OLAP databases: what's new and what's best in 2025

Choosing an OLAP database in 2025 means navigating a landscape that changed significantly over the past year, with new capabilities like vector search, lakehouse formats, and serverless ingestion becoming standard features. The "best" database depends on your specific workload: query latency requirements, data volume, ingestion patterns, and whether you're building internal dashboards or customer-facing analytics APIs.

This article explains what OLAP databases do, covers the technical innovations that emerged in 2025, compares the leading open source and managed options, and provides a practical framework for evaluating which solution fits your use case.

What is an OLAP database and why does it matter?

OLAP stands for Online Analytical Processing, a type of database built specifically for running complex queries across large datasets. When you want to answer questions like "What was revenue by region last quarter?" or "Which user cohorts show the highest retention?", you're doing analytical work that traditional transactional databases weren't designed to handle efficiently.

The difference comes down to storage architecture. Row-oriented databases like PostgreSQL store each record as a continuous block, which works well when you're fetching or updating individual records. Columnar databases store each column separately, so queries that scan or aggregate specific columns can skip irrelevant data entirely. This makes columnar databases 10x to 100x faster for analytical queries. For a comprehensive comparison of PostgreSQL and ClickHouse across storage models, performance, and use cases, see our ClickHouse vs PostgreSQL comparison.

Columnar storage vs row storage

Think about reading the revenue column from a billion-row table. In a row-oriented database, you have to scan every single row even though you only care about one field. Column-oriented databases store all revenue values together in compressed blocks, so scanning that column touches far less disk and memory.

This matters most when your queries aggregate or filter on a small subset of columns. A query like SELECT AVG(price) FROM orders WHERE date > '2024-01-01' only reads the price and date columns, not customer names, addresses, or other metadata that might be stored in the same rows. This enables queries to run in 2 seconds instead of 42 compared to traditional row-based systems.

Common OLAP query patterns

OLAP databases excel at three main patterns: aggregations (sums, averages, counts), time-series analysis (trends over hours or days), and multi-dimensional slicing (breaking down metrics by region, product, or user segment). You'll see patterns like this in dashboards, business intelligence tools, and real-time analytics APIs.

A typical OLAP query might calculate daily active users grouped by country and device type over 90 days. This requires scanning millions of events, filtering by timestamp, and grouping by multiple dimensions.

OLAP database examples in production

Real-world applications include business intelligence platforms where analysts explore sales data, real-time dashboards showing website traffic or API latency, and log analytics systems that aggregate server metrics. Companies also use OLAP databases to power customer-facing analytics, like showing users their usage statistics or generating on-demand reports.

What changed in OLAP software in 2025?

The OLAP landscape shifted significantly in 2025, with technical innovations that distinguish modern systems from older data warehouses. The changes reflect growing demand for real-time analytics, AI integration, and developer-friendly infrastructure.

Vector search inside OLAP engines

ClickHouse® and other modern OLAP databases now support vector similarity search directly within the analytical engine, with ClickHouse achieving production-ready vector search in version 25.8.

For example, you can find the top 10 products most similar to a given item based on embedding vectors, then join that result with sales data to calculate revenue by similar product category. This eliminates the need for separate vector databases.

Iceberg and lakehouse table formats

Open table formats like Apache Iceberg, Delta Lake, and Hudi became widely adopted in 2025. These formats allow multiple query engines to read and write the same data without vendor lock-in, providing ACID transactions, schema evolution, and time travel on top of object storage like S3 or GCS.

You can write data with Spark, query it with ClickHouse®, and run machine learning jobs with Python, all using the same underlying tables. The lakehouse architecture blurs the line between data lakes and data warehouses.

Separation of storage and compute becomes default

Most modern OLAP databases now separate storage and compute by default rather than tightly coupling them in a single cluster. This architecture lets you scale query capacity independently from data volume, which reduces costs and improves elasticity.

You can spin up additional compute nodes during peak query hours, then scale back down when traffic drops, without moving or replicating data. Storage remains on object stores like S3, which are cheaper and more durable than local disks.

Serverless ingestion pipelines

Managed OLAP services increasingly offer serverless ingestion that handles streaming data from sources like Kafka, Kinesis, or webhooks without requiring you to configure or maintain ingestion infrastructure. This eliminates the operational burden of managing connectors, monitoring backpressure, and tuning batch sizes.

In practice, developers can start ingesting clickstream events or log data by pointing a configuration at their data source rather than writing and deploying custom ingestion code.

Open source OLAP databases to watch

ClickHouse

ClickHouse is an open source columnar database known for extremely fast query performance, even on datasets with billions of rows. It uses vectorized query execution, aggressive compression, and sparse indexing to deliver sub-second latency on complex aggregations.

ClickHouse supports standard SQL with extensions for arrays, nested data structures, and time-series functions. It scales horizontally across clusters and integrates with common data sources through native connectors. Many large-scale production deployments run on ClickHouse, including web analytics platforms, observability systems, and real-time dashboards.

Apache Druid

Druid specializes in streaming ingestion and time-series analytics, with built-in support for real-time data pipelines from Kafka or Kinesis. It offers elastic scalability through a distributed architecture that separates ingestion, querying, and storage into independent services.

Druid excels at high-concurrency workloads where many users or applications query the same dataset simultaneously. Its indexing strategy optimizes for filtering and grouping on time ranges and categorical dimensions. For a comprehensive comparison of Druid and ClickHouse for real-time analytics, see our ClickHouse vs Druid guide.

Apache Pinot

Pinot was originally built at LinkedIn for user-facing analytics that require ultra-low latency at massive scale. It supports both batch and streaming ingestion, with features like adaptive indexing and tiered storage that balance query speed with cost.

Pinot works well for applications that serve analytics directly to end users, such as showing a user their activity history or generating personalized reports. Query latency typically stays under 100 milliseconds even with high concurrency.

StarRocks

StarRocks is a columnar data warehouse built for real-time analytics with MySQL wire protocol compatibility. It uses a vectorized execution engine and cost-based optimizer to deliver fast query performance on both batch and streaming data.

StarRocks supports materialized views, partitioning, and distributed joins, making it suitable for complex analytical workloads that involve multiple large tables. MySQL compatibility makes it easier for teams already familiar with MySQL to adopt an OLAP database.

DuckDB for embedded analytics

DuckDB is an in-process analytical database that runs inside your application, similar to how SQLite works for transactional workloads. It requires no separate server or configuration, making it ideal for local analysis, data pipelines, or embedding analytics into desktop or mobile applications.

DuckDB reads data directly from Parquet files, CSV files, or data frames in Python or R, without requiring a separate data loading step. This makes it particularly useful for data scientists and analysts who want to run SQL queries on local files.

Up and comers to watch

Polars is a Rust-based DataFrame library that offers fast query execution with a Python API similar to pandas.

Apache Doris is an MPP database that provides high concurrency and low latency for real-time analytical queries. It supports both batch and streaming ingestion with automatic data compaction and indexing.

QuestDB focuses on time-series data with optimized ingestion performance that can handle millions of rows per second on a single node. It uses SQL with extensions for time-based operations.

Hydra is a distributed SQL query engine built on columnar storage with automatic query optimization.

Full list of emerging open source OLAP databases

The open source OLAP ecosystem continues to expand rapidly, with new projects emerging and existing ones gaining significant traction. Here's a comprehensive list of databases worth watching:

Established and Growing:

• ClickHouse - Columnar database with vectorized execution and extreme performance
• Apache Druid - Real-time analytics with streaming ingestion and time-series optimization
• Apache Pinot - Ultra-low latency OLAP for user-facing analytics at scale
• StarRocks - MPP database with MySQL compatibility and lakehouse integration
• DuckDB - Embedded analytical database for local and cloud analytics
• Apache Doris - Unified analytics database with real-time and batch processing

Rising Stars:

• Databend - Cloud-native data warehouse built in Rust with elastic scaling
• Oxla - Self-hosted data warehouse optimized for low-latency analytics
• QuestDB - Time-series database with high-performance ingestion
• Hydra - Distributed SQL engine with automatic query optimization
• MatrixOne - Distributed OLAP database for real-time analytics
• ChDB - Embedded OLAP engine powered by ClickHouse

Specialized Solutions:

• Polars - Rust-based DataFrame library with Python/R APIs
• Apache Kylin - OLAP engine for big data with sub-second query latency
• ParadeDB - PostgreSQL-based database with search and analytics features
• HeavyDB (formerly OmniSciDB) - GPU-accelerated analytics database
• RisingWave - Cloud-native streaming database for real-time analytics
• Materialize - Streaming database for real-time applications

Query Engines and Frameworks:

• Apache Arrow DataFusion - Query engine with Arrow in-memory format
• Trino - Distributed SQL query engine for data lakes
• Presto - Distributed SQL query engine for big data
• Greenplum - Advanced data warehouse with cost-based optimization

Embedded and Lightweight:

• SQLite with extensions - Lightweight OLAP capabilities
• DuckDB - In-process analytical database
• ChDB - Embedded ClickHouse engine

This diverse ecosystem reflects the growing demand for specialized analytical databases, with each solution optimized for different use cases ranging from real-time streaming analytics to embedded applications and large-scale data warehousing.

Managed OLAP services that cut ops work

Managed services eliminate the infrastructure work required to run OLAP databases in production. Platforms like these handle cluster provisioning, scaling, upgrades, and monitoring, allowing developers to focus on queries and data pipelines rather than DevOps.

For a detailed comparison of ClickHouse Cloud and its alternatives, including Tinybird, Altinity, and Aiven, see our comprehensive ClickHouse Cloud alternatives guide.

ClickHouse Cloud

ClickHouse Cloud is the official managed service from the creators of ClickHouse. It offers automatic scaling, built-in replication, and integration with the broader ClickHouse ecosystem. The service handles cluster sizing, version upgrades, and performance tuning automatically.

Tinybird

Tinybird is a managed ClickHouse platform designed specifically for developers who want to integrate real-time analytics into their applications. It provides streaming ingestion, SQL-based data pipelines defined as code, and automatic API generation from SQL queries.

Developers can test locally with the Tinybird CLI, then deploy to production with a single command. The platform handles scaling, observability, and security, which reduces the time from idea to production API from weeks to hours.

Aiven for ClickHouse

Aiven offers managed ClickHouse with multi-cloud support across AWS, GCP, and Azure. It includes enterprise features like VPC peering, private networking, and compliance certifications. Aiven's platform also provides managed services for Kafka, PostgreSQL, and other data infrastructure.

Firebolt

Firebolt is a cloud-native data warehouse built on a custom engine that separates storage and compute. It uses sparse indexing and automatic query optimization to deliver fast performance on large datasets. For a detailed comparison of Firebolt and ClickHouse across architecture, performance, and operational considerations, see our ClickHouse vs Firebolt comparison.

MotherDuck for DuckDB

MotherDuck extends DuckDB to the cloud, allowing analysts to run queries on data stored in cloud object storage without managing infrastructure. It maintains DuckDB's simplicity while adding cloud-scale storage and collaboration features.

How to pick the best database for OLAP use cases

Choosing an OLAP database requires evaluating several technical and operational factors. The right choice depends on your specific workload characteristics, team skills, and long-term requirements.

Data volume and retention needs

Start by estimating your data volume and growth rate. If you're ingesting gigabytes per day and querying recent data, a single-node ClickHouse instance or DuckDB might suffice. If you're dealing with terabytes of historical data that you query across months or years, you'll want a distributed system with efficient compression and tiered storage.

Retention policies also matter. Some OLAP databases handle time-based partitioning and data expiration automatically, while others require manual configuration.

Query latency and concurrency targets

Different OLAP databases optimize for different query patterns. If you want sub-second latency for user-facing dashboards with high concurrency, systems like Pinot or ClickHouse work well. If you're running batch reports overnight or ad-hoc analysis with lower concurrency, engines like Spark SQL or Trino might be more cost-effective.

Think about whether your queries are simple aggregations over time ranges or complex joins across multiple large tables. Some engines excel at simple queries with high throughput, while others handle complex joins more efficiently.

Streaming vs batch ingestion sources

If your data arrives in real-time streams from Kafka, Kinesis, or webhooks, you'll want an OLAP database with native streaming ingestion support. ClickHouse, Druid, and Pinot all handle streaming data well, with different trade-offs in latency and consistency guarantees.

For batch workloads where data lands in S3 or GCS as Parquet files, query engines like Trino, DuckDB, or ClickHouse with S3 integration work efficiently without requiring a separate ingestion pipeline.

SQL dialect and ecosystem fit

Most OLAP databases support standard SQL with varying levels of compatibility. ClickHouse uses its own SQL dialect with powerful extensions for arrays and nested data, while StarRocks offers MySQL compatibility.

Consider whether you want integrations with existing tools like dbt, Airflow, Tableau, or Looker. Some OLAP databases have mature ecosystems with official connectors, while others require custom integration work.

Total cost of ownership

Cost includes more than just the database license or cloud service fees. Factor in infrastructure costs (compute, storage, network egress), operational costs (DevOps time, monitoring, upgrades), and development costs (learning curve, integration work).

Managed services typically cost more per query than self-hosted options, but they eliminate operational overhead. Self-hosting gives you more control and potentially lower costs at scale, but requires expertise in database administration, cluster management, and performance tuning.

Decision checklist for engineers and founders

Before committing to an OLAP database, work through practical steps to validate your choice against real requirements.

Run a realistic benchmark dataset

Create a benchmark using your actual data schema and query patterns, not synthetic data or example queries from documentation. Load a representative sample of your data, then run the queries your application will actually execute in production.

Measure not just average query latency, but also p95 and p99 latency under concurrent load. Some databases perform well on simple queries but struggle with complex joins or high concurrency.

Validate developer experience locally

Install the database or managed service CLI and try building a simple data pipeline from scratch. Evaluate how long it takes to create a table, ingest data, write queries, and debug issues when things go wrong.

Good developer experience means clear error messages, comprehensive documentation, and local development tools that match production behavior.

Assess security and governance features

Check whether the database supports the security features you want: encryption at rest and in transit, role-based access control, audit logging, and compliance certifications like SOC 2 or GDPR.

For production deployments, you'll also want features like query quotas, resource isolation, and the ability to restrict access to sensitive columns or rows.

Plan for observability and autoscaling

Understand what metrics the database exposes and how you'll monitor query performance, resource utilization, and errors in production. Some managed services provide built-in dashboards and alerting, while self-hosted options require integration with tools like Prometheus and Grafana.

Evaluate whether the database can scale automatically based on query load or if you'll have to manually provision additional capacity.

Where ClickHouse and Tinybird fit in the OLAP landscape

ClickHouse has become one of the most widely adopted open source OLAP databases, and Tinybird offers a managed platform that makes ClickHouse easier to integrate into application backends.

When ClickHouse is the right engine

ClickHouse works well for workloads that require fast aggregations over large datasets with flexible query patterns. Its SQL dialect, vectorized execution, and sparse indexing make it particularly effective for time-series data, event analytics, and real-time dashboards.

ClickHouse handles both streaming and batch ingestion, supports distributed queries across clusters, and integrates with common data sources through native connectors or tools like dbt.

Why a managed platform accelerates delivery

Running ClickHouse in production requires expertise in cluster configuration, replication, backups, query optimization, and performance monitoring. Managed platforms like Tinybird handle operational complexity, allowing developers to focus on building features rather than managing infrastructure.

Tinybird adds developer-focused features on top of managed ClickHouse: streaming ingestion from multiple sources, SQL-based data pipelines defined as code, local development with the Tinybird CLI, and automatic API generation from SQL queries. This reduces the time from idea to production API from weeks to hours.

Sign up for a free Tinybird plan

You can try Tinybird without a credit card by signing up for a free account at https://cloud.tinybird.co/signup. The free tier includes enough resources to prototype analytics features, test with realistic data volumes, and deploy APIs to production.

FAQs about choosing an OLAP database

Is PostgreSQL good for OLAP workloads?

PostgreSQL works for small analytical workloads with moderate data volumes, but it lacks the columnar storage and vectorized execution that specialized OLAP databases provide. For queries that scan millions of rows or aggregate across large tables, ClickHouse or other columnar databases typically perform 10x to 100x faster than PostgreSQL. For a detailed comparison of ClickHouse and PostgreSQL including the impact of PostgreSQL extensions like TimescaleDB and pgvector, see our ClickHouse vs PostgreSQL guide.

How much data can an open source OLAP database handle on a single node?

Most modern OLAP databases like ClickHouse can handle multiple terabytes on a single node, depending on your hardware specs and query patterns. The exact limit depends on factors like compression ratio, query complexity, and whether you're using SSDs or HDDs for storage.

Can I start on a managed service and later self-host if needed?

Yes, most managed services use standard open source engines like ClickHouse that you can migrate to self-hosted deployments. However, you'll lose managed features like automatic scaling, built-in monitoring, and API layers. The migration process involves exporting your data and recreating your cluster configuration.