Complete Guide to Google BigQuery for Data and ML Engineers

Course details

Data and the ability to analyze it and use it to build machine learning models are driving innovation and new ways of organizing work in businesses. Enter Google BigQuery, the widely used data platform for data warehousing, analytics, and machine learning. BigQuery is offered as a managed, serverless offering from Google Cloud that lets you spend less time maintaining infrastructure and more time building machine learning systems and extracting insights from data. In this course, join instructor Dan Sullivan as he shows you how to get the most out of BigQuery as a data or machine learning engineer. Along the way, discover essential skills for ingesting data, transforming data to prepare it for analysis, and building, evaluating, deploying, and monitoring models in production.

Concepts

0. Introduction

01 - Google BigQuery for data and ML engineers - Introduction

1. BigQuery for Data Engineering and Machine Learning Engineering

02 - Topics
03 - Serverless, multifunction data platform
04 - Architecture of BigQuery
05 - Data engineering and machine learning in BigQuery

2. Data Ingestion in BigQuery

06 - Topics
07 - Batch data ingestion
08 - Lab assignment - Batch data ingestion
09 - Streaming ingestion
10 - Lab assignment - Ingest streaming data

3. Data Quality and Data Exploration

11 - Topics
12 - SQL for data quality checks
13 - Lab assignment - Data quality checks
14 - DataFrames for data exploration
15 - Lab assignment - data exploration
16 - Cloud Dataproc Spark and BigQuery

4. Machine Learning with BigQuery

17 - Topics
18 - Introduction to machine learning
19 - Machine learning workflow

5. Building Classification and Regression Models in BigQuery

20 - Topics
21 - Building and evaluating a classification model
22 - Lab assignment - Build a classification model
23 - Building and evaluating a regression model
24 - Lab assignment - Build a regression model

6. Building a Time Series Predictive Model

25 - Topics
26 - Introduction to time series modeling
27 - Building time series model in SQL
28 - Lab building a time series model in SQL

7. Using Generative AI with BigQuery

29 - Topics
30 - Generative AI services in Google Cloud
31 - Introduction to generative AI in BigQuery
32 - Lab using generative AI tools in BigQuery
33 - Working with text in BigQuery

Conclusion

34 - Google BigQuery for data and ML engineers - Summary

Complete Guide to Google BigQuery for Data and ML Engineers

Authors

Dan Sullivan

Pearson