Real-Time Analytics with ClickHouse – Level 1 is the foundational entry point into one of the fastest-growing open-source databases in the world. Across three in-depth modules, you will go from asking "What is ClickHouse?" to a working, queryable dataset running in ClickHouse Cloud — ready to serve as the basis for a real-world proof of concept.

By the end of Level 1, you will have the conceptual framework and hands-on experience needed to begin building performant, scalable analytics solutions on their own data.

This course is designed for engineers, practitioners, and architects who are new to ClickHouse and want to understand not just how to use it, but why it works the way it does. Understanding ClickHouse's architecture is what separates a fast, well-modeled deployment from one that runs into problems — and Level 1 lays that critical foundation.

 

Module 1: Introduction to ClickHouse
This module sets the stage for the entire course by answering the fundamental question: What is ClickHouse, and why should I care? You are introduced to ClickHouse from the ground up — its origins, its purpose, and the types of problems it is uniquely suited to solve.

 

What You’ll Learn:

 

What ClickHouse Is 

ClickHouse is an open-source (Apache 2.0), column-oriented SQL database designed for OLAP (Online Analytical Processing) workloads. It is purpose-built for ingesting large volumes of data very quickly and querying that data as fast as possible. The name itself originates from its first use case: a clickstream data warehouse.

A Helpful Mental Model 

Think of traditional databases (Postgres, Oracle, MySQL) as cars — familiar, flexible, great for everyday tasks. ClickHouse is an airplane: it's built for speed over long distances and massive workloads. You still need a "car" (transactional database) for everyday transactional tasks, but when you need analytical power at scale, ClickHouse is the tool for the job.

Key Use Cases

1. Real-Time Analytics – Applications that need to process and surface large volumes of event data quickly.
2. Observability – Storing and querying logs, metrics, and traces (ClickHouse's ClickStack uses OpenTelemetry + HyperDX)
3. Data Warehousing – Centralizing data from multiple sources (Salesforce, transactional DBs, files) for analysis
4. Machine Learning / GenAI – Feature stores, LLM integrations, and MCP server connectivity

 

Module 2: Deep Dive into ClickHouse Architecture

This is the most critical module of the entire Level 1 course. You will learn how to use ClickHouse effectively by understanding its architecture. This module covers the four foundational concepts that make ClickHouse fast: column storage, parts, granules, and the primary index.

 

What You’ll Learn:

 

Table Engines 

Every table in ClickHouse must have an engine that determines how and where data is stored. There are many engines:

• Integration engines (S3, PostgreSQL, Kafka, MySQL, Delta Lake, Iceberg, etc.) — for connecting to external data sources
• MergeTree family — the core engine for storing data inside ClickHouse

Column-Oriented Storage
Unlike row-oriented databases (Postgres, Oracle, MySQL) where entire rows are stored together, ClickHouse stores each column in its own file (e.g., price.bin, id.bin).

Parts - How Inserts Work

Every INSERT statement creates a new part — an immutable folder on disk containing the column files for just the rows in that insert.

Granules: The Unit of Query Processing

ClickHouse never processes one row at a time. It divides data into fixed-size granules of 8,192 rows each. Every query is broken down into granule-sized chunks, which are then distributed across CPU threads in parallel. This is how ClickHouse can query billions of rows in milliseconds — it spreads the work across all available CPUs simultaneously.

The Primary Key and Primary Index 

This is the most important design decision you'll make for any ClickHouse table. 

Partitioning vs. Primary Key 

An important distinction: partitioning in ClickHouse is for data management (e.g., dropping old data by month), not primarily for query speed.

 

Module 3: Inserting Data into ClickHouse

With a solid understanding of architecture from Module 2, you are now ready to learn the many ways data can be brought into ClickHouse. From files in cloud object storage to streaming pipelines to full database synchronization.

 

What You’ll Learn:

The Fundamental Rule - everything is an INSERT 

No matter how data arrives in ClickHouse, it ultimately ends up in a table via an INSERT command. The ecosystem of table functions, table engines, and ClickPipes are all different mechanisms to make that INSERT happen conveniently and efficiently.

Table Functions vs. Table Engines

• Table Functions (e.g., s3(), url(), postgresql(), mysql()) are used for one-off queries — they point to an external source and let you query or insert from it without persisting any metadata. Great for ad hoc exploration.
• Table Engines (e.g., S3, PostgreSQL, Kafka, S3Queue) create a persistent table definition that stores connection metadata, making it convenient for repeated access.

Input/Output Formats ClickHouse supports over 100 input and output data formats.

Materialized Views as Insert Triggers 

A Materialized View in ClickHouse is fundamentally an insert trigger. When data is written to a source table (like an S3Queue table or a Kafka engine table), the materialized view intercepts that data, optionally transforms it, and writes it to a destination MergeTree table. This is the standard pattern for streaming ingestion in ClickHouse.

Schema Inference

ClickHouse can automatically infer the schema of an external file or data source, making it easy to bootstrap a table definition. You will learn to use the schema_inference_make_columns_nullable = 0 setting to avoid unwanted Nullable columns in the auto-generated schema.

 

Upon successfully completing the quizzes at the end of all three modules, you will earn the ClickHouse Database Associate credential, shareable on LinkedIn and other professional networks.