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High-Speed Data Engineering with ClickHouse

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210 minIntermediate09:00-12:30Max 20 participantsMelbourne
WorkshopClickHouseData EngineeringAnalyticsPerformance TuningData ModelingObject Storage

Description

As a data engineer, you know the value of fast, flexible analytics — and you've probably heard that ClickHouse is built for exactly that. But what does it really take to make it work in your stack?

Abstract

As a data engineer, you know the value of fast, flexible analytics — and you've probably heard that ClickHouse is built for exactly that. But what does it really take to make it work in your stack? In this instructor-led, hands-on workshop, we'll take you deep into the core mechanics of ClickHouse, from data modeling and ingestion to architecture and performance tuning. You'll walk away not just knowing how to use ClickHouse — but understanding why it works the way it does, and how to use it effectively in production. What you'll learn: - How ClickHouse handles massive scale through its architecture, indexing, and storage engine design - Strategies for modeling and inserting data that won't backfire as your volumes grow - How to work with structured files, object storage, and efficient query patterns - Use cases like analytics pipelines, observability systems, and cloud-native data lakes (time permitting) This training includes: - Getting Started: Install ClickHouse, run your first queries, explore the formats - Modeling Data: MergeTree tables, primary keys, granules, and when they matter - Ingest at Scale: Table engines, async inserts, schema design, and best practices - Use Cases: From raw files to real insights — hands-on walkthroughs Hands-on Labs: - Querying data directly from object storage - Modeling and inserting UK property data - Building tables with S3 and exploring transform patterns

Key Takeaways

  • Understand ClickHouse architecture and performance tuning
  • Learn data modeling strategies for massive scale
  • Master ingestion patterns and best practices
  • Get hands-on experience with real-world use cases
  • Explore analytics pipelines, observability systems, and cloud-native data lakes

Prerequisites

  • Basic understanding of data engineering concepts
  • Familiarity with SQL
  • Laptop with internet access for hands-on labs

Required Materials

  • Laptop with internet access
  • ClickHouse installation (instructions provided)
  • Basic SQL knowledge
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