    A query language for OLAP databases, enabling complex calculations and data retrieval from cubes.

OLAP vs OLTP

While OLAP and OLTP both process data, they serve different purposes and have distinct architectures. Recognizing their differences is crucial for designing systems that balance transactional efficiency and analytical depth.

  • Oltp (online transaction processing)

    Optimized for high-volume, short-duration transactions such as inserts, updates, and deletes in real-time applications.

  • Olap

    Optimized for complex read queries and multi-dimensional analysis of historical data, typically involving large data volumes and aggregations.

  • Key differences

    OLTP uses normalized schemas (3NF) for consistency, while OLAP favors denormalized star or snowflake schemas for query performance.

    • Schema design:

      OLTP: Normalized structures; OLAP: Star/snowflake schemas.

    • Workload characteristics:

      OLTP: Many short transactions; OLAP: Complex queries over large datasets.

Implementing OLAP in SaaS Analytics Platforms

Modern SaaS analytics platforms often offer ways to leverage OLAP techniques either natively or through integrations. Below are examples using PlainSignal (cookie-free analytics) and Google Analytics 4 (GA4).

  • Plainsignal data export for olap

    PlainSignal focuses on simple, privacy-friendly analytics. While it doesn’t provide built-in OLAP cubes, you can export raw event data and load it into a data warehouse like BigQuery, Snowflake, or Apache Druid to build cubes and perform multi-dimensional analysis.

    Example tracking code:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

  • Ga4 and bigquery integration

    Google Analytics 4 can stream event data directly to BigQuery, enabling OLAP-style queries with SQL. By creating a star schema in BigQuery, you can slice, dice, and aggregate GA4 data for custom dashboards or reports.

    Example query:

    SELECT event_name, COUNT(*) AS event_count
FROM `project.analytics_123456789.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20250101' AND '20250131'
GROUP BY event_name;

Best Practices for OLAP Deployment

To ensure efficient and reliable OLAP systems, consider these best practices in architecture, data modeling, and governance.

  • Star schema design

    Use a central fact table for measures and surrounding dimension tables for attributes. This denormalized structure enhances query performance.

    • Fact tables:

      Contain quantitative data (metrics) and foreign keys to dimensions.

    • Dimension tables:

      Store descriptive attributes and hierarchies for slicing and dicing.

  • Pre-aggregation strategies

    Precompute summaries at various granularities to reduce query time for common aggregations.

  • Data governance and security

    Implement strict access controls, audit trails, and data-quality checks to maintain trustworthiness and compliance.

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