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This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.

The training will be given in 3 days.

Course overview

Target audience:

• This course is intended for the following participants:
• Data analysts, business analysts, business intelligence professionals

Objectives:

• Derive insights from data using the analysis and visualization tools on Google Cloud Platform
• Load, clean, and transform data at scale with Google Cloud Dataprep
• Explore and Visualize data using Google Data Studio
• Troubleshoot, optimize, and write high performance queries
• Practice with pre-built ML APIs for image and text understanding
• Train classification and forecasting ML models using SQL with BQML

Prerequisites:

• To get the most of out of this course, participants should have:
• Basic proficiency with ANSI SQL

Content

Module 1: Introduction to Data on the Google Cloud Platform

• Highlight Analytics Challenges Faced by Data Analysts.
• Compare Big Data On-Premise vs on the Cloud.
• Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud.
• Navigate Google Cloud Platform Project Basics.
• Lab: Getting started with Google Cloud Platform.

Module 2: Big Data Tools Overview

• Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools.
• Demo: Analyze 10 Billion Records with Google BigQuery.
• Explore 9 Fundamental Google BigQuery Features.
• Compare GCP Tools for Analysts, Data Scientists, and Data Engineers.
• Lab: Exploring Datasets with Google BigQuery.

Module 3: Exploring your Data with SQL

• Compare Common Data Exploration Techniques.
• Learn How to Code High Quality Standard SQL.
• Explore Google BigQuery Public Datasets.
• Visualization Preview: Google Data Studio.
• Lab: Troubleshoot Common SQL Errors.

Module 4: Google BigQuery Pricing

• Walkthrough of a BigQuery Job.
• Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs.
• Optimize Queries for Cost.
• Lab: Calculate Google BigQuery Pricing.

Module 5: Cleaning and Transforming your Data

• Examine the 5 Principles of Dataset Integrity.
• Characterize Dataset Shape and Skew.
• Clean and Transform Data using SQL.
• Clean and Transform Data using a new UI: Introducing Cloud Dataprep.
• Lab: Explore and Shape Data with Cloud Dataprep

Module 6: Storing and Exporting Data

• Compare Permanent vs Temporary Tables.
• Save and Export Query Results.
• Performance Preview: Query Cache.
• Lab: Creating new Permanent Tables.

Module 7: Ingesting New Datasets into Google BigQuery

• Query from External Data Sources.
• Avoid Data Ingesting Pitfalls.
• Ingest New Data into Permanent Tables.
• Discuss Streaming Inserts.
• Lab: Ingesting and Querying New Datasets.

Module 8: Data Visualization

• Overview of Data Visualization Principles.
• Exploratory vs Explanatory Analysis Approaches.
• Demo: Google Data Studio UI.
• Connect Google Data Studio to Google BigQuery.
• Lab: Exploring a Dataset in Google Data Studio.

Module 9: Joining and Merging Datasets

• Merge Historical Data Tables with UNION.
• Introduce Table Wildcards for Easy Merges.
• Review Data Schemas: Linking Data Across Multiple Tables.
• Walkthrough JOIN Examples and Pitfalls.
• Lab: Join and Union Data from Multiple Tables.

Module 10: Advanced Functions and Clauses

• Review SQL Case Statements.
• Introduce Analytical Window Functions.
• Safeguard Data with One-Way Field Encryption.
• Discuss Effective Sub-query and CTE design.
• Compare SQL and Javascript UDFs.
• Lab: Deriving Insights with Advanced SQL Functions.

Module 11: Schema Design and Nested Data Structures

• Compare Google BigQuery vs Traditional RDBMS Data Architecture.
• Normalization vs Denormalization: Performance Tradeoffs.
• Schema Review: The Good, The Bad, and The Ugly.
• Arrays and Nested Data in Google BigQuery.
• Lab: Querying Nested and Repeated Data.

Module 12: More Visualization with Google Data Studio

• Create Case Statements and Calculated Fields.
• Avoid Performance Pitfalls with Cache considerations.
• Share Dashboards and Discuss Data Access considerations.

Module 13: Optimizing for Performance

• Avoid Google BigQuery Performance Pitfalls.
• Prevent Hotspots in your Data.
• Diagnose Performance Issues with the Query Explanation map.
• Lab: Optimizing and Troubleshooting Query Performance.

Module 14: Data Access

• Compare IAM and BigQuery Dataset Roles.
• Avoid Access Pitfalls.
• Review Members, Roles, Organizations, Account Administration, and Service Accounts.

Module 15: Notebooks in the Cloud

• Cloud Datalab.
• Compute Engine and Cloud Storage.
• Lab: Rent-a-VM to process earthquakes data.
• Data Analysis with BigQuery.

Module 16: How Google does Machine Learning

• Introduction to Machine Learning for analysts.
• Practice with Pretrained ML APIs for image and text understanding.
• Lab: Pretrained ML APIs.

Module 17: Applying Machine Learning to your Datasets (BQML)

• Building Machine Learning datasets and analyzing features.
• Creating classification and forecasting models with BQML.
• Lab: Predict Visitor Purchases with a Classification Model in BQML.
• Lab: Predict Taxi Fare with a BigQuery ML Forecasting Model.