Data Lakes and ClickHouse Integration - Understanding Open Table Formats and Real-time Analytics

Overview

Explore data lake integration with ClickHouse®, covering Parquet, Iceberg formats, and real-time analytics implementation using Apache Spark and Kafka for large-scale data processing.

Syllabus

• Introduction to Data Lakes
Overview of Data Lakes vs. Data Warehouses
Benefits of Data Lakes for Large-scale Analytics
Key Technologies Powering Data Lakes
• ClickHouse Overview
Introduction to ClickHouse and its Architecture
ClickHouse Configuration and Setup for Data Integration
Advantages of Using ClickHouse for Real-time Analytics
• Open Table Formats
Introduction to Parquet Format
Structure and Benefits of Parquet
Reading and Writing Parquet with ClickHouse
Introduction to Apache Iceberg Format
Features and Use Cases of Iceberg
Integration of Iceberg with ClickHouse
• Real-time Analytics with Apache Spark
Introduction to Apache Spark for Big Data Processing
Setting Up Spark for Integration with ClickHouse
Transforming Data on-the-fly using Apache Spark
• Real-time Data Streaming with Apache Kafka
Understanding Apache Kafka and Its Components
Kafka Setup and Best Practices for Data Lakes
Streaming Data into ClickHouse via Kafka
• Integrating Data Lakes with ClickHouse
Strategies for Efficient Data Loading
Query Optimization for Mixed Workloads
Case Studies and Examples of Data Lake Integration
• Hands-on Labs
Setting Up a Data Lake with ClickHouse
Practicing Data Format Conversion (Parquet, Iceberg)
Implementing Real-time Data Pipelines with Kafka and Spark
• Conclusion and Future Trends
Reviewing Key Learnings
Exploring Emerging Trends in Data Lakes and Real-time Analytics
Roadmap for Further Learning and Exploration
• Additional Resources and Reading
Recommended Books and Articles
Online Tutorials and Documentation
Community Forums and Support Channels

Subjects

Business