Architectural considerations for Pub/Sub and Managed Service for Apache Kafka

View the content before continuing.

Latihan ini adalah bagian dari kursus

Build Streaming Data Pipelines on Google Cloud

Lihat Kursus

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Build Streaming Data Pipelines on Google Cloud

SkillTag.level.intermediateSkillTag.label

5.0+

2 reviews

Mulai Kursus Gratis

This module introduces the fundamentals of building streaming data pipelines on Google Cloud, providing a foundation for the entire course. It begins by outlining the course's overall learning objectives and introducing a practical, hands-on scenario that will be used throughout the content and labs to make the concepts tangible.

Exercise 1: What's in it for me?

This module provides an introduction to streaming data use cases and architectures. You will learn about the applications and common architectural patterns for real-time data processing across four key scenarios: Streaming ETL, Streaming AI/ML, Streaming Application, and Reverse ETL.

Exercise 1: The power of streaming data pipelines with Google Cloud products

This module provides a comprehensive overview of building streaming data pipelines on Google Cloud, covering the core services for messaging, processing, and analysis. It's designed to give you a hands-on understanding of how these components work together in a cohesive, real-time architecture.

Exercise 1: Understanding the products Exercise 2: Architectural considerations for Pub/Sub and Managed Service for Apache Kafka

Latihan Saat Ini

Exercise 3: Stream data with pipelines - Esports Use case Exercise 4: Quiz 1 Question 1 Exercise 5: Quiz 1 Question 2 Exercise 6: Quiz 1 Question 3 Exercise 7: Dataflow: The processing powerhouse Exercise 8: Use Apache Beam and Bigtable to Enrich esports DLC Data Exercise 9: Quiz 2 Question 1 Exercise 10: Quiz 2 Question 2 Exercise 11: Quiz 2 Question 3 Exercise 12: BigQuery: The analytical engine Exercise 13: Stream E-sports Data with Pub/Sub and BigQuery Exercise 14: Quiz 3 Question 1 Exercise 15: Quiz 3 Question 2 Exercise 16: Quiz 3 Question 3 Exercise 17: Quiz 3 Question 4 Exercise 18: Bigtable: The solution for operational data Exercise 19: Monitor E-sports Chat with Streamlit Exercise 20: Quiz 4 Question 1 Exercise 21: Quiz 4 Question 2 Exercise 22: Quiz 4 Question 3

This module provides a comprehensive wrap-up of the course, summarizing the key concepts you've learned for building resilient and robust streaming data pipelines on Google Cloud.

Exercise 1: What did I walk away with?