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Course Overview
Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s 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.
Who Should Attend
Data Analysts, Business Analysts, Business Intelligence professionals
Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform
Course 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
Course Outline
1 - Introduction to Data on Google Cloud Platform
- 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
2 - Analyzing Large Datasets with BigQuery
- Data Analyst Tasks, Challenges, and Google Cloud Data Tools
- Fundamental BigQuery Features
- Google Cloud Tools for Analysts, Data Scientists, and Data Engineers
3 - Exploring your Data with SQL
- Common Data Exploration Techniques
- Use SQL to Query Public Datasets
4 - Cleaning and Transforming your Data with Dataprep
- 5 Principles of Dataset Integrity
- Dataset Shape and Skew
- Clean and Transform Data using SQL
- Introducing Dataprep by Trifacta
5 - Visualizing Insights and Creating Scheduled Queries
- Data Visualization Principles
- Common Data Visualization Pitfalls
- Google Data Studio
6 - Storing and Ingesting New Datasheets
- Permanent vs Temporary Data Tables
- Ingesting New Datasheets
7 - Enriching your Data Warehouse with JOINs
- Merge Historical Data Tables with UNION
- Introduce Table Wildcards for Easy Merges
- Review Data Schemas: Linking Data Across Multiple Tables
- JOIN Examples and Pitfalls
8 - Advanced Features and Partitioning your Queries
and Tables for Advanced Insights
- Advanced Functions (Statistical, Analytic, User-defined)
- Date-Partitioned Tables
9 - Designing Schemas that Scale: Arrays and Structs in BigQuery
- BigQuery Versus Traditional Relational Data Architecture
- ARRAY and STRUCT Syntax
- BigQuery Architecture
10 - Optimizing Queries for Performance
- BigQuery Performance Pitfalls
- Prevent Data Hotspots
- Diagnose Performance Issues with the Query Explanation Map
- Describe how to analyze and troubleshoot broken queries
11 - Controlling Access with Data Security Best Practices
- Hashing Columns
- Authorized Views
- IAM and BigQuery Dataset Roles
- Access Pitfalls
12 - Predicting Visitor Return Purchases with BigQuery ML
- Machine Learning on Structured Data
- Scenario: Predicting Customer Lifetime Value
- Choosing the Right Model Type
- Creating ML models with SQL
13 - Deriving Insights from Unstructured Data Using Machine Learning
- 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