Real-time analytics has become a critical requirement across industries. From monitoring infrastructure metrics and detecting fraud in milliseconds to powering interactive dashboards for millions of users, the demand for low-latency analytical systems is growing fast.
To meet this demand, modern engineering teams are turning to real-time OLAP (Online Analytical Processing) databases—systems optimized for rapid, high-concurrency queries on continuously updating data.
In this guide, we’ll break down the top real-time OLAP databases in 2025 and highlight their differences in performance, scalability, and architecture. Whether you're building an internal analytics platform or embedding insights into customer-facing products, understanding these databases will help you choose the right engine for your real-time data needs.
What is a Real-Time OLAP Database?
OLAP databases are designed for fast, complex analytical queries over large volumes of data. A real-time OLAP database goes a step further: it enables analytics on data as it arrives, with minimal delay between ingestion and query ability.
This makes them essential for:
- Live dashboards
- Anomaly detection systems
- Real-time reporting and KPIs
- Monitoring and observability platforms
- Embedded analytics in SaaS applications
Key characteristics include:
- Columnar storage for high compression and speed
- High-concurrency query processing
- Support for streaming or CDC-based ingestion
- Horizontal scalability
Let’s dive into the top real-time OLAP database contenders for 2025.
1. ClickHouse
ClickHouse is a high-performance, open-source OLAP database designed for real-time analytics on large-scale datasets. It uses a columnar storage format and a distributed architecture to deliver ultra-fast query performance, even on billions of rows. ClickHouse is ideal for scenarios where sub-second analytics, massive concurrency, and rapid ingestion are critical.
Key Features & Strengths
- MPP Design: Massively Parallel Processing architecture enables concurrent query execution across multiple cores.
- Storage Efficiency: High compression ratios reduce disk usage and improve data retrieval speeds.
- Materialized Views: Pre-aggregated tables enhance performance for frequent, complex queries.
- Flexible Deployment: Available as a self-managed service or fully managed through ClickHouse Cloud.
Also read: ClickHouse vs Snowflake
2. Apache Druid
Apache Druid is a real-time OLAP database optimized for streaming ingestion and time-series analytics. It combines a column-oriented layout with a memory-mapped storage engine to deliver ultra-low-latency queries. Druid is purpose-built for use cases that require immediate visibility into fast-moving data, making it a top choice for operational intelligence systems.
Key Features
- Streaming Integration: Native support for Kafka and Kinesis enables continuous ingestion with minimal lag.
- Pre-Aggregation: Built-in rollups reduce storage footprint and speed up query performance.
- Sub-Second Latency: Designed for instant access to data as it arrives.
- Operational Focus: Ideal for dashboards, alerting, and time-series event analytics.
3. StarRocks
StarRocks is a modern, high-performance OLAP database designed for real-time analytics at scale. It combines an MPP (Massively Parallel Processing) architecture with vectorized execution to deliver exceptional performance on high-concurrency workloads. With built-in support for lakehouse formats like Apache Iceberg, StarRocks bridges the gap between traditional data warehouses and next-gen lakehouse designs.
Key Features
- Lakehouse Compatibility: Natively supports Apache Iceberg and Hive for flexible data lake integration.
- High-Performance SQL: Optimized for complex JOINs, aggregations, and analytical queries.
- BI-Ready: Seamless integration with popular business intelligence tools and real-time dashboards.
- Scalable Deployment: Available as open-source or enterprise edition via CelerData.
4. Apache Pinot
Apache Pinot is a real-time distributed OLAP database purpose-built to serve low-latency analytical queries. Originally developed at LinkedIn, Pinot excels at delivering personalized insights and metrics in milliseconds, making it ideal for user-facing dashboards and applications. Its support for real-time ingestion and indexing ensures fresh data is always queryable.
Key Features
- Streaming Ingestion: Integrates seamlessly with Kafka and other stream processors for real-time data capture.
- Sub-Second Query Latency: Engineered for high-throughput, low-latency analytical workloads.
- Fault Tolerance: Ensures high availability with built-in replication and recovery mechanisms.
- Search + Analytics: Supports hybrid queries across structured and semi-structured data.
5. Apache Doris
Apache Doris is an open-source, high-performance real-time analytical database designed for low-latency, high-concurrency workloads. It supports both batch and streaming data ingestion, making it suitable for various analytics scenarios, including real-time reporting, data lakehouse integration, and complex ad-hoc queries.
Key Features
- Real-Time Ingestion: Supports push-based micro-batch and pull-based streaming data ingestion within seconds.
- High Performance: Optimized for high-concurrency and high-throughput queries with a columnar storage engine and vectorized execution.
- Lakehouse Integration: Facilitates federated querying of data lakes such as Hive, Iceberg, and Hudi.
- Elastic Architecture: Offers compute-storage decoupled mode for cost efficiency and scalability.
6. ClickHouse Cloud
ClickHouse Cloud is the fully managed version of ClickHouse, offering the same high-performance OLAP capabilities without the operational overhead. Built for the cloud, it supports serverless or dedicated cluster modes and is optimized for real-time analytics at scale. It’s ideal for teams that want sub-second query performance with zero infrastructure management.
Key Features
- Instant Provisioning: Deploy production-ready clusters in minutes.
- Elastic Scaling: Automatically adjusts resources for bursty or unpredictable workloads.
- Serverless Simplicity: Pay-as-you-go model with autosuspend to save costs.
- Production-Grade Reliability: Operated and supported by the ClickHouse team.
7. Firebolt
Firebolt is a cloud-native data warehouse engineered for ultra-low latency and high-concurrency analytics. It combines a decoupled storage and compute architecture with advanced indexing and query optimization to deliver sub-second query performance on large datasets. While Firebolt includes features like vector search for AI applications, its core strengths remain in delivering low-latency query performance and high concurrency for interactive dashboards and user-facing applications.
Key Features
- High Performance: Delivers millisecond-level query responses, suitable for interactive dashboards and user-facing applications.
- Scalability: Supports thousands of concurrent queries with efficient resource utilization.
- Flexible Infrastructure: Offers multi-dimensional and granular scaling with instant elasticity.
- AI-Ready: Supports features like vector search and real-time data processing, though these are emerging capabilities compared to its core analytics strengths
Sync Your Data to Firebolt in Real-Time. Get Started with Estuary
8. BigQuery
BigQuery is Google Cloud’s fully managed, serverless data warehouse built for scalable analytics. While it supports near real-time ingestion through its streaming API, BigQuery is not purpose-built for sub-second, ultra-low-latency analytics. Its architecture is optimized for large-scale analytical workloads and batch processing, making it ideal for teams seeking a low-maintenance solution that integrates deeply with the Google Cloud ecosystem. For use cases requiring true real-time analytics with millisecond latency, other OLAP databases on this list may be more suitable.
Key Features
- Serverless Architecture: No infrastructure to manage; automatically scales for performance and concurrency.
- Streaming Inserts: Enables near real-time analytics by ingesting data as it's generated, though query latency may not be sub-second.
- Federated Queries: Run SQL directly on external sources like Google Sheets or Cloud Storage.
- Strong Ecosystem: Seamlessly integrates with Looker, Data Studio, and other GCP services
Want to make the most of BigQuery’s analytics power with real-time data?
Move Data To and From BigQuery in Minutes. Start Free with Estuary.
9. MotherDuck
MotherDuck is a cloud-based analytics platform that extends the power of DuckDB, a lightweight analytical engine known for its fast local performance. While DuckDB itself is not a full-fledged OLAP database, MotherDuck enhances its capabilities by adding cloud-native collaboration and storage, enabling teams to run analytics workflows that span from developer laptops to the cloud. MotherDuck is best suited for interactive analytics, prototyping, and embedded analytics in lightweight applications, rather than as a primary OLAP engine for large-scale, high-concurrency workloads.
Key Features
- Hybrid Execution: Seamlessly switch between local DuckDB queries and cloud-based processing.
- Cloud Object Storage: Store and access data centrally while running queries from anywhere.
- Developer-Friendly: Ideal for notebooks, prototyping, and embedded analytics in lightweight apps.
- Serverless Simplicity: No infrastructure to manage; collaborative and easy to onboard
Sync Data to MotherDuck in Real Time — No Code Required. Get Started →
Want to see it in action?
• BigQuery to MotherDuck
• Snowflake to MotherDuck
10. Amazon Redshift
Amazon Redshift Streaming extends Redshift’s traditional OLAP capabilities with real-time data ingestion from sources like Amazon Kinesis and MSK. While this feature allows users to query streaming data with minimal delay, Redshift’s architecture is primarily optimized for batch-oriented analytics. Redshift Streaming is best suited for operational analytics and real-time reporting within AWS-centric environments, but may not deliver the ultra-low-latency required for high-frequency, sub-second analytics.
Key Features
- Streaming Ingestion: Natively supports Kinesis and MSK for near real-time data pipelines.
- Columnar Storage: Optimized for analytical workloads with high compression and fast scan speeds.
- Deep AWS Integration: Seamless connectivity with S3, Glue, SageMaker, and other AWS services.
- Managed Scalability: Offers automatic scaling, concurrency handling, and fault tolerance
Move data to and from Amazon Redshift in real time with Estuary — Start Free →
Power Your Real-Time OLAP Stack with Estuary Flow
Real-time analytics starts with real-time data delivery—and that’s exactly where Estuary Flow excels. Whether you're using ClickHouse, MotherDuck, Redshift, or another OLAP engine, Estuary keeps your analytical layer continuously updated with fresh, actionable data.
Stream Data from Your Operational Systems
With native support for Change Data Capture (CDC) and stream processing, Estuary lets you ingest and sync data from:
- Databases: PostgreSQL, MySQL, MongoDB, SQL Server
- Event Streams: Apache Kafka, Redpanda
- SaaS Tools: NetSuite, Salesforce, Stripe, HubSpot
...and many more.
Why Use Estuary Flow?
- Real-Time Ingestion: Built-in CDC and streaming support for low-latency data movement
- No-Code Pipelines: Configure and deploy in minutes—no engineering heavy-lifting required
- In-Flight Transformation: Filter, reshape, or enrich your data before it hits the destination.
- OLAP-Ready Outputs: Materialize directly into ClickHouse, MotherDuck, Redshift, and others
Estuary turns your raw, operational data into real-time insights—without the complexity of traditional ETL
Final Thoughts
Choosing the right real-time OLAP database in 2025 depends on your specific needs—whether you need ultra-fast dashboards, embedded analytics, or continuous streaming personalization. Each tool on this list has unique strengths.
But regardless of the database you choose, pairing it with a powerful real-time ingestion layer like Estuary Flow ensures your analytics stay current, reliable, and actionable.
Ready to sync data into your OLAP destination? Get started with Estuary Flow today.
Also Read
Popular Articles
