Introduction the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. Explore the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Key components of Google Cloud's infrastructure. Big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
Learning Objectives:
Identify how elements of the Google Cloud infrastructure have enabled big data and ML capabilities.
Identify the big data and machine learning products available on Google Cloud.
Explore a BigQuery dataset.
- Google Cloud infrastructure, Compute, Storage
- The history of big data and ML products
- Big data and ML product categories
Lab: Exploring a BigQuery Public Dataset
Reading list:
- The Google Cloud Training website
- Learning path: Data Engineering and Sma Analytics
- Learning path: Machine Learning and AI
Introduction to Google Cloud's solution to managing streaming data. End-to-end pipeline, data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.
Learning Objectives:
Describe an end-to-end streaming data workflow from ingestion to data visualization.
Identify modern data pipeline challenges and how to solve them at scale with Dataflow
Build collaborative real-time dashboards with data visualization tools.
Create a streaming data pipeline for a real-time dashboard with Dataflow.
- Big data challenges
- Message-oriented architecture
- Designing streaming pipelines with Apache Beam
- Implementing streaming pipelines on Cloud Dataflow
- Visualization with Looker Studio
Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
Reading list:
BigQuery - Google's fully-managed, serverless data warehouse. BigQuery ML, and the processes and key commands that are used to build custom machine learning models.
- Storage and analytics
- Querying TB of data in seconds
- Using BigQuery ML to predict customer lifetime value
- BigQuery ML project phases and key commands
Reading list: