GCPDI - From Data to Insights with Google Cloud Platform

Overview

Duration: 3.0 days

Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. This certification & training 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.

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
Audience:

Content

The course includes presentations, demonstrations, and hands-on labs.

Module 1: 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.
Lab: Getting started with Google Cloud Platform.

Module 2: 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.
Lab: Exploring Datasets with Google BigQuery.

Module 3: 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.
Lab: Troubleshoot Common SQL Errors.

Module 4: Google BigQuery Pricing

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 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.
Lab: Explore and Shape Data with Cloud Dataprep.

Module 6: Storing and Exporting Data

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 BigQuery

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 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 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.
Lab: Join and Union Data from Multiple Tables.

Module 10: 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.
Lab: Deriving Insights with Advanced SQL Functions.

Module 11: 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.
Lab: Querying Nested and Repeated Data.

Module 12: 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.

Module 13: Optimizing for Performance

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 14: Data Access

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

Module 15: Notebooks in the Cloud

Cloud Datalab.
Compute Engine and Cloud Storage.
Lab: Rent-a-VM to process earthquakes data.
Data Analysis with BigQuery.

Module 16: How Google does Machine Learning

Introduction to Machine Learning for analysts.
Practice with Pretrained ML APIs for image and text understanding.
Lab: Pretrained ML APIs.

Module 17: Applying Machine Learning to your Datasets (BQML)

Building Machine Learning datasets and analyzing features.
Creating classification and forecasting models with BQML.
Lab: Predict Visitor Purchases with a Classification Model in BQML.
Lab: Predict Taxi Fare with a BigQuery ML Forecasting Model.

Audience

This class is intended for the following participants:

Data analysts, business analysts, business intelligence professionals.

Prerequisites

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

Basic proficiency with ANSI SQL

Certification

This course is not associated with any Certification.

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