By Tiger Data Team
Updated at Apr 13, 2026
Originally published on Nov. 28, 2024
Online analytical processing (OLAP) databases power the queries that drive business decisions, but choosing the right OLAP database is genuinely complicated. The category now spans traditional relational approaches, purpose-built columnar engines, cloud data warehouses, and PostgreSQL-native solutions.
This guide covers the main OLAP database types, how the modern landscape has changed the trade-offs, and a practical framework for deciding which option fits your workload. For a deeper explanation of what OLAP is and how it differs from OLTP, see this companion guide: Understanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQL.
OLAP databases are optimized for complex analytical queries over large datasets. They aggregate millions of rows, slicing data across multiple dimensions, and returning results fast enough to be useful in decision-making. They do this through a combination of columnar storage, pre-computed aggregations, and query engines optimized for read-heavy workloads.
Traditional OLAP systems organized data into structures called data cubes, multidimensional arrays of pre-aggregated data grouped by dimensions like time, geography, and product. Five operations define how users interact with a data cube:
Roll-up (aggregate to a higher level, such as daily to monthly)
Drill-down (navigate from summary to detail)
Slice (filter by one dimension)
Dice (filter across multiple dimensions)
Pivot (rotate the view to change which dimensions are rows and columns)
Modern systems have largely replaced physically stored cubes with columnar storage engines and vectorized query execution, which compute these operations on the fly without the maintenance overhead of pre-built cubes. But the conceptual model remains the same.
Understanding the three main OLAP architectures helps clarify why different products make different trade-offs.
MOLAP (multidimensional OLAP) systems store data in pre-calculated, multidimensional cube structures. Queries are fast because the aggregations already exist.
The trade-off is inflexibility. Cubes must be defined and built in advance, refresh cycles introduce latency, and storage requirements grow with the number of dimensions.
MOLAP works well for stable reporting workloads with predictable query patterns, but it's poorly suited to real-time analytics or ad-hoc exploration outside of pre-defined dimensions.
ROLAP (relational OLAP) systems store data in standard relational tables and translate analytical queries into SQL at runtime. This keeps everything in a familiar environment and integrates naturally with existing infrastructure.
However, performance is limited. Without specialized optimizations, relational databases can be slow on the complex aggregations and multi-dimensional scans OLAP demands.
-- ROLAP query example (standard SQL with ROLLUP)
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
ROLLUP (
dim_location.region,
dim_product.product_category);HOLAP (hybrid OLAP) combines both approaches: detailed data is stored relationally, while commonly queried aggregations are pre-computed and stored in multidimensional structures. This gives fast response times for typical queries while preserving flexibility for ad-hoc analysis.
Most modern analytical systems have converged on HOLAP-like architectures, using columnar storage for fast scans combined with materialized aggregates for frequently run queries.
The OLAP category looks significantly different today than it did a decade ago, thanks to three major shifts.
Real-time analytics has become a baseline expectation. Traditional OLAP relied on nightly or hourly extract, transform, and load (ETL) batch loads into a data warehouse. Modern applications, including IoT platforms, financial systems, and user-facing dashboards, need analytics to reflect data as it arrives, not hours later. This has driven demand for systems that can ingest continuously and query immediately.
The line between OLTP and OLAP has blurred. The old pattern was: write to a transactional database, ETL into a warehouse, and analyze there. That separation creates synchronization complexity, introduces latency, and doubles your infrastructure footprint. Hybrid systems that handle both transactional writes and analytical reads on the same data store have become a compelling alternative for many workloads.
Cloud data warehouses have matured, but come with cost and architectural trade-offs. Snowflake, Google BigQuery, and Amazon Redshift handle petabyte-scale workloads without infrastructure management. While their foundational architectures were historically built for batch-loaded data, all three have since introduced robust streaming ingestion APIs for real-time use cases. However, maintaining continuous stream ingestion and sub-second interactive querying on these platforms can significantly drive up compute costs and introduce pipeline complexity.
Three main approaches exist for implementing OLAP databases today: traditional ROLAP on a relational database, specialized OLAP databases, and extended relational databases. Each makes different trade-offs on performance, complexity, and integration.
Standard relational databases like PostgreSQL or MySQL can handle OLAP workloads through ROLAP. They store data in normalized tables and use SQL to run analytical queries. This approach requires no new systems or tooling and integrates naturally with existing data pipelines.
ROLAP works well for organizations with moderate analytical needs and datasets under 1 TB. Teams with strong SQL skills can query immediately without learning new interfaces. However, at scale, complex analytical queries requiring multiple joins and aggregations across large tables can run 10 to 50 times slower than specialized systems.
A regional retail chain analyzing 800 GB of sales data across 50 stores is a good fit. Analysts run overnight batch processes to combine point-of-sale data, inventory records, and customer data. The latency is acceptable, the tooling is familiar, and no new infrastructure is needed.
Purpose-built OLAP databases such as Apache Kylin and Apache Druid are designed from the ground up for analytical workloads. They use columnar storage, vectorized execution, and pre-aggregation strategies. Some use their own query interfaces optimized for multidimensional analysis.
-- Specialized OLAP query example (MDX syntax)
SELECT
{[Measures].[Sales]} ON COLUMNS,
{[Product].[Category].Members} ON ROWS
FROM [Sales_Cube]
WHERE [Region].[North_America];Specialized databases can run 100 times faster than ROLAP on complex analytical queries and scale to multiple petabytes. They make sense when data volumes are very large, query complexity is high, and your team has the engineering capacity to operate dedicated infrastructure.
However, the cost is real. These systems require separate infrastructure, new data pipelines, and, in many cases, new query languages or tooling. Teams need to build ETL processes to keep the analytical system in sync with transactional data, and the operational overhead of running two separate systems compounds over time.
A global streaming service or ad-tech platform ingesting millions of events per second, querying trillions of rows of clickstream data to power sub-second, highly-concurrent analytics dashboards that allow thousands of users to slice and dice performance metrics in real time, is a legitimate use case for a purpose-built OLAP system.
A third path has become increasingly viable by extending a general-purpose relational database with OLAP capabilities instead of maintaining a separate analytical system. This approach occupies the middle ground, meaningfully faster than standard ROLAP (typically 5 to 20 times) while staying in a familiar SQL environment, eliminating the need for an ETL pipeline between transactional and analytical stores.
-- Extended relational database query example
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;This is where the Tiger Data platform sits. PostgreSQL extended with Hypercore (a hybrid row-columnar storage engine), hypertables, and continuous aggregates delivers HOLAP-style architecture natively on PostgreSQL. New data is written to the rowstore for fast ingest and full ACID compliance. As data ages, Hypercore automatically moves it to the columnstore, compressing it by more than 90% and optimizing it for analytical scans. Continuous aggregates maintain pre-computed rollups that stay up to date as new data arrives, eliminating the need for batch jobs or a separate aggregation layer.
A streaming service analyzing 400 TB of viewer behavior data, combining historical viewing patterns with real-time engagement metrics and updating recommendations hourly, is a strong fit. The workload benefits from analytical optimizations, but the real-time requirement and SQL compatibility make a separate OLAP system harder to justify.
Criteria | ROLAP | Specialized OLAP | Extended relational |
Scaling | Limited beyond 1 TB | Excellent, up to multiple petabytes | Good, up to several TB or PB |
Performance on complex queries | 10–50x slower than specialized | Fastest; 100x+ faster than ROLAP | 5–20x faster than ROLAP |
Real-time ingest | Batch | Native | Native |
Transactional writes (ACID) | Yes | Limited | Yes |
Query language | Standard SQL | Varies; often custom | Standard SQL + extensions |
Setup time | Hours | Days to weeks | Hours to days |
Maintenance overhead | Low | High | Low to moderate |
Separate system required | No | Yes | No |
Open-source option | Yes | Yes | Yes (TimescaleDB) |
ROLAP is the right choice when your analytical needs are moderate, and your data volumes are manageable. If your team runs batch reports overnight, works with less than 1 TB of data, and has strong SQL skills with no appetite for new infrastructure, ROLAP on an existing relational database is the simplest path.
Specialized tools make sense when data volumes are very large, query complexity is high, and your team has the resources to operate dedicated infrastructure. If you're processing billions of events per day, need sub-second dashboards across dozens of dimensions at high concurrency, and have engineering capacity to manage a separate analytical platform, the performance payoff justifies the operational cost.
Extended relational databases are often the right choice for teams that have outgrown basic ROLAP but want to avoid the operational overhead of a separate OLAP system. They work particularly well for real-time analytics use cases, including IoT dashboards that need to reflect sensor data as it arrives, financial systems that combine live transactions with historical context, and operational reporting where analysts need current data rather than yesterday's batch.
If you're already on PostgreSQL, the path to extended analytical capabilities is particularly direct: no migration, no new query language, and no separate system to keep in sync.
Consider these questions when making your selection:
What is your current data volume and growth rate?
How complex are your analytical queries?
Do you need real-time or near-real-time analysis?
What is your team's technical expertise with database infrastructure?
What is your tolerance for operational complexity (separate systems, ETL pipelines, additional failure modes)?
Do your workloads require transactional guarantees alongside analytics?
Your answers will guide you toward the most suitable option. Teams that need real-time analytics with full transactional integrity on PostgreSQL, without the overhead of a separate analytical system, are the natural fit for an extended relational approach.
If you're evaluating PostgreSQL-based OLAP solutions, Tiger Cloud helps you explore what's possible on managed PostgreSQL with analytical extensions built in.
For self-hosted deployments, TimescaleDB, the open-source extension we build on, is available to install on any PostgreSQL instance.
Useful resources for going deeper:
Understanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQL: the companion guide covering OLAP fundamentals
Hypertables: time-partitioned tables for fast time-range queries
Continuous aggregates: pre-computed, automatically maintained OLAP aggregations
Hypercore: our hybrid row-columnar storage engine
PostgreSQL as a Real-Time Analytics Database: a deeper look at real-time analytics architecture on PostgreSQL
Try Tiger Cloud free and start running analytical queries on your PostgreSQL data today.
What is the best OLAP database?
There is no single best OLAP database. The right choice depends on your data volume, query complexity, real-time requirements, and operational capacity. Specialized systems like Apache Kylin and Apache Druid deliver the highest raw performance at large scale. Extended relational databases like PostgreSQL with Tiger Data offer a strong balance of analytical performance, SQL familiarity, and operational simplicity, particularly for workloads that combine real-time ingest with transactional integrity. Standard ROLAP is sufficient for modest data volumes with batch reporting needs.
What is the difference between MOLAP, ROLAP, and HOLAP?
MOLAP (multidimensional OLAP) stores pre-calculated data cube structures for fast query responses but requires upfront aggregation and doesn't handle real-time data well. ROLAP (relational OLAP) stores data in relational tables and translates OLAP queries into SQL at runtime, keeping everything in a familiar environment at the cost of performance at scale. HOLAP (hybrid OLAP) combines both: relational storage for detailed data and pre-computed aggregations for common queries. Most modern analytical systems implement HOLAP-like architectures using columnar storage with materialized aggregates.
What is the difference between OLAP and OLTP?
OLTP (online transaction processing) databases are optimized for high-throughput, low-latency operations on individual rows: inserting orders, updating customer records, processing payments. OLAP databases are optimized for complex aggregations over large datasets: summing revenue by region and quarter, analyzing trends across millions of events. Traditional architectures kept these separate, but modern extended relational databases like Tiger Data’s TimescaleDB handle both, eliminating the synchronization overhead between transactional and analytical systems.
Can PostgreSQL handle OLAP workloads at scale?
Standard PostgreSQL is not optimized for large-scale OLAP workloads. With extensions like TimescaleDB, however, PostgreSQL can handle analytical queries at terabyte to petabyte scale through columnar storage via Hypercore, time-partitioned hypertables, and continuous aggregates that maintain pre-computed rollups automatically. For workloads that need both transactional writes and analytical reads on the same data, this approach outperforms architectures that require a separate analytical system.
When should I choose a dedicated OLAP database over an extended relational database?
A dedicated OLAP database makes sense when you're processing multiple petabytes of purely analytical data, need sub-second responses on extremely complex multi-dimensional queries at very high concurrency, and have engineering resources to operate separate infrastructure. For most teams handling terabytes rather than petabytes, with real-time analytics requirements, ACID compliance needs, and a preference for SQL, extending PostgreSQL is simpler to operate and fast enough for the workload.