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. Virtual LearningThis interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
COURSE OBJECTIVE:
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 • 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
TARGET AUDIENCE:
• 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
COURSE PREREQUISITES:
To get the most out of this course, participants should have: • Basic proficiency with ANSI SQL
COURSE CONTENT:
Module 1: Introduction to Data on the Google Cloud PlatformBefore and Now: Scalable Data Analysis in the CloudTopics Covered • 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 Sharpen the Tools in your Data Analyst toolkitTopics Covered • 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 BigQueryModule 3: Exploring your Data with SQL Get Familiar with Google BigQuery and Learn SQL Best PracticesTopics Covered • 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 ErrorsModule 4: Google BigQuery Pricing Calculate Google BigQuery Storage and Query Costs Topics Covered • 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 Wrangle your Raw Data into a Cleaner and Richer DatasetTopics Covered • 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 Create new Tables and Exporting Results Topics Covered • 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 Bring your Data into the CloudTopics Covered • 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 Effectively Explore and Explain your Data through Visualization Topics Covered • 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 StudioModule 9: Joining and Merging Datasets Combine and Enrich your Datasets with more DataTopics Covered • 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 TablesModule 10: Google BigQuery Table Deep Dive What sets Cloud Architecture apart?Topics Covered • 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 SnapshotsModule 11: Schema Design and Nested Data Structures Model your Datasets for Scale in Google BigQueryTopics Covered • 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 Studio Create Pixel-Perfect DashboardsTopics Covered • 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 StudioModule 13: Advanced Functions and Clauses Dive Deeper into Advanced Query Writing with Google BigQueryTopics Covered • 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 FunctionsModule 14: Optimizing for Performance Troubleshoot and Solve Query Performance ProblemsTopics Covered • Avoid Google BigQuery Performance Pitfalls • Prevent Hotspots in your Data • Diagnose Performance Issues with the Query Explanation map • Lab: Optimizing and Troubleshooting Query PerformanceModule 15: Advanced Insights Think, Analyze, and Share Insights like a Data Scientist Topics Covered • Distill Complex Queries • Brainstorm Data-Driven Hypotheses • Think like a Data Scientist • Introducing Cloud Datalab • Lab: Reading a Google Cloud Datalab notebookModule 16: Data Access Keep Data Security top-of-mind in the Cloud Topics Covered • Compare IAM and BigQuery Dataset Roles • Avoid Access Pitfalls • Review Members, Roles, Organizations, Account Administration, and Service Accounts
FOLLOW ON COURSES:
Gain a wider view of Google Cloud Platform using Big Data and ML Fundamentals (GO8325)
