CH-201
| Formats: | Asynchronous |
| Blended | |
| Online | |
| Onsite | |
| Part-time | |
| Level: | Intermediate |
| Prerequisites: | |
| Recommended Knowledge | |
| Solid SQL Skills. | |
| Basic Data Warehousing Concepts. | |
| Familiarity with Command Line Tools & Linux Basics. | |
Formats: We offer our training content in a flexible format to suit your needs. Contact Us if you wish to know if we can accommodate your unique requirements.
Level: We are happy to customize course content to suit your skill level and learning goals. Contact us for a customized learning path.
Data Warehousing with ClickHouse (CH-201)
Building High-Performance Analytical Databases for Real-time Insights in South Africa
For organisations in South Africa grappling with massive datasets and the demand for lightning-fast analytical queries, ClickHouse stands out as a game-changer. As a column-oriented database management system (DBMS) for online analytical processing (OLAP), ClickHouse is specifically engineered for high-performance ingestion and querying of large volumes of data, making it the ideal choice for modern data warehousing needs.
At Big Data Labs, based in Randburg, Gauteng, we recognise the transformative power of ClickHouse in today's data landscape. This Data Warehousing with ClickHouse course is meticulously crafted for data engineers, data architects, and BI professionals eager to master this powerful database. You will learn to design, implement, and optimise data warehouses that deliver unparalleled analytical speed, transforming your ability to derive real-time insights from your data.
Target Audience
This course is designed for technical professionals who need to handle high-volume analytical data with extreme speed:
Data Engineers & Big Data Developers
Building and optimising high-throughput data ingestion and analytical querying systems.
Data Architects
Designing scalable and high-performance data warehousing solutions using ClickHouse.
BI Developers & Analysts
Who work with massive datasets and require sub-second query response times for reporting.
DevOps Engineers & SREs
Managing and maintaining ClickHouse clusters for production analytical environments.
Prerequisite Skills
To benefit fully from this course, participants should have:
- Solid SQL Skills: Proficiency in writing and understanding SQL queries.
- Basic Data Warehousing Concepts: Familiarity with dimensional modeling, ETL/ELT principles.
- Familiarity with Command Line Tools & Linux Basics: Comfort with navigating and executing commands in a terminal environment.
What One Will Learn (Learning Outcomes)
Upon completion of this course, you will be able to:
- Understand ClickHouse Architecture: Grasp its columnar storage, distributed nature, and query execution model.
- Design Data Models for ClickHouse: Create efficient schemas using appropriate table engines and indexing strategies.
- Optimise Query Performance: Apply advanced techniques for writing blazing-fast analytical queries in ClickHouse.
- Implement High-Volume Data Ingestion: Master various methods for efficiently loading data into ClickHouse.
- Manage and Scale ClickHouse Clusters: Understand replication, sharding, and monitoring for robust deployments.
- Integrate with BI Tools: Connect ClickHouse to popular business intelligence and visualisation applications.
Target Market
This course is highly relevant for organisations in South Africa dealing with extreme data volumes and demanding real-time analytical capabilities across various industries:
Web Analytics & AdTech
Processing billions of events for real-time user behaviour and campaign performance analysis.
Monitoring & Telemetry
Analysing logs, metrics, and IoT data for operational intelligence and anomaly detection.
FinTech & Trading Platforms
Processing high-frequency transactional data for fraud detection and market analysis.
Gaming & Mobile Apps
Analysing user engagement, in-app events, and performance data at scale.
Cloud-Native Data Teams
Leveraging ClickHouse for cutting-edge analytical solutions in cloud environments.
Course Outline: Data Warehousing with ClickHouse
This course provides a comprehensive understanding of data warehousing principles and the practical application of ClickHouse for building high-performance analytical databases.
Module 1: Data Warehousing Fundamentals & ClickHouse Introduction
- Introduction to Data Warehousing: Concepts, benefits, and common use cases.
- Data Warehouse Architectures: Understanding different models and components.
- Dimensional Modeling: Star schema, snowflake schema, and design principles.
- ETL vs. ELT: Understanding the differences and selecting the right approach for ClickHouse.
- Introduction to ClickHouse: Origin, key features (columnar, OLAP, speed, open-source).
- ClickHouse Architecture: Distributed, columnar storage, vectorised query execution.
Module 2: ClickHouse Data Modeling and Table Engines
- ClickHouse Data Types and their implications for performance.
- Understanding Table Engines: In-depth look at the MergeTree family (MergeTree, SummingMergeTree, AggregatingMergeTree, CollapsingMergeTree, ReplacingMergeTree).
- ReplicatedMergeTree and Distributed tables for high availability and scalability.
- Designing schemas for ClickHouse: Denormalisation, flat tables for analytics, and best practices.
- Choosing the right primary key and `ORDER BY` clause for optimal query performance.
Module 3: Querying and Optimizing ClickHouse
- Basic and Advanced SQL in ClickHouse: Specific functions for arrays, nested data, and complex aggregations.
- Understanding ClickHouse Indexes: Primary Key, Data Skipping Indexes (minmax, set, bloom filter), and their usage.
- Partitioning and Sub-partitioning strategies for managing and pruning large tables.
- ClickHouse Query Execution Plan (`EXPLAIN`) for understanding query performance.
- Performance Tuning Best Practices: Server settings, hardware considerations, query patterns, and data distribution.
Module 4: Data Ingestion and ETL/ELT with ClickHouse
- Data Ingestion Methods: Using `INSERT` statements, `CSV`/`TSV` formats, HTTP API, `clickhouse-client`.
- Batch vs. Streaming Ingestion strategies for various use cases.
- Integrating ClickHouse with real-time data pipelines (e.g., Apache Kafka engine, File engine).
- Handling Data Transformations and Data Quality within ClickHouse ETL/ELT workflows.
Module 5: ClickHouse Cluster Management and Scaling
- Distributed Tables and Sharding: Designing for horizontal scalability across multiple nodes.
- Replication and Fault Tolerance in ClickHouse using ZooKeeper/Keeper.
- Cluster Configuration and Best Practices for production environments.
- Monitoring ClickHouse Clusters: Key metrics, tools (Grafana, Prometheus).
- Backup and Restore Strategies for large-scale ClickHouse deployments.
Module 6: Advanced Topics & Ecosystem Integration
- Materialized Views for pre-aggregations and accelerating complex queries.
- Working with Arrays and Nested Data Structures in ClickHouse.
- Integration with BI Tools (Grafana, Metabase, Tableau, Power BI) for visualisation.
- Using ClickHouse in the cloud: Managed services vs. self-hosting on cloud VMs.
- Common Use Cases and Industry Examples (e.g., web analytics, monitoring, IoT, cybersecurity).