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Overview

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.
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What You'll Learn

  • 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:

Who Should Attend

This class is intended for the following participants:

Data analysts, business analysts, business intelligence professionals.
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Prerequisites

To get the most out of this course, participants should have:
  • Basic proficiency with ANSI SQL

Learning Journey

Want to boost your career in Google Cloud? Click on the roles below to see the learning pathways, specially designed to give you the skills to succeed.

  • Business Intelligence Developer
  • Data Analyst

Business Intelligence Developer

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Fundamentals

GCPDI : From Data to Insights with Google Cloud Platform

Course Duration

3 Days

Certification

Intermediate

Expert

Data Analyst

icon-trainocate-certified.png

Fundamentals

GCPDI : From Data to Insights with Google Cloud Platform

Course Duration

3 Days

Certification

Intermediate

Expert

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.
This course is not associated with any Certification.

Frequently Asked Questions (FAQs)

  • Why get Google Cloud Platform (GCP) certified?

    Google Cloud certifications validate your expertise in cloud technologies and your proficiency in using Google Cloud Platform's vast array of services.

    These certifications are recognized globally and highly sought after by employers, as they demonstrate your ability to design, develop, and manage scalable and secure cloud solutions on GCP.

    Google Cloud-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    Google Cloud offers a variety of certification exams across different levels (Foundational, Associate, and Professional) covering various job roles and specializations.

    The exams typically consist of multiple-choice and multiple-select questions, as well as scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is Google Cloud Platform (GCP) certification valid for?

    Most Google Cloud certifications, including Professional-level certifications, are valid for two years from the date of passing the exam.

    The Cloud Digital Leader and Associate Cloud Engineer certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the same exam or an equivalent higher-level certification exam before your current certification expires.

    You will receive a notification from Google Cloud prior to your certification's expiration date.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Course Curriculum

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Exam & Certification

Exam & Certification

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Frequently Asked Questions

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