Software Development Engineer at a tech vendor with 201-500 employees
Real User
Top 10
Dec 5, 2025
My main use case for ClickHouse is data ingestion and for its OLAP properties, as we had use cases where database locks were slowing us down and because ClickHouse does not have that, we chose to use it. I could give you a quick, specific example of how I'm using ClickHouse for data ingestion where the lack of database locks helped us when we were parsing IOCs among other things, as a lot of that data has to be processed really quickly and ingested into the database for further processing and identifying which IOCs are compromised. ClickHouse helped us solve the problem that we were having and it's one of the two databases we used at Cyware—one was Postgres, the other was ClickHouse.
Distinguished AI Leader at Walmart Global Tech at Walmart
Real User
Top 5
May 8, 2025
I have experience in ClickHouse ( /products/clickhouse-reviews ), and we also use Apache Druid ( /products/druid-reviews ), which has corporate support from Druid ( /products/druid-reviews ), along with data products in Hadoop ( /categories/hadoop ). We are currently exploring many platforms such as GMI, TKI, and Vertex. I use ClickHouse ( /products/clickhouse-reviews ) as a merchant side portal, especially when we started exploring how to use the data, which was coming from multiple sources such as logs, mainframe, Teradata ( /products/teradata-reviews ), and many file systems that come to the data lake. The real-time challenge was joining the data and providing more analytical queries for our merchants, who work throughout the year to improve GMB, sales, and ensure the right quantity of items is ordered at the right time. That's the challenge for the merchants, and we aim for fast analytical queries on larger databases, which is why we selected ClickHouse as our columnar OLAP database supporting real-time analytics with its own SQL interface. We have installed both local Docker ( /products/docker-37146-reviews ) versions, which are quite scalable, and usually connect with BI tools such as Grafana ( /products/grafana-reviews ), Superset ( /products/superset-reviews ), and Tableau ( /products/tableau-reviews ) while utilizing materialized views, DDLs partitions, and many other connectors with Python, such as ClickHouse connectors and drivers. It's exciting to see how ClickHouse has evolved, and we are evaluating ClickHouse Cloud while also having the on-premises version. We are already a customer of ClickHouse, with Sam's Club utilizing it on the merchant side while also exploring ClickHouse for consumers, primarily for user analytics, metrics, and streaming data analysis in ad tech. Additionally, we use custom analysis and metrics for fraud detection in payments and ad campaign metrics, with various teams utilizing it for ad campaign management and user behavior analytics, particularly on e-commerce sites focusing on customer behavior. It's extensively used due to its low latency, fast aggregations, and excellent OLAP columnar storage, featuring quick joins and real-time data visibility, making ClickHouse very appealing to us.
The main use case for ClickHouse is as a data warehouse for handling large volumes of data that exceed the capabilities of traditional databases like Postgres. I use it for creating dashboards and performing analytical tasks such as determining the total number of orders, average order value, and evaluations and ratios for various stores. I deploy ClickHouse both on the cloud provided by ClickHouse itself and on-premises for IoT and similar data tasks.
Full-stack Web Developer at a tech services company with 51-200 employees
Real User
Top 5
Jul 12, 2024
Our company had about nine platforms, each with its own database and data. We had to gather all these data in one database and just one table. We used Apache Superset to integrate this database with the business intelligence tool. We had too many choices or options initially for the database engine. We initially tested a database, and its performance was good. When we tried ClickHouse, we switched to it immediately because the performance was really amazing. When we had a huge amount of data, about five or six gigabytes in just one table, and we used ClickHouse to deduplicate some duplicated entries or records.
I used ClickHouse to collect data, put it in the database, and then analyze it to find insights. The main advantage is that I can install it on my computer instead of using cloud-based solutions, so I don't have to pay for every query like with Google or Amazon cloud services.
We use the solution not just as an analytics database but also as a data warehouse. A lot of our internal services communicate with the tool. Our analytics and ML teams also use the solution. It's the hub of our company.
Software Engineer at Activant Solutions Pvt Ltd, Jaipur
Real User
Top 10
Jun 13, 2024
We use it to store live data and for tracking and monitoring every action on a PC, like which websites are opened, through Kubernetes and Google Chrome. Data is sent every second from ActiveMQ, and ClickHouse can insert millions of data points per millisecond.
Software Development Engineer II at a financial services firm with 10,001+ employees
Real User
Top 20
May 21, 2024
Our use cases are for data analytics, both real-time and batch, and also for logging Clickstream data. We use it in our organization. We have it in our production environment.
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.
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...
My main use case for ClickHouse is data ingestion and for its OLAP properties, as we had use cases where database locks were slowing us down and because ClickHouse does not have that, we chose to use it. I could give you a quick, specific example of how I'm using ClickHouse for data ingestion where the lack of database locks helped us when we were parsing IOCs among other things, as a lot of that data has to be processed really quickly and ingested into the database for further processing and identifying which IOCs are compromised. ClickHouse helped us solve the problem that we were having and it's one of the two databases we used at Cyware—one was Postgres, the other was ClickHouse.
I have experience in ClickHouse ( /products/clickhouse-reviews ), and we also use Apache Druid ( /products/druid-reviews ), which has corporate support from Druid ( /products/druid-reviews ), along with data products in Hadoop ( /categories/hadoop ). We are currently exploring many platforms such as GMI, TKI, and Vertex. I use ClickHouse ( /products/clickhouse-reviews ) as a merchant side portal, especially when we started exploring how to use the data, which was coming from multiple sources such as logs, mainframe, Teradata ( /products/teradata-reviews ), and many file systems that come to the data lake. The real-time challenge was joining the data and providing more analytical queries for our merchants, who work throughout the year to improve GMB, sales, and ensure the right quantity of items is ordered at the right time. That's the challenge for the merchants, and we aim for fast analytical queries on larger databases, which is why we selected ClickHouse as our columnar OLAP database supporting real-time analytics with its own SQL interface. We have installed both local Docker ( /products/docker-37146-reviews ) versions, which are quite scalable, and usually connect with BI tools such as Grafana ( /products/grafana-reviews ), Superset ( /products/superset-reviews ), and Tableau ( /products/tableau-reviews ) while utilizing materialized views, DDLs partitions, and many other connectors with Python, such as ClickHouse connectors and drivers. It's exciting to see how ClickHouse has evolved, and we are evaluating ClickHouse Cloud while also having the on-premises version. We are already a customer of ClickHouse, with Sam's Club utilizing it on the merchant side while also exploring ClickHouse for consumers, primarily for user analytics, metrics, and streaming data analysis in ad tech. Additionally, we use custom analysis and metrics for fraud detection in payments and ad campaign metrics, with various teams utilizing it for ad campaign management and user behavior analytics, particularly on e-commerce sites focusing on customer behavior. It's extensively used due to its low latency, fast aggregations, and excellent OLAP columnar storage, featuring quick joins and real-time data visibility, making ClickHouse very appealing to us.
The main use case for ClickHouse is as a data warehouse for handling large volumes of data that exceed the capabilities of traditional databases like Postgres. I use it for creating dashboards and performing analytical tasks such as determining the total number of orders, average order value, and evaluations and ratios for various stores. I deploy ClickHouse both on the cloud provided by ClickHouse itself and on-premises for IoT and similar data tasks.
Our company had about nine platforms, each with its own database and data. We had to gather all these data in one database and just one table. We used Apache Superset to integrate this database with the business intelligence tool. We had too many choices or options initially for the database engine. We initially tested a database, and its performance was good. When we tried ClickHouse, we switched to it immediately because the performance was really amazing. When we had a huge amount of data, about five or six gigabytes in just one table, and we used ClickHouse to deduplicate some duplicated entries or records.
We use ClickHouse for a passive monitoring system in telecommunications. It is used to record primary data from the mobile network technology.
I used ClickHouse to collect data, put it in the database, and then analyze it to find insights. The main advantage is that I can install it on my computer instead of using cloud-based solutions, so I don't have to pay for every query like with Google or Amazon cloud services.
We use the solution not just as an analytics database but also as a data warehouse. A lot of our internal services communicate with the tool. Our analytics and ML teams also use the solution. It's the hub of our company.
We use it to store live data and for tracking and monitoring every action on a PC, like which websites are opened, through Kubernetes and Google Chrome. Data is sent every second from ActiveMQ, and ClickHouse can insert millions of data points per millisecond.
Our use cases are for data analytics, both real-time and batch, and also for logging Clickstream data. We use it in our organization. We have it in our production environment.