Real-Time Analytics & ClickHouse Systems

1

Fundamentals of stream processing

Introduction to stream processing: difference between batch and real-time, fundamental architectures and patterns.

2

ClickHouse: fundamentals and architecture

Introduction to ClickHouse: column-oriented architecture, storage engine, and optimization for real-time analytics.

Prerequisites: 1

3

Ingestion patterns for realtime analytics

Ingestion patterns for realtime analytics. Core lesson of the Real-Time Analytics & ClickHouse Systems module with real problem, conceptual model, rigorous formalization, applied case, 3-level lab, and final checkpoint.

Prerequisites: 1

4

Change Data Capture (CDC) pattern

How to capture changes in transactional databases and propagate them in real-time.

Prerequisites: 1

5

Real-time dashboards and operational monitoring

Designing dashboards that update in real-time on event streams.

Prerequisites: 1

6

Materialized Views and Continuous Aggregates

Advanced pre-aggregation techniques for real-time queries on huge data volumes.

Prerequisites: 1

7

Alerting and anomaly detection on streams

Detect anomalies in real time: statistical patterns and practical implementation.

Prerequisites: 1

8

Cheat Sheet — Real-Time Analytics

Quick reference sheet for real-time analytics architectures with Kafka and ClickHouse.

Prerequisites: 1

9

Project: end-to-end real-time pipeline

Build a complete pipeline from Kafka to ClickHouse to live dashboards.

Prerequisites: 1