Mastering Data Analytics—From Batch to Real-Time Streaming with Kafka, R...

Mastering Data Analytics — From Batch to Real-Time Streaming with Kafka, Redis, MinIO, Python, PostgreSQL - Part6

Ready to crank up the velocity of your Data Lakehouse? In Part 6 of the Mastering Data Analytics series, we evolve our system from batch processing to a powerful, real-time streaming architecture. Say goodbye to stale data and hello to instant insights!

In this end-to-end tutorial, you'll learn how to build and deploy a complete "Speed Layer" using industry-standard tools. We'll walk through the entire data flow: from simulating live events with a Python generator, publishing them to Apache Kafka topics, and processing them in real-time with a stream processor. You'll see exactly how to use Redis for lightning-fast caching and how to load the processed data into PostgreSQL for durable storage.

This is not just theory—this is a hands-on guide to building the event-driven systems that power modern data platforms.

By the end of this video, you will have built and understood:

1.  A real-time data ingestion pipeline using **Apache Kafka**.

2.  A Python data generator to simulate realistic, streaming events (transactions, IoT, etc.).

3.  A robust Python stream processor to consume, validate, and route data from Kafka.

4.  How to use **Redis** as a real-time caching layer for instant metrics and alerts.

5.  The dual-write pattern: loading data into both **PostgreSQL** (for analytics) and Redis (for speed).

6.  The complete, end-to-end flow of a streaming event, from creation to storage, in under a second.

This video is perfect for data engineers, software developers, and analytics professionals looking to add real-time stream processing to their skill set.

https://www.youtube.com/watch?v=Y5mnsT0loXU