CPB200: Google BigQuery for Data Analysts, Toronto

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Google BigQuery for Data Analysts


Course Length: 3 days

Course Description

This 3 day instructor-led class introduces participants to Google BigQuery. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to use BigQuery to analyze data sets no matter how large, at the performance and scale of Google. In this course you model data for BigQuery, load data into BigQuery, query data, and visualize data.


At the end of this course, participants will be able to:

  • Integrate Google BigQuery into your big data analysis strategy
  • De-normalize relational data for efficient analysis in BigQuery
  • Transform, stage, and load data into BigQuery
  • Write BigQuery SQL queries
  • Use BigQuery functions and user-defined functions
  • Optimize query performance with efficient modeling, caching, destination tables, table decorators, and table range operators
  • Control access to data using Access Control Lists
  • Export query results
  • Integrate BigQuery with spreadsheets, third-party tools, and Google Analytics Premium data

Who Should Attend

Data Analysts and Data Scientists.


Before attending this course, you should have:

  • Completed CP100A: Google Cloud Platform Fundamentals
  • Experience with data analysis using SQL
  • Experience with business intelligence, reporting, and data visualization
  • Familiarity with extract, transform, and load (ETL) activities
  • Familiarity with data modeling

Course Agenda

Module 1: Introduction to BigQuery

  • Examine the current state big data landscape
  • Exploit the cloud for big data
  • Define what is BigQuery
  • Explore BigQuery use cases

Module 2: BigQuery Functional Overview

  • Organize BigQuery projects
  • Store data in BigQuery
  • Leverage BigQuery architecture
  • Interact with BigQuery from the web console and command API

Module 3: BigQuery Fundamentals

  • Define BigQuery table schemas
  • De-normalize relational data structures for efficient processing
  • Execute BigQuery jobs
  • Maximize performance and reduce cost with destination tables and caching

Module 4: Transform, Stage, and Load Data

  • Prepare and transform data for upload into BigQuery
  • Stage and ingest data
  • Load data

Module 5: Pricing and Quotas

  • Understand the BigQuery pricing model
  • Calculate the cost of queries
  • Avoid quota limitations

Module 6: Clauses and Functions

  • Leverage user-defined functions

Module 7: Nested and Repeated Fields

  • Model big data schemas to optimize performance and cost
  • Define nested, repeated, and nested repeated fields using JSON
  • Query nested and repeated fields in BigQuery

Module 8: Tuning Query Performance

  • Explore how the BigQuery architecture impacts query processing
  • Analyze the effects of Join and Group By statements on performance
  • Optimize reads with table decorators and table ranges
  • Diagnose and resolve query performance issues

Module 9: Troubleshooting Errors

  • Recognize common BigQuery SQL errors
  • Prevent resource errors
  • Diagnose HTTP errors

Module 10: Access Control

  • Define access control lists (ACLs) to protect data
  • Understand project and dataset access controls
  • Apply views for row-level security

Module 11: Exporting Data

  • Export data for backups and for use with third-party tools
  • Run export jobs

Module 12: Interfacing with External Tools

  • Integrate BigQuery with Google Sheets
  • Analyze BigQuery data with R

Module 13: Working with Google Analytics Premium Data

  • Mine Google Analytics and Google AdSense data with BigQuery

Module 14: Data Visualization

  • Visualize data using Google Cloud Datalab
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