Data Insights with GCP Training Overview
This From Data to Insights with Google Cloud Platform training teaches participants how to mine, load, visualize, and extract insights from diverse Google BigQuery datasets. This GCP course covers data loading, querying, schema modeling, optimizing performance, query pricing, data visualization, and machine learning.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, we offer some courses as live, instructor-led online classes for individuals.
Objectives
- Derive insights from data using the analysis and visualization tools on Google Cloud
- 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
- Leverage pre-built ML APIs for image and text understanding
- Train classification and forecasting ML models using SQL with BQML
Prerequisites
All students must have basic proficiency with ANSI SQL.
Outline
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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
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
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
Google BigQuery Pricing
- Walkthrough of a BigQuery Job
- Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
- Optimize Queries for Cost
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
Storing and Exporting Data
- Compare Permanent vs Temporary Tables
- Save and Export Query Results
- Performance Preview: Query Cache
Ingesting New Datasets into Google BigQuery
- Query from External Data Sources
- Avoid Data Ingesting Pitfalls
- Ingest New Data into Permanent Tables
- Discuss Streaming Inserts
Data Visualization
- Overview of Data Visualization Principles
- Exploratory vs Explanatory Analysis Approaches
- Demo: Google Data Studio UI
- Connect Google Data Studio to Google BigQuery
Joining and Merging Datasets
- Merge Historical Data Tables with UNION
- Introduce Table Wildcards for Easy Merges
- Review Data Schemas: Linking Data Across Multiple Tables
- Walk through JOIN Examples and Pitfalls
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
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
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
Optimizing for Performance
- Avoid Google BigQuery Performance Pitfalls
- Prevent Hotspots in your Data
- Diagnose Performance Issues with the Query Explanation map
Data Access
- Compare IAM and BigQuery Dataset Roles
- Avoid Access Pitfalls
- Review Members, Roles, Organizations, Account Administration, and Service Accounts
Notebooks in the Cloud
- Cloud Datalab
- Compute Engine and Cloud Storage
- Data Analysis with BigQuery
How Google does Machine Learning
- Introduction to Machine Learning for analysts
- Practice with Pretrained ML APIs for image and text understanding
Applying Machine Learning to your Datasets (BQML)
- Building Machine Learning datasets and analyzing features
- Creating classification and forecasting models with BQML
Conclusion
Training Materials
All GCP training students receive comprehensive courseware.
Software Requirements
Students must have a modern web browser (ideally Chrome) and Internet access.
