Top OLAP Databases for Real-Time Analytics in 2025

The need for real-time data analysis is growing fast. Businesses want to make quick, informed decisions using the latest data. Traditional OLAP databases, designed for analyzing historical data, are adapting. Plus, new stream processing tools are emerging, offering fresh ways to handle real-time data.

This article explores five top OLAP databases for 2025 and briefly introduces stream processing solutions like RisingWave and ksqlDB. These tools are at the forefront of real-time analytics, offering speed, scalability, and advanced features for handling large and complex datasets.

1. ClickHouse: Fast Real-Time Analytics

ClickHouse, originally developed at Yandex, is a popular open-source, column-oriented OLAP database. It's famous for being incredibly fast, processing billions of rows per second. This speed makes it excellent for demanding real-time analytics.

ClickHouse is designed to be distributed, meaning you can add more servers to handle growing data and user needs. Data is split across these servers, so queries can be processed in parallel, making everything much faster. It's like having multiple chefs working on different parts of a meal at the same time.

ClickHouse handles data ingestion in various ways – from batch uploads (like CSV files) to real-time streams using Apache Kafka. It uses a special storage system called the MergeTree family, which is optimized for quickly adding and retrieving data.

Instead of processing data row by row, ClickHouse uses vectorized query execution. Think of it like processing entire columns at once, which is much more efficient for modern CPUs. This, combined with its columnar storage, results in incredibly fast analytical queries.

Key Features:

• High Performance: Optimized for fast analytical queries.

• Scalable: Easily add more servers as needed.

• Real-time Data: Works well with Kafka for streaming data.

• SQL Support: Uses a powerful SQL dialect.

• Materialized Views: Supports pre-aggregated results for speed.

Use Cases:

ClickHouse is great for:

• Real-time dashboards

• Interactive data exploration

• Analyzing website/app user behavior

• Monitoring application performance

• Detecting security threats and fraud

Limitations:

• SQL Dialect: Not fully ANSI SQL compliant.

• Updates/Deletes: Updating or deleting individual records can be less efficient.

Deployment:

You can deploy ClickHouse yourself or use a managed cloud service (like ClickHouse Cloud, AWS, or Google Cloud).

Companies Using ClickHouse:

Companies like Cloudflare, Uber, and Spotify use ClickHouse for real-time insights.

2. Apache Druid: Analyzing Event Streams in Real-Time

Apache Druid is an open-source database designed for analyzing event-driven data in real-time. It's perfect for situations where you need to analyze high-dimensional, time-series data with low latency, such as clickstream analysis or application monitoring.

Druid is built for high availability and scalability. It uses a microservices-based architecture, allowing you to deploy and scale different parts of the system independently. Key components include:

• Master Server: Manages data and ingestion.

• Query Server: Handles queries.

• Data Server: Stores and serves data.

• Historical and MiddleManager Services: For storing and ingesting data, respectively.

Druid excels at ingesting data from streaming sources like Kafka and Kinesis, as well as batch sources. Data is stored in segments, which are optimized for fast filtering and aggregation, especially for time-based queries. Druid uses various indexing techniques, including inverted indexes and bitmap indexes, to speed up queries.

Key Features:

• Real-time Ingestion: Optimized for streaming data.

• Time-Series Focus: Great for time-based queries.

• High Dimensionality: Handles many data dimensions.

• Scalable: Handles massive datasets.

• Fault Tolerance: Designed for high availability.

Use Cases:

• Clickstream analysis

• Application performance monitoring (APM)

• Network monitoring

• IoT data analytics

• Business intelligence dashboards

Limitations:

• Query Language: Primarily uses a JSON-based language (SQL support is improving).

• Joins: Limited and less performant for complex joins.

Deployment:

You can deploy Druid yourself or in the cloud. Imply offers a managed service.

Companies Using Druid:

Companies like Netflix, Airbnb, and Lyft use Druid.

3. Apache Pinot: Ultra-Low Latency for User-Facing Analytics

Apache Pinot is designed for extremely fast analytical queries, even with high user loads. This makes it ideal for user-facing analytics applications where speed is critical. It was developed at LinkedIn and is now used by companies like Uber and Stripe.

Pinot's architecture is built for speed and resilience. Key components include:

• Pinot Servers: Store data and execute queries.

• Pinot Brokers: Receive and route queries.

• Pinot Controllers: Manage the cluster.

• Pinot Minions: Handle tasks like data ingestion (optional).

• Deep Storage: Pinot relies on a deep storage system (e.g. HDFS, S3, GCS).

Pinot handles both real-time data streams (like Kafka) and batch data (from HDFS or cloud storage). It's incredibly fast thanks to its indexing capabilities, including Forward, Inverted, Sorted, and the unique Star-Tree index (which pre-aggregates data for faster group-by queries).

Key Features:

• Ultra-Low Latency: Queries return in milliseconds.

• High Throughput: Handles many concurrent queries.

• Real-Time & Batch: Supports both types of ingestion.

• Pluggable Indexing: Many index types, including Star-Tree.

Use Cases:

• User-facing analytics dashboards

• Real-time fraud detection

• Anomaly detection

• Inventory management

Limitations:

• SQL Support: Uses PQL; JDBC driver has limited SQL support.

• Data Updates: Primarily designed for append-only data.

Deployment:

You can deploy Pinot yourself or in the cloud. StarTree offers a managed service.

4. SingleStore: Combining Transactions and Analytics

SingleStore (formerly MemSQL) is a SQL database that handles both operational workloads (like transactions) and analytical queries in a single system. This simplifies data pipelines and reduces delays.

SingleStore's architecture features:

• Aggregators: Handle client connections and query coordination (Master and Child Aggregators).

• Leaves: Store data and execute queries.

SingleStore supports Rowstore tables (for fast transactions, stored in memory) and Columnstore tables (for analytics, stored on disk). Universal Storage combines these features. This hybrid approach (HTAP) eliminates the need for separate systems, simplifying your setup.

Key Features:

• High Performance: Fast for both transactions and analytics.

• Scalable: Handles large datasets and high concurrency.

• Full SQL Support: Excellent support for standard SQL.

• Real-Time Ingestion: Supports Kafka.

• HTAP: Handles both transactional and analytical workloads.

Use Cases:

• Real-time dashboards and reporting

• Operational intelligence

• Fraud detection

• Risk management

• Applications needing both transaction and analytic capabilities

Limitations:

• Pure Analytics Focus: Might not be as specialized for only analytics.

Deployment:

On-premise, cloud, or as a managed service (SingleStore Helios).

5. StarRocks: A Rising Star for Real-Time Analytics

StarRocks is a newer OLAP database designed for high-performance, real-time analytics. It's gaining popularity for its speed and scalability.

StarRocks uses a Massively Parallel Processing (MPP) architecture:

• Frontend (FE) nodes: Handle query planning and coordination.

• Backend (BE) nodes: Store data and execute queries.

• Disaggregated Architecture: StarRocks also supports compute-storage disaggregation.

StarRocks' speed comes from its vectorized query engine (processing columns in batches) and a cost-based optimizer (CBO) that intelligently chooses the best query plan. It supports real-time streaming (Kafka) and batch loading. It uses a columnar format and offers various indexing options, including Sorted Stream, Inverted, Bitmap, and Bloom Filter indexes. It also supports materialized views.

Key Features:

• High Performance: Very fast query performance.

• Scalability: Handles high concurrency and large data volumes.

• Real-Time Ingestion: Supports streaming sources.

• Materialized Views: Pre-calculated results for common queries.

• Vectorized Engine & CBO: For speed and efficiency.

Use Cases:

• Real-time dashboards and reporting

• Ad-hoc queries

• User behavior analysis

• Log analytics

Limitations:

• Maturity: Newer, so the community is still developing.

• SQL Support: Good, but improving.

Deployment:

On-premise, cloud, or cloud-native version (StarRocks Cloud).

Companies Using StarRocks:

Companies like Airbnb, Lenovo, and Trip.com use StarRocks.

Stream Processing: A Different Approach

Stream processing systems handle data as it arrives, continuously. This is great for situations where you need to take immediate action.

RisingWave: Cloud-Native Streaming Database

RisingWave simplifies real-time analytics by letting you define materialized views over streaming data. These views are updated automatically as new data arrives.

• Architecture: Separate compute and storage for easy scaling.

• SQL Support: Uses standard SQL.

• Use Cases: Real-time monitoring, alerting, live dashboards.

• Deployment: It can be deployed on Kubernetes and is also available as a fully managed cloud service.

ksqlDB: Stream Processing for Data Prep

ksqlDB helps you filter, transform, and join data streams using SQL-like syntax. It's often used to prepare data before it's sent to an OLAP database or streaming database.

• Integration: Works well with Kafka and can be used with RisingWave.

• Use Cases: Filtering data, transforming formats, enriching data.

• Deployment: On-premise, Cloud (Confluent Cloud).

Comparison Table

Conclusion

The real-time analytics landscape is changing quickly. ClickHouse, Druid, Pinot, SingleStore, and StarRocks are leading OLAP databases, each with its own strengths. RisingWave offers a different approach with continuous processing, and ksqlDB helps prepare real-time data.

Choosing the right solution depends on your needs. Consider data volume, query complexity, latency, scalability, and your team's skills. By understanding these tools, you can build a real-time analytics platform that helps your business make better decisions, faster.