ClickHouse^® vs Firebolt: Which is best for real-time analytics

ClickHouse^® and Firebolt are both columnar analytical databases designed for fast queries over large datasets, but they differ significantly in architecture, operational model, and ideal use cases. ClickHouse^® is an open-source OLAP database you can self-host or run through managed services, while Firebolt is a proprietary cloud data warehouse tracing its ancestry back to a forked version of ClickHouse^® with added optimizations.

This comparison covers the architectural differences between the two systems, their approaches to real-time data ingestion, query performance characteristics, operational complexity, and when to choose each option for your analytics workloads.

Architecture differences between ClickHouse^® and Firebolt

ClickHouse^® is an open-source columnar OLAP database known for fast analytical queries, while Firebolt is a cloud data warehouse originally built on a forked version of ClickHouse^® with added proprietary optimizations. The main architectural difference is that ClickHouse^® couples compute and storage together in a system you manage yourself, whereas Firebolt separates storage and compute into independent layers managed as a cloud service.

Columnar storage and data skipping

Both systems store data in columns rather than rows, which compresses data efficiently and lets queries read only the columns they need. ClickHouse^® uses sparse primary key indexes that skip large blocks of data when those blocks can't contain matching rows. Firebolt adds proprietary indexing layers on top of the ClickHouse^® engine, including aggregating indexes that pre-compute common aggregations like sums or averages to speed up repeated query patterns.

The practical difference is that ClickHouse^® gives you full control over index design and table engines, while Firebolt abstracts some complexity behind automatic index recommendations.

Separation of compute and storage layers

In the classic implementation, ClickHouse^® stores data and runs queries on the same nodes. This tight coupling can be efficient for predictable workloads but makes it harder to scale elastically when query patterns vary. Note that it is possible to achieve separation of storage and compute by combining ClickHouse^® with Amazon S3.

Firebolt natively decouples storage from compute by storing data in object storage like S3 while running queries on separate compute clusters called engines. This architecture lets you scale query capacity independently from data volume, which can reduce costs for workloads with unpredictable concurrency.

Caching strategies

ClickHouse^® uses a mark cache to store locations of data blocks and an uncompressed cache to hold recently decompressed data in memory. Both caches operate at the node level and help avoid repeated disk reads for frequently accessed data.

Firebolt implements a multi-tier caching system that includes local SSD caches on compute nodes to compensate for the latency of reading from remote object storage. The cache warming strategies are managed automatically by Firebolt's query optimizer.

Real-time ingestion and indexing

Both systems handle streaming data ingestion, but they differ in how they maintain query performance while writes are happening. ClickHouse^® provides native integrations for streaming sources, while Firebolt uses connector-based ingestion that buffers data before writing.

Streaming via Kafka or HTTP

ClickHouse^® has a native Kafka table engine that continuously pulls data from Kafka topics and writes it to MergeTree tables. You can also send data directly via HTTP using INSERT statements, which many developers use for application-generated events.

CREATE TABLE events_queue (
    event_id String,
    user_id String,
    timestamp DateTime
) ENGINE = Kafka
SETTINGS kafka_broker_list = 'localhost:9092',
         kafka_topic_list = 'events',
         kafka_group_name = 'clickhouse_consumer';

Firebolt ingests data through connectors that pull from sources like S3, Kafka, or other data stores. The ingestion process buffers data and writes it in batches to optimize for query performance rather than write latency.

Index types and primary keys

ClickHouse^® uses sparse primary key indexes in MergeTree table engines. The primary key determines the sort order of data on disk, which affects query performance for filters and aggregations.

• Sparse index: Stores one index entry per granule (typically 8,192 rows), not per row
• Sorting key: Can differ from the primary key to optimize for different query patterns
• Skip indexes: Additional indexes like bloom filters or min/max indexes for specific columns

Firebolt adds aggregating indexes on top of the base ClickHouse^® engine. These pre-compute aggregations like sums, counts, or averages for common query patterns, speeding up dashboard queries that ask similar questions repeatedly.

Data freshness guarantees

ClickHouse^® writes data to tables immediately but uses eventual consistency for distributed tables. New data becomes visible to queries within seconds, though the exact latency depends on merge operations and replication settings.

Firebolt buffers incoming data and writes it in batches to optimize query performance. This results in a typical delay of minutes between data arrival and query availability, making Firebolt less suitable for applications requiring sub-second data freshness.

Query performance at high concurrency

Both systems can serve sub-second analytical queries, but their performance under concurrent load varies. ClickHouse^® excels at high-throughput batch analytics, while Firebolt is optimized for interactive BI workloads with many concurrent users, achieving 120ms latency under 2,500 QPS.

Benchmarks on single-node workloads

ClickHouse^® performs well on single-node analytical queries that scan large amounts of data, thanks to efficient CPU vectorization and columnar compression. Its open-source nature allows optimization of table engines and indexes for specific query patterns.

Firebolt's proprietary optimizations focus on reducing query latency for repeated patterns, common in BI tools. Benchmarks show 3.7× faster than Snowflake.

Distributed query execution

Queries against a distributed table are routed to each shard, then results are combined. This requires manual sharding configuration.

CREATE TABLE events_distributed AS events_local
ENGINE = Distributed(cluster_name, database_name, events_local, rand());

Firebolt's query engine manages distributed execution automatically. It determines how to distribute work across compute nodes without user-defined sharding strategies.

Latency under burst traffic

ClickHouse^® handles concurrent queries by allocating threads from a shared pool, which can lead to increased latency or queuing under heavy load.

Firebolt allows provisioning multiple compute engines with different sizes, routing high-priority queries to dedicated engines, helping maintain consistent latency. Benchmarks show tens of milliseconds under 8,000 concurrent requests.

Scaling models and operational overhead

The operational complexity of each system varies. ClickHouse^® requires hands-on cluster management, while Firebolt offers a managed service that automates infrastructure tasks.

Elastic compute resize

ClickHouse^® needs manual intervention to add or remove nodes, involving provisioning, configuration, and data rebalancing, which can take hours.

Firebolt enables resizing compute engines via UI or API, with the system handling coordination automatically.

Sharding and replication management

ClickHouse^® provides full control over sharding and replication strategies, requiring configuration and management by the user.

Firebolt abstracts these details, managing data distribution and replication automatically, reducing operational overhead.

Observability and incident response

ClickHouse^® offers system tables with detailed metrics, but requires manual setup for monitoring and alerting.

Firebolt includes built-in observability features like query history and performance metrics, with routine maintenance handled by the managed service.

SQL features, BI tooling, and ecosystem fit

The SQL dialect and ecosystem compatibility influence integration ease. ClickHouse^® uses an optimized SQL dialect, while Firebolt aims for broader SQL standard compliance.

ANSI SQL coverage

ClickHouse^® emphasizes performance, with limited support for some standard SQL features. Firebolt adds PostgreSQL-compatible features, including window functions and complex joins, easing migration from other warehouses.

Joins, updates, and deletes

ClickHouse^® supports joins but performs best with denormalized data; updates and deletes are asynchronous via ALTER TABLE mutations. Firebolt offers more immediate consistency for updates and deletions through its transactional layer.

ALTER TABLE events DELETE WHERE user_id = 'user_123';

Connectors for Airflow, dbt, BI tools

ClickHouse^® has drivers for many languages and integrates with tools like Airflow, dbt, Grafana, and Metabase. Firebolt provides official connectors for dbt, Airflow, Tableau, and Looker, with features like query result caching to enhance BI performance.

Pricing and total cost of ownership

Cost structures differ: self-hosted ClickHouse^® involves infrastructure costs and operational overhead, while Firebolt charges based on consumption and storage.

Pay-as-you-go vs reserved capacity

ClickHouse^® on cloud infrastructure charges for provisioned instances, with options like spot instances or autoscaling. Firebolt charges for compute time and storage separately, suitable for variable workloads.

Storage costs and compression

ClickHouse^® uses local or network storage with high compression ratios (10x to 100x). Firebolt stores data in object storage like S3, with automatic compression and lower per-GB costs.

Support and engineering headcount

Running ClickHouse^® requires expertise in cluster management and query optimization. Firebolt's managed service reduces the need for dedicated operational staff, simplifying maintenance and support.

Understanding the Evolution of Real-Time Data Warehousing

The idea of a real-time data warehouse has evolved from a niche innovation into a foundational requirement for modern analytics. Traditional data warehouses were built for batch ingestion and periodic reporting, often delivering insights hours or even days after data was created.

Today, organizations operate in environments where seconds matter. Data freshness directly impacts revenue, risk, customer experience, and operational stability. A real-time data warehouse continuously ingests, transforms, and serves data as it’s generated, eliminating the wait for nightly ETL jobs and enabling low-latency pipelines that feed analytical storage layers ready for immediate querying.

This architecture delivers up-to-the-minute visibility into transactions, devices, and user behavior — turning every data point into an actionable signal. For additional context on architectural options, you can explore clickhouse alternatives or compare platforms in tinybird vs clickhouse.

Key Principles Behind Real-Time Data Warehousing

Modern real-time data warehouses combine several architectural innovations that together make instant analytics possible:

• Continuous ingestion and transformation: Data flows from event streams, APIs, and transactional systems without batch windows or downtime.
• Columnar, compressed storage: Organizes data for analytical speed, scanning only the blocks required while keeping datasets fresh.
• Parallelized, distributed compute: Each node handles ingestion and queries simultaneously, scaling throughput as demand increases.
• Low-latency access layers: Cached or in-memory components ensure millisecond-level query execution even under heavy concurrency.
• Unified data modeling: Schema evolution and transformation happen in real time so teams always query consistent, structured datasets.

These principles remove the traditional tradeoff between freshness and performance, making real-time analytics practical at scale. To understand which engines best support this model, see best database for real time analytics.

Why Real-Time Data Matters Now

Businesses in every sector rely on instant insight loops:

• E-commerce teams adjust pricing and recommendations based on live demand shifts.
• Financial services detect fraud as transactions occur.
• IoT systems track sensor data to optimize logistics and prevent failures.

This immediacy has created a new baseline of expectations. Internal stakeholders want dashboards that reflect operational truth, not yesterday’s version of it. Customers expect fast, personalized experiences powered by up-to-date data.

Real-time data warehousing makes this possible by combining streaming ingestion, sub-second querying, and scalable infrastructure into a single analytical environment. It’s not just a faster version of the traditional warehouse — it’s a different paradigm designed for the constant motion of modern business.

Balancing Latency, Concurrency, and Cost

Delivering low latency at scale introduces new engineering tradeoffs. As workloads grow, teams must carefully balance query concurrency, freshness, and infrastructure efficiency. Real-time data warehouses achieve this through:

• Tiered storage that keeps recent “hot” data in fast systems while offloading older “cold” data to cheaper layers.
• Adaptive caching that predicts query patterns and warms caches before they’re needed.
• Dynamic scaling that expands compute resources during traffic spikes and contracts them when load decreases.

The result is an architecture that preserves both speed and affordability, sustaining thousands of concurrent queries without overprovisioning.

Real-time systems are no longer experimental. They’ve become a core part of the analytical backbone, bridging operational and analytical workloads in a continuous feedback loop.

Building for Developer Velocity and Operational Simplicity

The rise of real-time data products has shifted analytics from a back-office concern into an integral component of modern application development. Teams now need to build, version, and deploy analytical logic with the same speed and rigor as any software codebase.

From Data Infrastructure to Data as Code

In modern engineering environments, the data warehouse is no longer a black box managed by a separate team. Developers define data models, transformations, and APIs as code — checked into version control and deployed through CI/CD pipelines.

This approach brings software engineering discipline to analytics: testing, rollback, peer review, and repeatability. Changes to ingestion logic or schema definitions can be deployed safely, without downtime or data integrity risks.

Treating data workflows as code enables auditability, collaboration, and predictable evolution of data pipelines.

Automating Real-Time Pipelines

Automation is essential for keeping real-time systems reliable at scale. A modern real-time data warehouse should support:

• Declarative pipelines, where developers describe transformations in SQL or configuration and the system handles execution.
• Automated ingestion connectors that eliminate manual maintenance of streaming jobs or ingest scripts.
• Instant deployment of data APIs, exposing query results directly to dashboards, applications, or services.

These capabilities let teams iterate quickly — turning a dataset into a live analytical API in minutes rather than days. Automation also ensures reliability and operational consistency as systems grow more complex.

Observability and Continuous Improvement

Operational simplicity doesn’t mean less control. Modern data teams expect deep visibility into their pipelines: query latency, ingestion throughput, connector logs, and lineage tracking.

Real-time warehouses provide this observability out of the box, enabling teams to detect bottlenecks, measure freshness, optimize performance, and continuously refine their models.

Observability also drives performance tuning. Developers can identify slow queries, adjust indexing strategies, or refine caching policies based on live system metrics instead of assumptions.

Accelerating Innovation with Real-Time APIs

Once real-time data is clean, consistent, and accessible, it becomes a platform for innovation. Teams can build live dashboards, customer-facing analytics, or AI-driven recommendations directly from the warehouse — without introducing new ETL layers or caching systems.

This shortens the path from data generation to user experience. Product teams can test new metrics, features, or dashboards in hours instead of weeks, enabling rapid experimentation and iteration.

Real-time data infrastructure succeeds not just by delivering speed, but by enabling continuous creation, innovation, and agility.

When to choose ClickHouse^®, Firebolt, or a managed ClickHouse^® platform

The decision depends on your use case, team skills, and control needs. Each has advantages for different scenarios.

Self-hosted ClickHouse^® for full control

Ideal when infrastructure control, compliance, or cost optimization for predictable workloads is a primary requirement. Teams with strong infrastructure expertise can tune performance and achieve excellent price-performance.

Firebolt for batch-oriented SaaS analytics

Suitable for traditional BI workloads with multiple users and variable concurrency. Its decoupled architecture and managed service ease scaling and migration from other warehouses like Redshift or Snowflake.

Tinybird for developer-first managed ClickHouse^®

Tinybird is a managed platform designed for developers building real-time data products and customer-facing analytics. It focuses on low-latency APIs, streaming ingestion, and infrastructure automation, enabling rapid development and deployment of data-driven applications.

Tinybird's approach to managed ClickHouse^® for developers

Tinybird simplifies operational management of ClickHouse^® while maintaining its performance for real-time analytics. It offers managed ingestion from streaming sources like Kafka, HTTP endpoints for query results, and automatic scaling. Developers define data pipelines as code using SQL, deploying via CLI or API, integrating easily with version control, testing, and CI/CD workflows.

Sign up for a free Tinybird plan to start building real-time APIs on managed ClickHouse^®.

FAQs about ClickHouse^® vs Firebolt

What are the migration paths from PostgreSQL or Snowflake to ClickHouse^® or Firebolt?

Both support standard ETL tools like Airbyte, Fivetran, or custom scripts. ClickHouse^® requires schema design and engine selection, while Firebolt offers automatic schema optimization, making migration easier for teams without ClickHouse^® expertise.

How do ClickHouse^® and Firebolt handle GDPR data deletion requests?

ClickHouse^® uses ALTER TABLE DELETE mutations that are asynchronous and may take time on large tables. Firebolt provides more immediate deletion capabilities via its transactional layer, facilitating compliance with timely data removal.

Can ClickHouse^® or Firebolt serve OLTP workloads alongside analytics?

Neither system is optimized for transactional OLTP workloads requiring ACID guarantees. ClickHouse^® can handle simple key-value lookups but is primarily for analytical queries. Firebolt focuses solely on analytics and isn't suitable for OLTP use cases.