Modern OLAP

Online Analytical Processing (OLAP) enables users to explore complex datasets through multi-dimensional queries that provide fast, interactive insights. By organizing data into dimensions and measures, OLAP allows businesses to easily slice, dice, and pivot through their data to answer key questions. However, OLAP’s journey has been anything but static—it has evolved significantly from its early days of precomputed cubes to today’s dynamic, real-time systems that deliver fresh, actionable insights.

The transformation of OLAP databases mirrors the broader evolution of data technologies, from rigid, pre-aggregated models to flexible, scalable architectures capable of handling modern big data challenges. Today, OLAP has shifted from its original focus on “online” access to delivering “Optimized Live Analytic Processing,” enabling real-time decision-making across industries.

A brief history of the OLAP database: Early success with built-in limitations

In the 1990s, OLAP revolutionized how businesses analyzed data, offering a structured way to query large datasets from back-office systems. The technology relied on OLAP cubes, which were precomputed aggregations of data designed to deliver fast, predictable query performance.

These OLAP cubes allowed businesses to answer predefined queries instantly by storing aggregated results ahead of time. However, this model had significant limitations:

• Data freshness issues: OLAP cubes were typically built on a weekly schedule, meaning any new data that arrived after the cube was built wouldn’t be reflected until the next update. In fast-moving industries, this delay often made insights outdated and less useful.
• Storage costs: To support both detailed and summarized queries, OLAP systems had to store both raw data and fully aggregated cubes, leading to high storage overhead.
• Lack of granularity: Once data was aggregated into a cube, drilling back down into the underlying details wasn’t possible. If a user wanted to explore the raw data behind an aggregate, they would need to rebuild the cube or query the original dataset—an impractical process at scale.

While first-generation OLAP systems provided a solution for their time, these limitations became increasingly problematic as data volumes exploded.

The decline of traditional Online Analytical Processing in the big data era

As big data technologies emerged, traditional OLAP databases began to fall out of favor. The limitations of static, precomputed cubes became more apparent in an era where data was growing exponentially and new use cases required more flexibility.

Companies found that rigid OLAP architectures couldn’t keep pace with unstructured and fast-evolving datasets. Instead, many organizations shifted to data lakes and distributed query engines that could handle larger volumes of data with greater flexibility, albeit at the cost of slower query performance.

The leading OLAP tools of the 1990s, such as Hyperion Essbase and Cognos PowerPlay, became legacy technologies.

The OLAP renaissance: A new era of real-time insights

The past decade has seen a resurgence of OLAP databases as businesses demand real-time analytics to support fast decision-making. The key to this resurgence has been technological innovations that address the shortcomings of traditional Online Analytical Processing while maintaining its strengths in fast, interactive querying.

One major advancement is the use of materialized views, which provide precomputed results that speed up query performance without the rigidity of traditional cubes. Additionally, the concept of partial caching has revolutionized how data is stored and queried in modern OLAP systems.

Intelligent partial aggregations: A smarter approach to aggregation

Traditional OLAP systems relied heavily on fully precomputed data cubes, where data was aggregated across multiple dimensions to improve query performance. While effective for answering predefined queries quickly, this approach sacrificed granularity and increased storage costs, as it required storing large volumes of aggregated data.

Modern OLAP systems have evolved to use intelligent partial aggregations, a more dynamic and efficient method for optimizing query performance. Rather than precomputing and storing all possible combinations of dimensions, intelligent partial aggregations focus on summarizing the dimensions that provide the most performance benefit—those with high cardinality or large row counts. Dimensions that yield little performance improvement when aggregated are left untouched, saving both storage space and CPU costs.

For example, consider a flight dataset with routes between various origins and destinations. Popular routes like New York to Los Angeles generate large volumes of data, with thousands of flights per month, while less popular routes, such as Dallas to Waco, generate far fewer records.

With intelligent partial aggregations, the system would precompute aggregations for high-volume routes like New York to Los Angeles to quickly answer queries such as “How many flights occurred between NYC and LA last month?”. For lower-volume routes like Dallas to Waco, it’s more efficient to process those records on the fly since the number of rows to scan is relatively small.

This targeted aggregation approach allows the system to reduce query load and improve performance for frequent queries on high-volume data, while avoiding unnecessary storage and computation for less frequent queries. The result is a more efficient and cost-effective analytics environment that delivers predictable query performance across diverse datasets.

Key use cases driving modern OLAP adoption

Modern OLAP systems are increasingly being adopted across industries to power both customer-facing applications and internal operational tools, where real-time insights are critical for delivering value at scale.

Customer-facing apps

Many organizations rely on modern OLAP systems to deliver real-time insights directly to their end users through interactive, data-driven applications. These apps require subsecond query performance to provide dynamic, up-to-date information that enhances the user experience.

• Live performance tracking: Platforms in gaming and social media use Online Analytical Processing to provide real-time leaderboards, engagement stats, and social interactions like likes, views, and comments. These live insights help keep users engaged by giving them immediate feedback on their activity.
• Merchant analytics: B2B platforms, such as payment processors and e-commerce platforms, use OLAP to provide real-time analytics dashboards to their customers. These dashboards offer key performance metrics like transaction volumes, revenue trends, and user behavior analytics, helping merchants optimize their businesses with actionable insights.

Internal apps

In addition to powering external user experiences, modern OLAP systems are widely used within organizations to drive internal operational efficiency by providing live metrics and operational insights across various functions.

• Live metrics monitoring: Internal teams rely on OLAP-powered observability dashboards to monitor system performance in real time, detect anomalies, and address issues before they escalate. This is especially critical in IT operations and cloud infrastructure management.
• Operational insights: Industries such as logistics and retail use OLAP for real-time supply chain tracking and inventory management. These tools provide up-to-the-minute updates on stock levels, order status, and potential bottlenecks.
• Digital media optimization: Advertising and digital media companies use OLAP systems to optimize ad performance by tracking impressions, clicks, and conversions in real time. This enables them to adjust campaigns on the fly for better engagement and ROI.
• Fraud detection and intervention: Financial institutions use OLAP to identify fraudulent activity by analyzing real-time transaction patterns. OLAP’s speed allows organizations to detect anomalies quickly and take immediate action, minimizing financial losses and risks.

By delivering fast, flexible analytics for both customer-facing and internal applications, modern OLAP systems help organizations improve user experiences, optimize operations, and make better, real-time decisions across a variety of use cases.

Apache Pinot: A modern OLAP solution for real-time analytics

One of the most prominent modern OLAP systems is Apache Pinot, a real-time distributed data store that excels at multi-dimensional time-series analysis.

Unlike traditional OLAP systems that required static cubes, Pinot enables users to analyze data across multiple dimensions in real time, providing subsecond query performance on high-cardinality datasets.

For example, a business could use Pinot to analyze sales trends and quickly pivot to view those trends in different currencies, regions, or product categories. This kind of multi-dimensional analysis is essential for businesses operating in fast-moving markets.

The future of OLAP: From static cubes to dynamic insights

The revival of Online Analytical Processing is being driven by the need for real-time, interactive analytics in today’s data-driven world. Modern OLAP systems like Apache Pinot have evolved to address the shortcomings of their predecessors by focusing on real-time processing, partial caching, and multi-dimensional analysis.

From its static beginnings to its dynamic renaissance, OLAP continues to be a critical tool for businesses looking to unlock actionable insights from their data. With modern innovations, it’s clear that OLAP is here to stay, adapting to meet the demands of real-time decision-making in a world where data is constantly in motion.