ClickHouse Engineering Resources

Explore the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems, examining how they serve unique purposes, the challenges of implementing OLAP, and guidelines for selecting the right system for various use cases. Real-world examples will illustrate when and why each system excels, providing practical insights for application.

In this guide, we’ll explore columnar databases. How do they differ from row-based databases? What are they good at? What are the advantages of using a column store?

In this guide, we'll learn all about real-time analytics - how does it compare to batch analytics, what are its main characteristics, use cases, and more.

This guide is all about real-time data visualization. We'll explore how it differs from normal visualization, see some examples, and learn about the tools we can use.

In this guide, we’ll learn all about time-series data and its use cases, time-series databases, and how to query time-series data.

In this guide, we'll learn about JSON, the types of databases that can store JSON, and how to work with JSON data in ClickHouse.

In this guide, we'll learn all about data applications - what are they, what are the main components, and why would you want to create one?

In this guide, we'll learn all about the Apache Avro and Apache Parquet big data formats.

In this guide, we explore the three main forms of data: structured data with rigid schemas like database tables, unstructured data like text and images with no predefined format, and semi-structured data like JSON that combines elements of both while maintaining flexibility.

In this guide, you'll learn how to build a Python dashboard using ClickHouse and Streamlit. We'll create a real-world example that visualizes Bluesky social media data, walking through everything from basic setup to interactive visualizations. Perfect for data scientists and analysts who want to share their insights through custom dashboards.