Wednesday, 23. January 2019, Los Angeles, From Data to Insights with Google Cloud Platform, Los Angeles

from 23. January 2019 - 9:00
till 24. January 2019 - 18:00

Los Angeles

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Event description
From Data to Insights with Google Cloud Platform
(2 days)

Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course!
This two-day instructor-led class teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. A personal laptop is required for all workshops and will not be provided.
This course teaches participants the following skills:

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

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Distinguish between exploratory and explanatory analytics and when to use each approach
Explore new datasets and uncover hidden insights quickly and effectively
Optimizing data models and queries for price and performance

This class is intended for the following participants:

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

To get the most out of this course, participants should have:

Basic proficiency with ANSI SQL (reference)

Course Outline

Module 1: Introduction to Data on the Google Cloud PlatformBefore and Now: Scalable Data Analysis in the Cloud

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 throughAnalytics on the Cloud
Navigate Google Cloud Platform Project Basics
Lab: Getting started with Google Cloud Platform

Module 2: Big Data Tools OverviewSharpen the Tools in your Data Analyst toolkit

Walkthrough Data Analyst Tasks, Challenges, and Introduce GoogleCloud 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 DataGet Familiar with Google BigQuery and Learn SQL Best Practices

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 PricingCalculate Google BigQuery Storage and Query Costs

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 DataWrangle your Raw Data into a Cleaner and Richer Dataset

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 DataCreate new Tables and Exporting Results

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 BigQueryBring your Data into the Cloud

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 VisualizationEffectively Explore and Explain your Data through 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 DatasetsCombine and Enrich your Datasets with more Data

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: Google BigQuery Tables Deep DiveWhat sets Cloud Architecture apart?

Compare Data Warehouse Storage Methods
Deep-dive into Column-Oriented Storage
Examine Logical Views, Date-Partitioned Tables, and Best Practices
Query the Past with Time Travelling Snapshots

Module 11: Schema Design and Nested Data StructuresModel your Datasets for Scale in Google BigQuery

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: Advanced Visualization with Google Data StudioCreate Pixel-Perfect Dashboards

Create Case Statements and Calculated Fields
Avoid Performance Pitfalls with Cache considerations
Share Dashboards and Discuss Data Access considerations
Lab: Visualizing Insights with Google Data Studio

Module 13: Advanced Functions and ClausesDive Deeper into Advanced Query Writing with Google BigQuery

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 14: Optimizing for PerformanceTroubleshoot and Solve Query Performance Problems

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 15: Advanced InsightsThink, Analyze, and Share Insights like a Data Scientist

Distill Complex Queries
Brainstorm Data-Driven Hypotheses
Think like a Data Scientist
Introducing Cloud Datalab
Lab: Reading a Google Cloud Datalab notebook

Module 16: Data AccessKeep Data Security top-of-mind in the Cloud

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

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