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Introduction to BigQuery

Course Summary

The Introduction to BigQuery training course teaches students how to query and process petabytes of data in seconds and about data analysis that scales automatically as data grows.

The course begins with teaching participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course then features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course concludes with covering data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

Purpose
 Learn to use Google's BigQuery to explore and gain insights from large datasets.
Audience
 Developers who work with Big Data, especially public datasets.
Role
Business Analyst - Software Developer - Technical Manager - Web Developer
Skill Level
Intermediate
Style
Hack-a-thon - Learning Spikes - Workshops
Duration
2 Days
Related Technologies
Google Cloud

 

Productivity Objectives
  • Derive insights from data using the analysis and visualization tools on Google Cloud Platform
  • Interactively query datasets using Google BigQuery
  • Load, clean, and transform data at scale
  • Visualize data using Google Data Studio and other third-party platforms
  • Distinguish between exploratory and explanatory analytics and when to use each approach
  • Explore new datasets and uncover hidden insights quickly and effectively
  • Optimize data models and queries for price and performance

What You'll Learn:

In the Introduction to BigQuery training course, you'll learn:
  • Introduction to Data on the Google Cloud Platform
    • Highlight Analytics Challenges Faced by Data Analysts
    • Compare Big Data On-Premises 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 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
  • Storing and Exporting Data
    • 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
    • Walkthrough 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
  • Advanced Insights
    • Introducing Cloud Datalab
    • Cloud Datalab Notebooks and Cells
    • Benefits of Cloud Datalab
  • Data Access
    • Compare IAM and BigQuery Dataset Roles
    • Avoid Access Pitfalls
    • Review Members, Roles, Organizations, Account Administration, and Service Accounts
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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