Why ClickHouse Should Be the Go-To Choice for Your Next Data Platform?
Are you looking for a fast and simple database management system for your data platform stack?
Recently, I was working on building a new Logs dashboard at Fossil to serve our internal team for log retrieval, and I found ClickHouse to be a very interesting and fast engine for this purpose. In this post, I’ll share my experience with using ClickHouse as the foundation of a light-weight data platform and how it compares to another popular choice, Athena. We’ll also explore how ClickHouse can be integrated with other tools such as Kafka to create a robust and efficient data pipeline.
ClickHouse?
ClickHouse is an open-source, distributed column-based database management system. It is designed to be highly performant and efficient, making it a great choice for data-intensive workloads. Compared to traditional relational databases, ClickHouse can query large datasets in seconds, making it perfect for analytics, machine learning, and other big data applications. With its comprehensive set of features and scalability, ClickHouse is a great choice for powering your data platform stack.
The MergeTree engine is one of the core features of ClickHouse and is the foundation of its scalability and performance. It is used to store data in distributed clusters and is able to efficiently query large datasets in a fraction of the time compared to traditional relational databases. Furthermore, the MergeTree engine is designed to provide high availability and fault-tolerance, ensuring that your data remains safe and secure even in the event of a failure.
CREATE TABLE raw_events (
event_time DateTime,
event_id String,
data String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY event_time
TTL event_time + INTERVAL 180 DAY
You can explore ClickHouse using the Playground without setting up by visiting https://play.clickhouse.com/play.
Streamline Your Data Pipeline with ClickHouse
ClickHouse can be integrated with other popular tools such as Apache Kafka and other data sources (Postgres, S3, MongoDB, …) to create a powerful and efficient data pipeline. By leveraging the power of these sources, data can be ingested, transformed, and stored in ClickHouse quickly and easily. Furthermore, ClickHouse can be used to analyze the data in real-time, giving you access to a wide range of analytics without the need for complex ETL processes. With its comprehensive set of features and scalability, ClickHouse is a great choice for powering your data platform stack.
The architecture design we have shown above provides an example of how ClickHouse can be integrated with Kafka to create an efficient data pipeline.
To see how to set this up in detail, please refer to the full blog post here.