ClickHouse: The Reference Architecture for Real-Time Analytics & Big Data

TL;DR

Data is the new oil, but traditional data warehouses (like AWS Redshift) are often expensive, sluggish refineries. ClickHouse has revolutionized the OLAP (Online Analytical Processing) market. With columnar storage and vectorized query execution, it delivers answers to questions over billions of records in milliseconds. While cloud services tie costs to data volume, ClickHouse decouples performance from price through extreme compression and tiering.

1. The Architecture Principle: Columnar Storage & Vectorization

Traditional databases (Postgres, MySQL) store data row-wise. This is perfect for transactions (someone buys one item) but disastrous for analytics (calculate the revenue of all items). To sum a column, the database must read entire rows from disk.

ClickHouse stores data columnar.

• I/O Efficiency: If you want to know the average price, ClickHouse reads only the “price” column. 99% of the data is ignored.
• Vectorized Execution: ClickHouse uses modern CPU instructions (SIMD) to process data not individually, but in entire blocks (vectors). This makes it factors faster than traditional systems.

2. Core Feature: Extreme Compression and MergeTree

Storage space costs money—especially in the cloud. Since columns often contain similar data (e.g., the same date or region repeatedly), ClickHouse can compress this extremely efficiently.

• The MergeTree: The heart of ClickHouse is the MergeTree engine. Data is written unsorted at lightning speed and continuously sorted and merged in the background. This enables extremely high write rates (ingestion) while allowing high-speed read access.
• Cost Reduction: Through codecs and compression, ClickHouse often uses only 10-20% of the storage space compared to raw data or other databases.

3. Real-Time Ingestion vs. Batch

Many data warehouses like Redshift prefer “batch loads” (e.g., loading a CSV from S3 every 15 minutes). For modern use cases (live monitoring, ad-tech, user tracking), this is too slow.

ClickHouse is designed to consume data streams (e.g., from Kafka) in real-time. Data is queryable fractions of a second after arrival. There is no waiting for the nightly ETL job.

4. Operational Models Compared: AWS Redshift vs. ayedo Managed ClickHouse

Here it is decided whether your analytics costs scale linearly with success or if you can break the cost curve.

Scenario A: AWS Redshift (The Cost Spiral)

Redshift is the standard entry into AWS Analytics. It is deeply integrated but architecturally rigid.

• Costs for Compute & Storage: Although Redshift (RA3) separates storage and compute, you pay high premiums for the proprietary technology. Features like “Concurrency Scaling” (when many users query simultaneously) cause massive additional costs.
• The “Black Box” Query Optimizer: You have little influence on how Redshift plans queries. If a query is slow, AWS’s answer is usually: “Buy a bigger cluster.”
• Vendor Lock-in: Redshift uses a proprietary SQL dialect and storage formats. Exporting petabytes of data is lengthy and expensive (egress fees).

Scenario B: ClickHouse with Managed Kubernetes by ayedo

In the ayedo app catalog, ClickHouse is provided as a high-performance cluster.

• Tiered Storage: ClickHouse can be configured so that “hot” data (e.g., last 7 days) resides on extremely fast NVMe SSDs, while historical data is automatically offloaded to cost-effective S3 object storage. You pay NVMe prices only for what requires performance.
• Unfair Performance: On the same hardware (bare metal or EC2), ClickHouse often outperforms Redshift by a factor of 10 to 100 in analytical queries.
• Open Standards: ClickHouse is open source. You can export and import data at any time in open formats (Parquet, JSON). There are no artificial limits for concurrent queries.

Technical Comparison of Operational Models

FAQ: ClickHouse & Data Strategy

Can ClickHouse replace my PostgreSQL/MySQL database?

No. ClickHouse is an OLAP database (Online Analytical Processing), PostgreSQL is OLTP (Online Transaction Processing). ClickHouse is not built to modify (UPDATE) or delete (DELETE) individual rows, as would be necessary in a webshop. It is designed to add and analyze billions of rows. In a modern architecture, you use both: Postgres for the user profile, ClickHouse for the user activity logs.

How do I migrate from Redshift to ClickHouse?

The switch is often easier than expected since ClickHouse speaks SQL. The biggest difference lies in the data schema: In ClickHouse, data is often denormalized (fewer joins) to achieve maximum speed. Tools like clickhouse-local even allow reading and importing data directly from S3 (exported by Redshift).

Do I need Hadoop or Spark for ClickHouse?

No. This is one of the biggest advantages. ClickHouse is a single binary. It does not require a complex ecosystem like Hadoop, ZooKeeper (no longer mandatory in newer versions), or Java Virtual Machines. This makes operation extremely lean and resource-efficient compared to old-school big data stacks (HDFS).

Is the switch worthwhile for small data volumes?

For very small data volumes (< 10 GB), the overhead is almost irrelevant. But as soon as you store terabytes of logs, metrics, or events, Redshift becomes noticeably expensive. ClickHouse often allows you to retain data that would have to be deleted in Redshift due to cost reasons (“Retention Policy”) in ClickHouse cost-effectively for years.

Conclusion

To remain competitive in the age of big data, you need answers in real-time, not the next morning. AWS Redshift was the pioneer of cloud data warehouses but is often a cost trap for rapidly growing data volumes today. ClickHouse democratizes high-performance analytics. It enables companies to analyze petabytes of data on standard infrastructure without going bankrupt. With the ayedo Managed Stack, you get this raw power fully configured, including S3 tiering and backup strategy, while retaining full data sovereignty.