Use Apache Beam and Bigtable to Enrich esports DLC Data

Deze oefening maakt deel uit van de cursus

Build Streaming Data Pipelines on Google Cloud

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Oefeninstructies

In this lab, you will use Apache Beam and BigTable to enrich esports DLC data, using a Jupyter Notebook in Vertex AI.

Deze oefening maakt deel uit van de cursus

Build Streaming Data Pipelines on Google Cloud

SkillTag.level.intermediateSkillTag.label

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Begin de cursus 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 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

Huidige oefening

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?