Course Overview
TOPExplore ways to derive insights from data at scale using BigQuery, Google Clouds serverless, highly scalable, and cost-effective cloud data warehouse. 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.
Scheduled Classes
TOP| 02/05/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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| 08/27/25 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPDerive insights from data using the analysis and visualization tools on Google Cloud
- Load, clean, and transform data at scale with 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 BigQuery ML
Outline
TOP
Viewing outline for:
BigQuery Performance Pitfalls
- Prevent Data Hotspots
- Diagnose Performance Issues with the Query Explanation Map
- Hashing Columns
- Authorized Views
- IAM and BigQuery Dataset Roles
- Access Pitfalls
- Machine Learning on Structured Data
- Scenario: Predicting Customer Lifetime Value
- Choosing the Right Model Type
- Creating ML models with SQL
- ML Drives Business Value
- How does ML on unstructured data work?
- Choosing the Right ML Approach
- Pre-built AI Building Blocks
- Customizing Pre-built Models with AutoML
- Building a Custom Model
- Analytics Challenges Faced by Data Analysts
- Big Data On-premise Versus on the Cloud
- Real-world Use Cases of Companies Transformed Through Analytics on the Cloud
- Google Cloud Project Basics
- Data Analyst Tasks, Challenges, and Google Cloud Data Tools
- Fundamental BigQuery Features
- Google Cloud Tools for Analysts, Data Scientists, and Data Engineers
- Common Data Exploration Techniques
- Use SQL to Query Public Datasets
- 5 Principles of Dataset Integrity
- Dataset Shape and Skew
- Clean and Transform Data using SQL
- Introducing Dataprep by Trifacta
- Data Visualization Principles
- Common Data Visualization Pitfalls
- Google Data Studio
- Permanent Versus Temporary Data Tables
- Ingesting New Datasets
- Merge Historical Data Tables with UNION
- Introduce Table Wildcards for Easy Merges
- Review Data Schemas: Linking Data Across Multiple Tables
- JOIN Examples and Pitfalls
- Advanced Functions (Statistical, Analytic, User-defined)
- Date-Partitioned Tables
- BigQuery Versus Traditional Relational Data Architecture
- ARRAY and STRUCT Syntax
- BigQuery Architecture
Prerequisites
TOPBasic proficiency with ANSI SQL
Who Should Attend
TOPData Analysts, Business Analysts, Business Intelligence professionals
- Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud
