Google Professional Data Engineer 2024 E-Learning

The Google Professional Data Engineer 2024 Training is designed to prepare professionals for the Google Professional Data Engineer certification exam, equipping them with essential skills to manage, secure, and transform data on Google Cloud Platform (GCP). This course is tailored for those who want to excel in data engineering roles, working on tasks such as designing and building data processing systems, ensuring data quality and reliability, and enabling machine learning solutions on GCP.

Key Learning Areas

• Data Storage and Processing: You will learn to design and implement data processing systems using Google Cloud tools like BigQuery, Cloud Storage, and Dataflow, focusing on efficient and scalable data handling.
• Machine Learning: The course includes hands-on labs for setting up machine learning workflows, using tools such as TensorFlow and BigQuery ML, allowing for integration of ML models into data systems.
• Data Security and Compliance: Learners gain expertise in implementing data security best practices, including IAM (Identity and Access Management) policies, encryption, and audit tracking to protect sensitive data and ensure compliance with industry regulations.
• Data Pipeline Optimization: The training covers designing and optimizing ETL pipelines and batch and streaming data workflows, ensuring data consistency and reliability for various use cases.

This award-winning e-learning includes an official practice exam, hands-on labs, and access to an online mentor, guiding you toward certification and professional success. This course is ideal for data engineers, data scientists, and IT professionals looking to deepen their GCP data engineering skills and enhance their career opportunities.

Course content

This e-learning collection contains 15courses (20 hours +).

1. Designing data processing systems

In this track, the focus will be on what it takes to become a Google Cloud Data Engineer, navigating the Google Cloud platform, building data structures, and planning data pipelines.
Courses:

GCP Data Engineer Pro: Becoming a Google Cloud Data Engineer

Course: 39 Minutes

• Course Overview
• The Role of Data Engineer
• Planning for the Professional Data Engineer Exam3
• Data Engineer vs. Other Cloud Roles
• Data Types and Storage
• Data Processing
• Data Analysis
• Machine Learning
• Mapping Requirements to Architecture
• Course Summary

GCP Data Engineer Pro: Navigating the Google Cloud Platform

Course: 1 Hour, 5 Minutes

• Course Overview
• Basic Google Cloud Platform (GCP) Services
• Regions and Zones
• Defining Identity with IAM Roles, Users, Service Accounts, and Policies
• Creating a Free Tier GCP Account
• The Google Cloud Console and gcloud CLI
• Setting Up Core Identity and Access Elements in Google Cloud
• Managing with the Google Cloud Console
• Managing with the gcloud CLI
• Cloud Key Management Service (KMS) on GCP
• Creating Cloud KMS Key Rings and Symmetric Encryption Keys
• Course Summary

GCP Data Engineer Pro: Building Robust Data Structures

Course: 1 Hour, 24 Minutes

• Course Overview
• Data Storage Architectures
• Designing the Data Model for a Data Warehouse
• Data Normalization in a Data Warehouse
• Data Access Defines Data Warehouse Architecture
• Discovering Data for a Google Cloud Platform (GCP) Data Lake
• Best Practices for Cost Control to a GCP Data Lake
• Best Practices for Access to a GCP Data Lake
• Processing Data in a Data Lake
• Monitoring on GCP
• Building a Data Mesh on GCP
• Building Governance for a GCP Data Mesh
• ELT and ETL
• Course Summary

GCP Data Engineer Pro: Creating a Pipeline of Services

Course: 1 Hour, 12 Minutes

• Course Overview
• Data Pipeline Structure
• Google Data Storage Services
• Google Data Ingestion Services
• Hadoop and Hadoop Distributed File System (HDFS)
• Apache Kafka
• Google Batch Data Processing
• Google Streaming Data Processing
• Apache Beam
• Google BigQuery
• MapReduce and Apache Spark
• Google Vertex AI
• Course Summary

2. Ingesting and processing the data

In this track, the focus will be on unstructured and structured data, data storage, and BigQuery data warehouse.
Courses:

GCP Data Engineer Pro: Google Cloud Unstructured Data

Course: 1 Hour, 41 Minutes

• Course Overview
• Unstructured Data Storage Options
• Google Cloud Storage
• Choosing Storage Classes
• Choosing Bucket Locations
• Cloud Storage Management
• Configuring Cloud Storage
• Object Lifecycle Management
• Object Security with Access Control
• Google Persistent Disk
• Google Filestore
• Google Memorystore for Redis
• Data Transfer Services
• Google Cloud Storage Pricing
• Unstructured Data Service Pricing
• Google Cloud Storage Troubleshooting
• Filestore Troubleshooting
• Course Summary

GCP Data Engineer Pro: Google Cloud OLTP Structured Data Storage

Course: 2 Hours, 1 Minute

• Course Overview
• Cloud SQL Overview
• Choosing the Best Cloud SQL Edition
• Core Management Tasks in Cloud SQL
• Provisioning Cloud SQL Instances
• Creating Databases on Cloud SQL
• Creating High Availability on Cloud SQL
• Recovering from Failure on Cloud SQL
• Migration to Google Cloud SQL
• Google Cloud Spanner
• Provisioning a Spanner Instance, Database, and Schema
• Defining Cloud Spanner Schema and Adding Data
• Pricing Out Structured Data Services
• Troubleshooting Cloud SQL
• Troubleshooting Cloud Spanner
• Course Summary

GCP Data Engineer Pro: Google Cloud Semi-structured Data

Course: 1 Hour, 51 Minutes

• Course Overview
• Firebase Mobile NoSQL
• Firebase Realtime Database
• Provisioning a Realtime Database in Firestore
• Indexed NoSQL with Firestore
• Firestore Data models
• Creating a Firestore Database
• Using Cloud Bigtable
• Bigtable Schema Design
• Provision a BigTable Instance
• Create a Bigtable Table
• Creating and Defining Data Storage in Bigtable
• Loading and Querying Data in Bigtable
• Pricing Out Semi-structured Data Services
• Troubleshooting Firebase
• Troubleshooting BigTable
• Course Summary

GCP Data Engineer Pro: BigQuery Data Warehouse

Course: 1 Hour, 57 Minutes

• Course Overview
• BigQuery Introduction
• Viewing BigQuery Data Public Datasets
• Navigating the BigQuery User Interface (UI) and Command Line Interface (CLI)
• Replication Options for Datasets in BigQuery
• Creating a Dataset
• BigQuery Tables and Views
• BigQuery Routines
• BigQuery Connections
• BigQuery Search and Vector Indexes
• Import Data into BigQuery
• Options to Batch Load Data into BigQuery
• Dataset Publishing for Sharing
• Pricing Models and BigQuery Cost Optimization
• Looker and Looker Studio
• Creating Reports in Looker
• Report Sharing from Looker
• Looker Pricing
• BigQuery Troubleshooting
• Looker Troubleshooting
• Course Summary

3. Storing the data

In this track, the focus will be on using Google data stream, messaging, dataset processing, and exploring machine learning and AI with Google.
Courses:

GCP Data Engineer Pro: Messaging with Pub/Sub

Course: 59 Minutes

• Course Overview
• Cloud Pub/Sub
• Cloud Pub/Sub vs. Pub/Sub Lite
• Cloud Pub/Sub Procedures
• Advanced Pub/Sub Procedures
• Pub/Sub Schemas
• Pub/Sub Client Libraries
• Configuring Cloud Pub/Sub
• Streaming Data through Pub/Sub to BigQuery
• Troubleshooting Pub/Sub
• Course Summary

GCP Data Engineer Pro: Using Google Datastream

Course: 50 Minutes

• Course Overview
• Datastream
• Datastream Sources and Destinations
• Network Controls for Datastream
• Creating Connection Profiles
• Stream Life Cycle and Management Steps
• Configuring Near Real-time Replication with Datastream
• Streaming Changes with DataStream to a Storage Bucket
• Datastream Pricing
• Datastream Troubleshooting
• Course Summary

GCP Data Engineer Pro: Dataset Processing

Course: 2 Hours, 15 Minutes

• Course Overview
• Comparing Google Cloud Dataproc, Dataprep, Dataflow, and Data Fusion
• Dataproc Cluster Options
• Creating a Dataproc Cluster and Running a Spark Job
• Dataprep Flows
• Profile, Transform, and Sample Data
• Using Dataprep
• Google Cloud Dataflow
• Building a Dataflow Pipeline
• Deploying a Dataflow Pipeline
• Dataflow Pipeline Options
• Google Cloud Data Fusion
• Data Fusion Setup
• Data Fusion Management
• Creating a Data Fusion Pipeline
• Pricing for Dataproc, Dataflow, Dataprep, and Data Fusion
• Dataproc Troubleshooting
• Dataflow Troubleshooting
• Course Summary

GCP Data Engineer Pro: Google Machine Learning and AI

Course: 1 Hour, 30 Minutes

• Course Overview
• Machine Learning Solutions for Business Problems
• Machine Learning Algorithms
• The Machine Learning Workflow
• Google Machine Learning Products
• Prebuilt Models
• Implementing a Prebuilt Model
• Custom Model Training
• Data Preparation for Machine Learning
• Training a Model Using Vertex AI
• Machine Learning Options for BigQuery
• Troubleshooting Issues in Machine Learning
• Course Summary

4. Preparing and using data for analysis

In this track, the focus will be on optimizing, monitoring, and troubleshooting data warehouses, and you will also explore data migrations.
Courses:

GCP Data Engineer Pro: Optimizing a Google Data Warehouse

Course: 1 Hour

• Course Overview
• Manage Google Cloud Platform (GCP) Costs
• Database and Smart Analytics Cost Optimization
• Control Resources in Dataproc Clusters
• Pipeline Automation with Cloud Composer or Workflows
• Creating and Running a DAG for Cloud Composer
• BigQuery Processing Optimization
• Workload Management for BigQuery
• Course Summary

GCP Data Engineer Pro: Monitoring and Troubleshooting Data Warehouses

Course: 1 Hour, 3 Minutes

• Course Overview
• Troubleshoot and Monitor with Google Cloud Monitoring
• Troubleshoot and Monitor with Google Cloud Logging
• Troubleshoot and Monitor with the Google BigQuery Admin Panel
• Manage Streaming Pipelines for Resiliency
• Load Balancing Jobs in Multiple Locations
• Duplicate Data Handling in Streaming Pipelines
• Data Replication for Cloud SQL8
• High Availability with Memorystore Redis Clusters
• Course Summary

GCP Data Engineer Pro: Data Migrations

Course: 59 Minutes

• Course Overview
• Data Migration Planning
• Plan Validation for Migration
• Database Migration Service
• Google Transfer Appliance
• BigQuery Data Transfer Service
• Integrate Other Services into a Successful Migration
• Data Migration Validation
• The Data Validation Tool (DVT)
• Data Governance Concerns During Data Migration
• Course Summary