The Cloud Database Engineer certification focuses on managing and optimizing cloud-based databases. It covers database design, deployment, security, and performance tuning across various cloud platforms. This certification is ideal for professionals who want to specialize in cloud database management and support data-driven decision-making.
Instructor
Cloud Database Engineer Training | Learn & Get Certified
Curriculum
Become a certified Cloud Database Engineer! Gain expertise in cloud data management, security, and optimization. Enroll now for hands-on training & certification.
This four-day instructor-led class provides participants with a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demonstrations, and practical labs, you will learn to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning solutions. The course covers various types of data, including structured, unstructured, and streaming data.
Key Features:
Session by Certified Instructor
Advanced hands-on labs
Official training content
Industry-recognized certification
Interactive sessions
Course Objectives:
Design and build data processing systems on Google Cloud.
Process batch and streaming data by implementing auto-scaling data pipelines using Dataflow.
Derive business insights from large datasets using BigQuery.
Leverage unstructured data with Spark and ML APIs on Dataproc.
Enable instant insights from streaming data.
Introduce ML APIs and BigQuery ML, and use Cloud AutoML to create powerful models without coding.
Course Outline
Module 1: Google Cloud Fundamentals: Core Infrastructure
Introducing Google Cloud
Resources and Access in the Cloud
Virtual Machines and Networks in the Cloud
Storage in the Cloud
Containers in the Cloud
Applications in the Cloud
Developing and Deploying in the Cloud
Logging and Monitoring in the Cloud
Hands-On:
Cloud Storage: Qwik Start - Cloud Console
Compute Engine: Qwik Start - Windows
Cloud IAM: Qwik Start
Cloud Monitoring: Qwik Start
Module 2: Enterprise Database Migration
Migrating Enterprise Databases to the Cloud
Google Cloud Data Migration Solutions
Google Implementation Methodology
Migration Strategies
Networking for Secure Database Connectivity
Migrating SQL Server Databases to Google Cloud
Migrating Oracle Databases to Google Cloud
Testing and Monitoring SQL Server Databases in Google Cloud
Google Cloud Data Migration Services
Making the Business Case for Moving to Google Cloud
Hands-On:
Cloud SQL for MySQL: Qwik Start
Migrating to Cloud SQL from Amazon RDS for MySQL Using Database Migration Service
Migrating On-premises MySQL Using a Continuous Database Migration Service Job
Migrating MySQL User Data When Running Database Migration Service Jobs
Connect an App to a Cloud SQL for PostgreSQL Instance
Securing a Cloud SQL for PostgreSQL Instance
Configure Replication and Enable Point-in-Time-Recovery for Cloud SQL for PostgreSQL
Cloud Spanner - Database Fundamentals
Cloud Spanner - Loading Data and Performing Backups
Cloud Spanner - Defining Schemas and Understanding Query Plans
Deploy a Modern Web App connected to a Cloud Spanner Instance
Designing and Querying Bigtable Schemas
Creating and Populating a Bigtable Instance
Streaming Data to Bigtable
Monitoring and Managing Bigtable Health and Performance
This four-day instructor-led class provides participants with a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demonstrations, and practical labs, you will learn to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning solutions. The course covers various types of data, including structured, unstructured, and streaming data.
Key Features:
Session by Certified Instructor
Advanced hands-on labs
Official training content
Industry-recognized certification
Interactive sessions
Course Objectives:
Design and build data processing systems on Google Cloud.
Process batch and streaming data by implementing auto-scaling data pipelines using Dataflow.
Derive business insights from large datasets using BigQuery.
Leverage unstructured data with Spark and ML APIs on Dataproc.
Enable instant insights from streaming data.
Introduce ML APIs and BigQuery ML, and use Cloud AutoML to create powerful models without coding.
Course Outline
Module 1: Google Cloud Fundamentals: Core Infrastructure
Introducing Google Cloud
Resources and Access in the Cloud
Virtual Machines and Networks in the Cloud
Storage in the Cloud
Containers in the Cloud
Applications in the Cloud
Developing and Deploying in the Cloud
Logging and Monitoring in the Cloud
Hands-On:
Cloud Storage: Qwik Start - Cloud Console
Compute Engine: Qwik Start - Windows
Cloud IAM: Qwik Start
Cloud Monitoring: Qwik Start
Module 2: Enterprise Database Migration
Migrating Enterprise Databases to the Cloud
Google Cloud Data Migration Solutions
Google Implementation Methodology
Migration Strategies
Networking for Secure Database Connectivity
Migrating SQL Server Databases to Google Cloud
Migrating Oracle Databases to Google Cloud
Testing and Monitoring SQL Server Databases in Google Cloud
Google Cloud Data Migration Services
Making the Business Case for Moving to Google Cloud
Hands-On:
Cloud SQL for MySQL: Qwik Start
Migrating to Cloud SQL from Amazon RDS for MySQL Using Database Migration Service
Migrating On-premises MySQL Using a Continuous Database Migration Service Job
Migrating MySQL User Data When Running Database Migration Service Jobs
Connect an App to a Cloud SQL for PostgreSQL Instance
Securing a Cloud SQL for PostgreSQL Instance
Configure Replication and Enable Point-in-Time-Recovery for Cloud SQL for PostgreSQL
Cloud Spanner - Database Fundamentals
Cloud Spanner - Loading Data and Performing Backups
Cloud Spanner - Defining Schemas and Understanding Query Plans
Deploy a Modern Web App connected to a Cloud Spanner Instance
Designing and Querying Bigtable Schemas
Creating and Populating a Bigtable Instance
Streaming Data to Bigtable
Monitoring and Managing Bigtable Health and Performance
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Live Online Training (Duration : 32 Hours)
