ClickHouse in Action: Real-Time Data Mastery

Course Overview

ClickHouse is an open-source, high-performance columnar database management system designed for Online Analytical Processing (OLAP) and real-time analytics on large datasets. This course provides participants with a comprehensive understanding of ClickHouse architecture, core concepts, practical operations, and key features including its powerful query capabilities, data loading techniques, clustering for scalability, and essential observability practices. Prerequisites include basic SQL knowledge and comfort with the Linux command line, as the course combines theoretical explanations with extensive hands-on labs to build practical skills.

Who is this course for

This course is ideal for Data Engineers, Backend Developers, Database Administrators, Site Reliability Engineers (SREs), DevOps Engineers, and Data Analysts who need to deploy, manage, or interact with ClickHouse for high-performance analytics, log processing, or handling large volumes of operational data.

Key Benefits

Harness ClickHouse's speed for near real-time data analytics and reporting.
Efficiently store and query massive datasets leveraging columnar storage and compression.
Gain practical skills to operate, manage, and scale ClickHouse deployments effectively.
Understand best practices for schema design, query optimization, and cluster management.
Learn from instructors with real-world experience deploying and managing ClickHouse at scale.

Learning Objectives

By the end of this course, you will be able to:

Understand ClickHouse's columnar architecture and its benefits for OLAP workloads.
Install, configure, and connect to a ClickHouse instance or cluster.
Design efficient schemas using appropriate data types and the MergeTree family of table engines.
Load data effectively into Clickhouse using various formats and methods.
Write complex analytical queries using ClickHouse SQL dialect, including aggregations and functions.
Configure and manage ClickHouse clustering for horizontal scalability and high availability.
Monitor ClickHouse performance and health using system tables and observability tools.

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Format

Prerequisites

Course contents

Day 1: Foundations, Schema, and Data Ingestion

Module 1: ClickHouse Introduction & Core Concepts

Understanding OLAP vs. OLTP workloads
ClickHouse Architecture Overview (Distributed nature, components)
Key Concept: Columnar Storage (Rows vs. Columns) and its advantages
Vectorized Query Execution Fundamentals
Everyday Use Cases (Web Analytics, Logs, Time Series, Reporting)

Module 2: Installation, Connecting, and Basic Schema

Installation methods: Docker, Packages, Developer mode
Connecting using clickhouse-client and other tools (e.g., DBeaver)
Working with Databases (CREATE, SHOW, DROP)
Creating basic Tables (CREATE TABLE).Understanding server configuration basics (config.xml, users.xml)
Understanding server configuration basics (config.xml, users.xml)

Module 3: Data Types & MergeTree Family Engines

Overview of ClickHouse Data Types (Numeric, String, Date/Time, Array, Nullable, etc.)
Introduction to Table Engines concept
Deep dive into the MergeTree engine: primary keys, sorting keys, and partitioning
Exploring ReplacingMergeTree for de-duplication scenarios
Understanding SummingMergeTree and AggregatingMergeTree for pre-aggregation

Module 4: Loading Data into ClickHouse

Using the INSERT INTO ... VALUES statement for small datasets
Bulk loading data using INSERT INTO ... FORMAT (CSV, TSV, JSONEachRow)
Loading data from files using clickhouse-client
Integrating with external sources (e.g., Kafka Engine basics, S3 table function overview)
Best practices for efficient data ingestion

Day 2: Querying, Clustering, and Operations

Module 5: ClickHouse Clustering: Sharding & Distributed Tables

Clustering Architecture: Shards and Replicas concepts
Role of {Zoo,Clickhouse}Keeper in managing cluster state and replication.
Configuring cluster topology in config.xml
Using the Distributed table engine to query across shards
Understanding the Distributed DDL Queries

Module 6: Replication & High Availability

Understanding the need for data replication and fault tolerance
Using the ReplicatedMergeTree family of engines
Configuring replicas and clickhouse-keeper paths for tables
Monitoring replication status and lag
Handling replica recovery and consistency considerations

Module 7: Querying Data: SELECT, Aggregations, and Functions

Writing SELECT queries with WHERE, ORDER BY, LIMIT
Performing powerful aggregations using GROUP BY and aggregate functions (count, sum, avg, uniq, etc.)
Utilizing common ClickHouse functions (String, Date/Time, Array, Conditional)
Understanding JOIN syntax and performance considerations in ClickHouse
Working with subqueries and WITH clauses

Module 8: Observability, Monitoring & System Tables

Introduction to key ClickHouse system tables (system.metrics, system.events, system.query_log)
Monitoring query performance and resource usage using system.query_log
Identifying essential server metrics (CPU, Memory, Disk I/O, Network)
Integrating ClickHouse metrics with tools like Prometheus & Grafana (conceptual overview)
Basic troubleshooting using logs and system tables

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