ClickHouse is renowned for its speed, scalability, and real-time query performance. Its compatibility with SQL standards enhances flexibility while enabling integration with popular tools.
| Product | Market Share (%) |
|---|---|
| ClickHouse | 5.3% |
| PostgreSQL | 16.8% |
| Firebird SQL | 15.8% |
| Other | 62.099999999999994% |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| MySQL | 4.1 | 9.2% | 91% | 150 interviewsAdd to research |
| PostgreSQL | 4.2 | 16.8% | 96% | 125 interviewsAdd to research |
ClickHouse excels in handling large volumes of data and queries with high efficiency, which significantly reduces processing time and resource consumption. Businesses have benefited from cost savings due to its open-source nature and its ability to scale vertically and horizontally. Additionally, the speed and performance of ClickHouse have enabled users to enhance their data analysis capabilities, leading to better, data-driven decision-making.
| Company Size | Count |
|---|---|
| Small Business | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 105 |
| Midsize Enterprise | 44 |
| Large Enterprise | 149 |
ClickHouse leverages a column-based architecture for efficient data compression and real-time analytics. It seamlessly integrates with tools like Kafka and Tableau and is effective in handling large datasets due to its cost-efficient aggregation capabilities. With robust data deduplication and strong community backing, users can access comprehensive documentation and up-to-date functionality. However, improvements in third-party integration, cloud deployment, and handling of SQL syntax differences are noted, impacting ease-of-use and migration from other databases.
What features make ClickHouse outstanding?ClickHouse is deployed in sectors like telecommunications for passive monitoring and is beneficial for data analytics, logging Clickstream data, and as an ETL engine. Organizations harness it for machine learning applications when combined with GPT. With the ability to be installed independently, it's an attractive option for avoiding cloud service costs.
| Author info | Rating | Review Summary |
|---|---|---|
| Distinguished AI Leader at Walmart Global Tech at Walmart | 4.5 | I use ClickHouse for merchant analytics due to its low latency, fast aggregations, and effective OLAP storage. It's affordable compared to Druid, though its documentation needs improvement. It's not great for frequent small writes or transactional tasks. |
| Software Analyst at CLSA | 4.0 | We use ClickHouse for IoT and analytics due to its open-source nature, scalability, and ready-to-use analytical functions. However, documentation is lacking, and it doesn't support transactions well. We switched from PostgreSQL for better performance with large data volumes. |
| Senior Data/Web Analyst at Raiffeisen Bank | 4.5 | I use ClickHouse daily for data collection and analysis due to its local installation, avoiding cloud costs. While it mostly meets my needs, minor issues with unique value queries exist. It was chosen over Postgres and Hadoop for security reasons. |
| Senior Data Engineer at Brightika, Inc. | 5.0 | We utilize ClickHouse as both an analytics database and data warehouse. It handles big data efficiently and speeds up our analysts' work. However, deploying clusters can be challenging, especially in the cloud, requiring experience with the product. |
| Senior Software Engineer at a energy/utilities company with 1,001-5,000 employees | 4.0 | I use ClickHouse primarily for data classification from Ethereum, finding its aggregation and compression features cost-efficient for handling large datasets. The primary challenge is the high cost of ClickHouse Cloud, making self-hosting a preferable option. |
| Software Engineer at Activant Solutions Pvt Ltd, Jaipur | 5.0 | We use ClickHouse to store and track live PC data efficiently, finding it significantly faster than traditional databases. Initial integration with ActiveMQ faced issues, but adjustments resolved this. Overall, ClickHouse offers high ROI and improved performance over two years. |
| Full-stack Web Developer at a tech services company with 51-200 employees | 4.5 | We consolidated data from multiple platforms into a single ClickHouse table due to its impressive performance, especially with large datasets. While it lacks traditional database triggers, its optimizer and seamless integration with Apache Superset significantly enhanced our data management and reporting efficiency. |
| Software Development Engineer II at a financial services firm with 10,001+ employees | 3.5 | We use ClickHouse for data analytics and logging Clickstream data in our production environment. Its query engine is super fast, and it's easy to scale on our Kubernetes cluster. However, connecting to third-party applications or the cloud needs improvement. |
