Titolo del corso:

Google Cloud Professional Data Engineer

Contents:

Module 1: Intro to 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

Module 2: Analyzing Large Datasets with BigQuery

  • 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: BigQuery Basics

Module 3: Exploring your Public Dataset 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: Explore your Ecommerce Dataset with SQL in Google BigQuery

Module 4: Cleaning and Transforming your Data with Cloud Dataprep

  • 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: Creating a Data Transformation Pipeline with Cloud Dataprep

Module 5: Visualizing Insights and Creating Scheduled Queries

  • Overview of Data Visualization Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery

Module 6: Storing and Ingesting new Datasets

  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Ingesting New Datasets into BigQuery

Module 7: Enriching your Data Warehouse with JOINs

  • 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: Troubleshooting and Solving Data Join Pitfalls

Module 8: Partitioning your Queries and Tables for Advanced Insights

  • 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: Creating Date-Partitioned Tables in BigQuery

Module 9: Designing Schemas that Scale: Arrays and Structs in 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
  • Lab: Schema Design for Performance: Arrays and Structs in BigQuery

Module 10: Optimizing Queries for Performance

  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost

Module 11: Controlling Access with Data Security Best Practices

  • Data Security Best Practices
  • Controlling Access with Authorized Views

Module 12: Predicting Visitor Return Purchases with BigQuery ML

  • Intro to ML
  • Feature Selection
  • Model Types
  • Machine Learning in BigQuery
  • Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML

Module 13: Deriving Insights from Unstructured Data using Machine Learning

  • Structured vs Unstructured ML
  • Prebuilt ML models
  • Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
  • Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML

Module 14: Completion

  • Summary and course wrap-up

Livello:

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Durata: 5 giorni

Codice: GCP-DATA

Prossime date:

Modalità di Erogazione:

Instructor Led (ILT)
Live Virtual Training (LVT)
Blended (BLD)

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