Introduction
Google BigQuery for data and ML engineers: Introduction
()
1. BigQuery for Data Engineering and Machine Learning Engineering
Topics
()
Serverless, multifunction data platform
()
Architecture of BigQuery
()
Data engineering and machine learning in BigQuery
()
2. Data Ingestion in BigQuery
Topics
()
Batch data ingestion
()
Lab assignment: Batch data ingestion
()
Streaming ingestion
()
Lab assignment: Ingest streaming data
()
3. Data Quality and Data Exploration
Topics
()
SQL for data quality checks
()
Lab assignment: Data quality checks
()
DataFrames for data exploration
()
Lab assignment: data exploration
()
Cloud Dataproc Spark and BigQuery
()
4. Machine Learning with BigQuery
Topics
()
Introduction to machine learning
()
Machine learning workflow
()
5. Building Classification and Regression Models in BigQuery
Topics
()
Building and evaluating a classification model
()
Lab assignment: Build a classification model
()
Building and evaluating a regression model
()
Lab assignment: Build a regression model
()
6. Building a Time Series Predictive Model
Topics
()
Introduction to time series modeling
()
Building time series model in SQL
()
Lab building a time series model in SQL
()
7. Using Generative AI with BigQuery
Topics
()
Generative AI services in Google Cloud
()
Introduction to generative AI in BigQuery
()
Lab using generative AI tools in BigQuery
()
Working with text in BigQuery
()
Conclusion
Google BigQuery for data and ML engineers: Summary
()