Real-Time Analytical Database

Conversely, real-time analytical databases like ClickHouse, Pinot, Druid, and DuckDB achieve higher performance by co-locating compute and storage[^1]. These systems keep frequently accessed data and aggregations in memory, enabling extremely fast query performance, similar to what the initial OLAP solutions such as SSAS and others did best initially.

These discoveries lead us to two main categories of analytical databases:

1. Cloud Data Warehouses (Snowflake, Redshift, BigQuery, Azure Fabric, Firebolt)
2. Real-time analytical databases (ClickHouse, Apache Pinot, Druid, DuckDB, StarRocks, Apache Doris, Apache Kylin).

If we categorize real-time analytical databases further, there might be a trade-off between scale vs. complexity.

So, what are real-time analytical databases?

Real-time databases allow us to have extremely fast response times, which is needed for time-critical analytics. They may additionally enable real-time updates through direct streaming ingestion, similar to streaming solutions. They allow a lower-cost approach in terms of cost-per-query if you have an analytical, query-heavy workload.

Real-time analytical databases use modern OLAP technologies to combine the best of traditional OLAP systems with modern analytical capabilities. Some of the benefits of real-time analytical databases:

• Sub-second query response times enable interactive dashboards and analytics experiences
• Columnar storage optimization dramatically speeds up aggregations and filtering by reading only the needed columns, reducing I/O bottlenecks.
• Vectorized processing leverages modern CPU capabilities to process data in chunks rather than row-by-row, delivering performance gains.
• Cost efficiency is achieved through co-located compute and storage architecture that eliminates expensive data movement between layers. Co-located compute also allows direct access to data without network transfer delays, reducing query latency.
• Real-time data ingestion supports streaming and batch processing, enabling fast insights from fresh data without separate pipelines.
• Lower operational costs for query-heavy analytical workloads as pre-calculated and not charged for each query.
• More flexibility by relaxing some of the constraints of traditional OLAP databases, for instance by enabling JOIN and UPSERT operations.
• Open-source foundations provide vendor independence and community-driven innovation, reducing lock-in risks compared to proprietary solutions.

Real-time databases serve analytical data in sub-seconds but have an additional overhead and some disadvantages; let’s look at how to choose the right tool for the right job.

Read more on Scaling Beyond Postgres: How to Choose a Real-Time Analytical Database.

• Not All Analytics Are Equal: Benchmarking Databases for Real-Time Analytics Applications | Timescale

Origin: Scaling Beyond Postgres- How to Choose a Real-Time Analytical Database
References: OLAP
Created 2025-03-13