OLAP (online analytical processing) forms a core part of data analysis systems. Organizations deal with increasing data volumes that require fast analytical processing for business intelligence and decision-making. The choice of an OLAP database directly impacts query performance, scalability, and integration capabilities with existing infrastructure.
The question is, which OLAP database should you choose? Several OLAP database options exist in the market today. Each offers different trade-offs between performance, ease of use, and integration capabilities. Some leverage traditional relational database systems, while others use specialized architectures built specifically for analytical workloads.
In this blog post, we look at the main types of OLAP databases:
Traditional relational databases configured for OLAP workloads
Purpose-built OLAP database systems
Hybrid solutions that extend relational databases
You'll learn the key technical characteristics of each option, compare their performance characteristics, and identify which scenarios best suit each architecture. We hope this will help you select the optimal OLAP database for your requirements.
Let's start by understanding what makes OLAP databases different from traditional transactional databases.
What Is an OLAP Database?
OLAP databases are specialized systems for storing and analyzing large volumes of multidimensional data. Unlike traditional relational databases that organize data in two dimensions (rows and columns), OLAP databases use data cube structures that support three or more dimensions.
A data cube extends beyond simple table structures by adding dimensions that represent different data attributes. For example, a sales data cube might include dimensions for time, product, location, and customer segments. This multidimensional structure enables fast analytical queries across multiple data attributes simultaneously.
Data cube architecture
The data cube forms the foundation of OLAP systems. Take a retail sales database as an example:
A traditional table shows sales by product and date
A data cube adds dimensions like store location, customer demographics, and promotional campaigns
Each cell in the cube contains measures like revenue, units sold, and profit margins
This structure pre-calculates aggregations at different dimensional levels, making complex analytical queries much faster than computing them on demand from raw data.
Core OLAP operations
OLAP databases provide five key operations for data analysis:
Roll-up: Aggregate data at higher levels of a dimension hierarchy. For example, rolling up daily sales into monthly totals or city-level data into regional summaries. This helps identify high-level trends.
Drill-down: Navigate from summary data to detailed records. This lets you examine specific data points that contribute to aggregate values. For instance, breaking down quarterly sales into individual transactions.
Slice: Filter data along a single dimension, creating a subset of the data cube. Like viewing sales data for a specific period or focusing on a particular product category.
Dice: Apply filters across multiple dimensions simultaneously. For instance, analyzing sales for specific products in particular regions during selected months. This creates focused data subsets for detailed analysis.
Pivot: Reorganize the cube visualization by rotating dimensions. This provides different analytical perspectives on the same dataset. You might switch from a time-based view to a geography-based view while maintaining the same underlying data.
These operations enable complex analytical queries that would be computationally expensive in traditional relational databases. The data cube structure optimizes these operations for faster query response times.
For example, here's how the data looks in a traditional table format:
Product
Region
Q1 Sales
Q2 Sales
Q3 Sales
Q4 Sales
Laptops
North
100,000
120,000
95,000
150,000
Laptops
South
85,000
90,000
88,000
120,000
Phones
North
250,000
280,000
290,000
350,000
Phones
South
220,000
225,000
240,000
300,000
Now, let's visualize this as a data cube:
OLAP Database Options
Three main approaches exist for implementing OLAP databases: using traditional relational databases (ROLAP), adopting specialized OLAP tools, or extending existing databases with OLAP capabilities. Each approach offers distinct trade-offs regarding implementation complexity, performance, and integration capabilities.
Relational database for OLAP (ROLAP)
ROLAP systems implement OLAP functionality using standard relational databases like PostgreSQL or MySQL. These systems store multidimensional data in relational tables and translate OLAP operations into SQL queries. The system dynamically generates SQL statements to handle analytical operations, making it accessible to teams with SQL expertise.
-- ROLAP query example (Standard SQL)
SELECT
dim_location.region,
dim_product.product_category,
SUM(sales_facts.sales_amount) as total_sales
FROM sales_facts
JOIN dim_product
ON sales_facts.product_id = dim_product.id
JOIN dim_location
ON sales_facts.location_id = dim_location.id
GROUP BY
dim_location.region,
dim_product.product_category
WITH ROLLUP;
ROLAP systems excel in environments where teams already have strong SQL expertise and existing relational database infrastructure. Organizations can leverage their current database investments and familiar tools for analytical processing. This approach minimizes initial training needs and allows immediate deployment using existing infrastructure.
However, ROLAP systems face significant performance challenges at scale. Complex analytical queries often require multiple table joins and aggregations, which can become resource-intensive. Real-time analytics on large datasets may experience latency issues due to the overhead of translating multidimensional operations into relational queries.
Specialized OLAP databases
Purpose-built OLAP databases like Apache Kylin or Druid implement native support for multidimensional data structures and OLAP operations. These systems store data in optimized formats specifically designed for analytical processing and provide dedicated query languages for multi-dimensional analysis.
-- Specialized OLAP query example (Using MDX syntax)
SELECT
{[Measures].[Sales]} ON COLUMNS,
{[Product].[Category].Members} ON ROWS
FROM [Sales_Cube]
WHERE [Region].[North_America];
Specialized OLAP databases deliver superior query performance through built-in optimizations for analytical workloads. They efficiently handle large data volumes using specialized storage formats and pre-aggregation strategies. These systems excel in environments requiring complex analytical processing, offering features like real-time cube updates and advanced caching mechanisms.
Integration with existing systems presents a notable challenge for specialized OLAP databases. Teams must invest time in learning new query languages and implementation patterns. The infrastructure requirements often differ from traditional databases, necessitating additional operational expertise and management overhead.
Extended relational databases
Database extensions like TimescaleDB for PostgreSQL enhance traditional relational databases with OLAP capabilities for subsets of OLAP workloads, like real-time analytics. Tracking stock prices, monitoring IoT data or analyzing user behavior? Your database needs to support real-time decisions, with millisecond query response times, by combining live data with historical context. These real-time analytics are typically delivered to you or your customers via dashboards or purpose-built applications.
This hybrid approach maintains SQL compatibility while adding optimizations for analytical workloads. Extensions typically add specialized data types, indexing methods, and query optimizations targeted at analytical processing.
-- Extended Database query example (TimescaleDB)
SELECT
time_bucket('1 day', timestamp) AS day,
product_category,
region,
SUM(sale_amount) as total_sales
FROM sales_timeseries
GROUP BY
day,
product_category,
region
ORDER BY day DESC;
Extended databases strike a balance between familiarity, flexibility, and performance. Development teams can continue using standard SQL—the lingua franca for data analysis—while gaining access to OLAP-specific optimizations. These systems integrate smoothly with existing data pipelines and tools, minimizing disruption to established workflows.
Extended databases typically have performance characteristics between ROLAP and specialized solutions. While they may not match the peak performance of dedicated OLAP systems, they significantly outperform standard ROLAP implementations for analytical workloads, offering greater versatility and a better developer experience.
Integration considerations
Each approach requires different levels of integration effort:
ROLAP systems integrate naturally with existing data warehouses and ETL (extract-load-transform) processes. They use standard database protocols and tools, making them straightforward to incorporate into existing architectures.
Specialized OLAP databases often require new data pipelines and integration patterns. Teams must implement ETL processes specific to the chosen platform and may need to modify existing applications to work with new query interfaces.
Extended databases maintain compatibility with standard database tools while adding OLAP capabilities. This allows you to gradually adopt OLAP features without significant architectural changes. Existing monitoring, backup, and administration tools continue to function, reducing operational overhead.
The choice between these options depends on specific requirements for query performance, data volume, integration needs, and team expertise. When selecting an OLAP database solution, you must evaluate your existing infrastructure, performance requirements, and development team capabilities.
Evaluating the Options
When selecting an OLAP database, consider three key aspects: scaling capabilities, query performance, and usability. Here's a detailed comparison across these dimensions:
Criteria
ROLAP
OLAP Specific Tools
Relational Extensions
Scaling
Limited scaling beyond 1 TB. Performance degrades with data growth.
Excellent scaling to multiple petabytes. Built-in data partitioning.
Good scaling up to several TB or PB. Depends on base database capabilities.
Performance
10-50x slower than specialized tools for complex queries. Good for simple aggregations.
Fastest query performance. 100x+ faster for complex analytical queries.
5-20x faster than ROLAP. Near-specialized performance for most queries.
Usability
High. Uses standard SQL. Works with existing tools.
Moderate. Requires learning new query languages. Custom tools needed.
High. SQL-based with additional syntax for OLAP features.
Setup Time
Hours
Days to weeks
Hours to days
Maintenance
Low
High
Low to Moderate
Use Case Recommendations
ROLAP scenarios
ROLAP solutions work best for organizations with moderate analytical needs and existing database expertise. These systems shine when data volume remains under 1 TB and the focus is on basic data aggregation and analysis. Teams with strong SQL skills can leverage their existing knowledge without learning new query languages or systems.
A typical ROLAP implementation might support a retail chain's monthly sales analysis. The system processes daily transaction data, generating reports that combine product categories, store locations, and time periods. With 24-hour refresh cycles, analysts can track key metrics like revenue trends, inventory turnover, and seasonal patterns across different store locations.
Real-world example: A regional supermarket chain uses ROLAP to analyze 800 GB of sales data across 50 stores. Their analysts run overnight batch processes to update sales reports, combining data from point-of-sale systems, inventory management, and customer loyalty programs.
Specialized OLAP tools
Organizations handling massive data volumes and requiring complex real-time analytics benefit most from specialized OLAP tools. These systems excel when processing multiple terabytes of data with requirements for sub-second query responses. They're particularly valuable for organizations with dedicated teams capable of managing specialized infrastructure.
Consider an e-commerce platform's recommendation engine. The system processes billions of user interactions daily, analyzing purchase patterns, browsing behavior, and inventory data in real time. Business intelligence dashboards provide instant insights across dozens of dimensions, from customer demographics to product attributes.
Real-world example: A global online marketplace uses specialized OLAP tools to process 10 PB of daily user interaction data. Their system analyzes customer behavior across 200 million products, providing real-time recommendations and fraud detection across 40 countries.
Relational extensions
Extensions offer an ideal middle ground for organizations outgrowing traditional ROLAP but not ready for fully specialized solutions. These systems can effectively handle petabytes of data while maintaining familiar SQL-based workflows. They're particularly effective when organizations need to combine real-time analytical processing with regular transaction handling and may offer convenient solutions to manage irregular sampling intervals or outages.
Take a subscription-based service provider analyzing customer behavior. Their system tracks user engagement metrics, payment patterns, and service usage, updating customer segments hourly. Marketing teams use this data to identify churn risks and upsell opportunities, while product teams monitor feature adoption rates.
Real-world example: A streaming service uses relational extensions to analyze 400 TB of viewer behavior data. Their platform combines historical viewing patterns with real-time engagement metrics, processing hourly updates to optimize content recommendations and advertising placements.
Decision factors
Consider these questions when making your selection:
What's your current data volume and growth rate?
How complex are your analytical queries?
Do you need real-time analysis?
What's your team's technical expertise?
What's your infrastructure budget?
Your answers to these questions will guide you toward the most suitable OLAP solution for your needs.
Conclusion
OLAP databases play a vital role in data analytics, providing sophisticated tools for multidimensional data analysis. Through data cube structures and specialized query operations, these systems enable you to extract meaningful insights from complex datasets efficiently.
The market offers three distinct paths for OLAP implementation. Traditional ROLAP systems provide a straightforward entry point for teams with existing SQL expertise and moderate data volumes. Specialized OLAP databases deliver unmatched performance for large-scale analytics but require dedicated resources and expertise.
Relational extensions like TimescaleDB bridge the gap, offering enhanced real-time analytical capabilities while maintaining the familiarity of traditional databases. It enables organizations to handle high-frequency IoT data ingestion, store terabytes or petabytes of historical data, and serve sub-second analytics queries on billions of rows—all with familiar SQL syntax.
Performance differences demonstrate that specialized OLAP databases excel at complex analytical workloads, while extensions like TimescaleDB deliver comparable performance for most common scenarios. These extensions combine PostgreSQL's reliability with optimized OLAP capabilities, making them an attractive option for growing organizations.
Your choice of OLAP solution should align with your specific needs:
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