ClickHouse Engineering Resources

Explore the top open source observability stacks for 2025. Compare ELK, LGTM, and unified observability solutions like ClickStack for cost, scale, and high-cardinality data.

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 article explores the suitability of MCP with Data Warehouses, and discusses the business and technical details you need to know to succeed

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.

Discover the fundamentals of log monitoring systems, exploring different log types, monitoring techniques, and modern tools, with practical insights into how organizations leverage solutions like ClickHouse to manage massive log volumes efficiently and cost-effectively.

In this guide, we’ll explore OpenTelemetry (OTel), a framework for collecting and standardizing telemetry data—metrics, logs, and traces—enhancing observability and performance monitoring in modern software systems.

In this guide, we'll explore telemetry data - the vital information that helps us understand, monitor, and improve our software systems through the collection of metrics, logs, and traces.

In this guide, we'll explore observability - the practice of understanding a system's internal state through its outputs, and how modern approaches are helping organizations gain deeper insights into their systems' behavior.

In this guide, we'll explore SIEM (Security Information and Event Management) - the central security system that collects, analyzes, and responds to security threats across your organization's entire infrastructure.

In this guide, we'll explore Application Performance Monitoring (APM) - the practice of tracking and analyzing application behavior in real-time to ensure optimal performance and user experience.

In this guide, we'll explore how teams monitor and debug their LLM applications, helping them understand everything from response accuracy and token usage to the complex reasoning chains of AI agents.

In this guide, we'll explore how organizations implement network monitoring to gain visibility, troubleshoot issues, and ensure optimal performance across distributed infrastructures.

In this guide, we'll explore how structured logging transforms traditional text-based logs into queryable data, enabling organizations to build powerful monitoring, analysis, and automation capabilities at scale.

In this guide, we'll explore the Iceberg, Delta Lake, and Hudi open table formats.

In this guide, we'll explore data catalogs for open table formats like Iceberg, Delta Lake, and Hudi, explaining how these metadata systems make modern data lakes more powerful and accessible.

The data lakehouse combines the best of data warehouses and data lakes into a unified architecture. We'll explore its key components, advantages, and how ClickHouse fits into this modern analytics platform.

Apache Iceberg transforms data lakes into robust lakehouse architectures with its high-performance table format. This article explores Iceberg's origins, key features like ACID transactions and schema evolution, and demonstrates how to query Iceberg tables in ClickHouse using both direct and catalog-based approaches.

In this guide, we’ll learn how to instrument the OpenAI client with OpenTelemetry (OTel) so that we can generate and collect observability data about our LLM calls.

What are the best alternatives to Splunk?

In this guide, we'll learn how to set up Apache Iceberg locally using PySpark.

In this guide, we'll learn about OpenLLMetry - the open-source observability framework for large language models.

Learn how databases compress data, the trade-offs between row and column storage, and why ClickHouse delivers industry-leading compression ratios.