Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.

Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way.

You'll learn how to:

• Employ best practices in building highly scalable data and ML pipelines on Google Cloud


• Automate and schedule data ingest using Cloud Run


• Create and populate a dashboard in Data Studio


• Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery


• Conduct interactive data exploration with BigQuery


• Create a Bayesian model with Spark on Cloud Dataproc


• Forecast time series and do anomaly detection with BigQuery ML


• Aggregate within time windows with Dataflow


• Train explainable machine learning models with Vertex AI


• Operationalize ML with Vertex AI Pipelines