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# ClickHouse vs Spark SQL Comparison - What are their main differences?

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ClickHouseSpark SQL[+ Add Product](#)

Pricing

**ClickHouse**

**Starting at $1.00** Per Month · ClickHouse Pricing

**Spark SQL**
No pricing available

**Pricing Insights**
from reviews

Perceived Cost

**ClickHouse**
Not enough reviews yet

**Spark SQL**
$$$$$

Average Discount

**ClickHouse**
Not enough reviews yet

**Spark SQL**
Not enough reviews yet

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Time to Implement

**ClickHouse** 6 months
Based on 100 responses

**Spark SQL** 6 months
Based on 100 responses

Return on Investment

**ClickHouse** 6 months
Based on 100 responses

**Spark SQL** 6 months
Based on 100 responses

**Pros & Cons**
from reviews

**ClickHouse**

Easy Integrations (2)

Integrations (2)

Beginner Unfriendliness (1)

Complex Usage (1)

**Spark SQL**
Not enough data

Integrations

**ClickHouse**
[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_809aa88d3571ee805a47d8fb156ba412/apache-kafka.jpg "Product Avatar Image")

Apache Kafka

](https://www.g2.com/products/apache-kafka/reviews)[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f176b4154a751d10150daa67a57b7dc5/azure-data-factory.jpg "Product Avatar Image")

Azure Data Factory

](https://www.g2.com/products/azure-data-factory/reviews)[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_5a70c0e0f26ded6f6c6813e514d2fa20/claude.png "Product Avatar Image")

Claude

](https://www.g2.com/products/claude/reviews)[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_017ca5b65bc8cc79fa434f2716af16ee/dbt.png "Product Avatar Image")

dbt

](https://www.g2.com/products/dbt/reviews)[
View all
](https://www.g2.com/products/clickhouse/integrations)

**Spark SQL**
[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_13aba37ae908d9cf29a2452925257ccc/azure-databricks.jpeg "Product Avatar Image")

Azure Databricks

](https://www.g2.com/products/azure-databricks/reviews)[

 ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_a6c205d533dba77b318af96d91beb2ac/databricks.jpeg "Product Avatar Image")

Databricks

](https://www.g2.com/products/databricks/reviews)[
View all
](https://www.g2.com/products/spark-sql/integrations)

Screenshots

**ClickHouse**
 ![ClickHouse screenshot](https://images.g2crowd.com/cdn-cgi/image/width=600,height=600,fit=scale-down,format=auto,onerror=redirect,/https://images.g2crowd.com/uploads/attachment/file/1384096/Screenshot-2024-06-21-at-7.45.48-AM.png)

**Spark SQL**
No screenshot available

**Ratings**
from reviews

Meets Requirements

**ClickHouse**

**9.0** /10

Based on 34 responses

**Spark SQL**

**8.8** /10

Based on 33 responses

Ease of Use

**ClickHouse**

**8.6** /10

Based on 35 responses

**Spark SQL**

**9.0** /10

Based on 36 responses

Ease of Setup

**ClickHouse**

**8.2** /10

Based on 29 responses

**Spark SQL**

**8.8** /10

Based on 15 responses

Ease of Admin

**ClickHouse**

**7.2** /10

Based on 15 responses

**Spark SQL**

**8.2** /10

Based on 13 responses

Quality of Support

**ClickHouse**

**8.1** /10

Based on 28 responses

**Spark SQL**

**8.4** /10

Based on 32 responses

Has the product been a good partner in doing business?

**ClickHouse**

**8.5** /10

Based on 10 responses

**Spark SQL**

**8.6** /10

Based on 14 responses

Product Direction (% positive)

**ClickHouse**

**9.2** /10

Based on 31 responses

**Spark SQL**

**10.0** /10

Based on 30 responses

**Features**
ratings from reviews

Relational Databases

**9.0** /10

Based on 7 responses

**8.5** /10

Based on 28 responses

Database Management Systems (DBMS)

**8.9** /10

Based on 7 responses

Not enough data

Columnar Databases

**8.2** /10

Based on 12 responses

Not enough data

Uncover more feature rated insights from real users to improve your shopping experience.

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Real-time Analytic Database

**9.2** /10

Based on 25 responses

Not enough data

User Insights

**Reviewers' Company Size**

Small-Business(50 or fewer emp.)
44.6%

Mid-Market(51-1000 emp.)
41.1%

Enterprise(\> 1000 emp.)
14.3%

Small-Business(50 or fewer emp.)
44.6%

Mid-Market(51-1000 emp.)
41.1%

Enterprise(\> 1000 emp.)
14.3%

Small-Business(50 or fewer emp.)
26.1%

Mid-Market(51-1000 emp.)
19.6%

Enterprise(\> 1000 emp.)
54.3%

Small-Business(50 or fewer emp.)
26.1%

Mid-Market(51-1000 emp.)
19.6%

Enterprise(\> 1000 emp.)
54.3%

**Reviewers' Industry**

[![ClickHouse](https://images.g2crowd.com/uploads/product/hd_favicon/1085fc1aec988cc21861ba894db51044/clickhouse.svg "ClickHouse")](https://www.g2.com/products/clickhouse/reviews)
ClickHouse

Information Technology and Services
20.9%

Computer Software
16.3%

Financial Services
11.6%

Banking
4.7%

Logistics and Supply Chain
4.7%

Other
41.9%

[![Spark SQL](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_9e6249d49a182e97a34b477a3611f47a/spark-sql.png "Spark SQL")](https://www.g2.com/products/spark-sql/reviews)
Spark SQL

Information Technology and Services
30.4%

Computer Software
13.0%

Retail
6.5%

Financial Services
6.5%

Telecommunications
4.3%

Other
39.1%

Alternatives

**ClickHouse Alternatives**

 ![Google Cloud BigQuery](https://images.g2crowd.com/uploads/product/hd_favicon/875b8a68eca98ce3458f26e9dd34cdf9/google-cloud-bigquery.svg "Google Cloud BigQuery")

[
BigQuery
](/products/google-cloud-bigquery/reviews)

 ![MySQL](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_240340edca859994ec71e4490aa20022/mysql.png "MySQL")

[
MySQL
](/products/mysql/reviews)

 ![Snowflake](https://images.g2crowd.com/uploads/product/hd_favicon/1544409170/snowflake.svg "Snowflake")

[
Snowflake
](/products/snowflake/reviews)

 ![Microsoft SQL Server](https://images.g2crowd.com/uploads/product/hd_favicon/a8a99a96fda235658139f710592f8a53/microsoft-sql-server.svg "Microsoft SQL Server")

[
MS SQL
](/products/microsoft-sql-server/reviews)

**Spark SQL Alternatives**

 ![Oracle Database](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_82f55e945509bff7b8009e8024cfe73c/oracle-database.jpg "Oracle Database")

[
Oracle Database
](/products/oracle-database/reviews)

 ![PostgreSQL](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_251be2af3ae607c45c14e816eaa1cf41/postgresql.png "PostgreSQL")

[
PostgreSQL
](/products/postgresql/reviews)

 ![Microsoft SQL Server](https://images.g2crowd.com/uploads/product/hd_favicon/a8a99a96fda235658139f710592f8a53/microsoft-sql-server.svg "Microsoft SQL Server")

[
MS SQL
](/products/microsoft-sql-server/reviews)

 ![SQLite](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_cf27b7c50adc61e4d44c2ef887f29fa3/sqlite.gif "SQLite")

[
SQLite
](/products/sqlite/reviews)

## ClickHouse vs Spark SQL FAQs

Generated using AI

Last updated: August 15, 2026

### What is the difference between Spark SQL vs ClickHouse?

Spark SQL stands out for its slightly higher G2 rating and ease of use, while ClickHouse is recognized for its performance and analytics capabilities.

| [Spark SQL](https://www.g2.com/products/spark-sql/reviews) | [ClickHouse](https://www.g2.com/products/clickhouse/reviews) |
| --- | --- |
| 4.6/5 (47 reviews) | 4.5/5 (23 reviews) |
| — | — |
| 8.8 | 9.0 |
| 8.4 | 8.3 |
| High performance with both small and large datasets | Exceptional query performance and analytics on large datasets |

### How do the pricing models of Spark SQL and ClickHouse compare?

Spark SQL is rated higher for value, with a stronger Price/Cost satisfaction score than ClickHouse.

- **Spark SQL:** Reviewers highlight Spark SQL as cost-effective for handling large datasets, though some mention higher costs compared to traditional databases.
- **ClickHouse:** Reviewers note that ClickHouse's cloud option can be expensive, especially when larger servers are required for optimal performance.

### What are the best alternatives to Spark SQL and ClickHouse?

The best alternatives to Spark SQL and ClickHouse are BigQuery, Oracle Database, and PostgreSQL.

| Product | G2 Rating (reviews) | Largest Segment | Pricing Insight | Top Reviewer-Cited Strength |
| --- | --- | --- | --- | --- |
| [Spark SQL](https://www.g2.com/products/spark-sql/reviews) | 4.6/5 (47 reviews) | — | Cost-effective for large datasets | High performance with both small and large datasets |
| [ClickHouse](https://www.g2.com/products/clickhouse/reviews) | 4.5/5 (23 reviews) | — | Cloud option can be expensive for larger servers | Exceptional query performance and analytics on large datasets |
| [BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) | 4.5/5 (1224 reviews) | Enterprise | — | — |
| [Oracle Database](https://www.g2.com/products/oracle-database/reviews) | 4.3/5 (1020 reviews) | Enterprise | — | — |
| [PostgreSQL](https://www.g2.com/products/postgresql/reviews) | 4.4/5 (684 reviews) | Small-Business | — | — |

### Which Relational Databases features should I prioritize when comparing Spark SQL and ClickHouse?

Buyers should prioritize performance, ease of setup, quality of support, storage, and analytics capabilities when comparing Spark SQL and ClickHouse.

- **Performance:** Spark SQL (high performance with both small and large datasets), ClickHouse (exceptional query performance and analytics on large datasets, 1 all-time mention for performance optimization)
- **Ease of Setup:** Spark SQL 8.8, ClickHouse 9.0
- **Quality of Support:** Spark SQL 8.4, ClickHouse 8.3
- **Storage:** ClickHouse (1 all-time mention for data storage), Spark SQL (reviewers highlight efficient handling of petabyte-scale data)
- **Analytics Capabilities:** ClickHouse (1 all-time mention for analytics and data analytics), Spark SQL (reviewers cite robust query optimization and unified data processing)

### What are the pros and cons of Spark SQL vs ClickHouse?

Spark SQL's headline strength is its high performance with both small and large datasets, while ClickHouse excels in exceptional query performance and analytics on large datasets.

- **Spark SQL strengths:** High performance with both small and large datasets, robust query optimization, unified data processing, and efficient handling of petabyte-scale data.
- **Spark SQL trade-offs:** Resource-intensive for complex queries, challenging local setup, higher learning curve, and limited real-time processing.
- **ClickHouse strengths:** Exceptional query performance, analytics on large datasets, columnar storage design, fast querying, and strong integration with analytics tools (1 all-time mention each for analytics, performance, data storage, and fast querying).
- **ClickHouse trade-offs:** Initial learning curve, complex query optimization, and higher costs for cloud/server scaling.

### Is Spark SQL or ClickHouse better for small businesses?

PostgreSQL is the top-ranked alternative with a strong small-business segment, but between Spark SQL and ClickHouse, Spark SQL is better suited for small businesses based on reviewer sentiment.

- **Spark SQL:** Reviewers highlight ease of use for those with basic SQL skills and effective handling of both small and large datasets.
- **ClickHouse:** Reviewers note a learning curve and the need for larger servers for optimal performance, which may be less ideal for small businesses.

### Which Relational Databases platform has better integrations?

ClickHouse is favored for integrations, with reviewers citing easy integrations and smooth connections to analytics tools.

- **Spark SQL:** Reviewers mention integration with multiple third-party software and reporting tools in recent reviews.
- **ClickHouse:** Reviewers highlight easy integrations and smooth integration with tools like Grafana and dbt.

### How do Spark SQL and ClickHouse compare on customer support?

Spark SQL and ClickHouse are rated at parity on Quality of Support, with scores of 8.4 and 8.3 respectively.

- **Spark SQL:** Reviewers describe support as responsive and helpful, with several citing positive experiences in troubleshooting and documentation.
- **ClickHouse:** Reviewers mention strong community and documentation support, though some note challenges with advanced use cases and onboarding.

### Which is easier to implement, Spark SQL or ClickHouse?

ClickHouse scores higher on Ease of Setup with a 9.0 versus Spark SQL's 8.8, indicating reviewers find ClickHouse easier to implement.

- **Spark SQL:** Reviewers note a straightforward setup for those familiar with SQL, but mention challenges with local deployment and resource requirements.
- **ClickHouse:** Reviewers highlight ease of implementation and fast setup, with some mentioning a learning curve for advanced features.

### Which product has better Storage?

ClickHouse is favored for Storage, with reviewers citing its columnar storage design and data compression capabilities.

- **Spark SQL:** Reviewers highlight efficient handling of petabyte-scale data and high-speed data processing.
- **ClickHouse:** 1 all-time mention for data storage, with reviewers citing columnar storage, data compression, and optimal storage for large datasets.