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From Data to Insights with Google Cloud Platform

Learn how to derive insights through data analysis and visualisation using the Google Cloud Platform on this two-day advanced data insights course.

2 day course

Supporting material

Classroom, Virtual, Private

Classroom

Face to face, interactive classroom training run from our global training centres.

Virtual Classroom

A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a Google Cloud Partner, we’ll share best practice on how you can use the GCP tools efficiently to query and process petabytes of data in seconds.

Jellyfish has been selected by Google to facilitate the delivery of this two-day instructor led course. All of our trainers are experienced practitioners, so you can learn with total confidence.

The course features interactive scenarios and hands-on labs where you will explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets.

We’ll cover data loading, querying, schema modelling, optimising performance, query pricing, and data visualisation.

This From Data to Insights with Google Cloud Platform course is available at our training centre in The Shard, London and is part of the Google Cloud Platform Data Analyst Track. This course will be run over two consecutive days. We also offer private training at a location of your choice or via Virtual Classroom.

Course overview

Who should attend:

This course is intended for Data Analysts, Business Analysts and Business Intelligence professionals. Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform will also benefit from attending this course.

Walk away with the ability to:

Derive insights from data using the analysis and visualisation tools on Google Cloud Platform
Interactively query datasets using Google BigQuery
Load, clean, and transform data at scale
Visualise 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
Optimising data models and queries for price and performance

Prerequisites

To get the most of out of this course, you should have basic proficiency with ANSI SQL and completed the Data Engineering on Google Cloud Platform course.

Course agenda

Module 1: 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
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: Analyse 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
Visualisation 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
Optimise Queries for Cost
Lab: Calculate Google BigQuery Pricing

Module 5: Cleaning and Transforming your Data

Examine the 5 Principles of Dataset Integrity
Characterise 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: DStoring 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 Visualisation

Overview of Data Visualisation 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
Normalisation vs Denormalisation: 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 Visualisation 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: Optimising for Performance

Avoid Google BigQuery Performance Pitfalls
Prevent Hotspots in your Data
Diagnose Performance Issues with the Query Explanation map
Lab: Optimising and Troubleshooting Query Performance

Module 14: Advanced Insights

Introducing Cloud Datalab
Cloud Datalab Notebooks and Cells
Benefits of Cloud Datalab

Module 15: Data Access

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

Book this course

Other options available

Private classes

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All of our scheduled courses are available as private sessions, tailored to the needs of your team. These can be delivered at our own training centres globally, any location of your choice or via Virtual Classroom.

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