Google Cloud Platform is a "cloud computing platform" used mainly for application development, data storage and analytics. The platform helps business owners and developers to minimize their expenses while enhancing efficiency and flexibility. In this course, Obaidy Academy will prepare you for the "Associate Cloud Engineer" ceritification exam from Google. After this course, you will be able to deploy cloud-native applications, monitor operations and create solutions using GCP and Python. The GCP Cloud Engineer role is in high demand on the market as an average salary for the position is over $120,000/year.
Google Cloud Platform - Syllabus
Monday through Friday - 4 hours per day
Introduction to Python
Day 1
- Variables, operators - Conditions, loops
Day 2
- Strings, lists, dictionaries, sets
Day 3
- Functions - Basics of object-oriented programming with Python
Day 4
- Basics of data analytics with Python
Day 5
- Basics of back-end programming with Python
Google Cloud Platform
Day 1
- Fundamentals of cloud technology - Introduction to GCP, overview of the platform, creating an account, working with the console
Day 2
- Overview of managed compute services and serverless computing
Day 3
- Overview of storage, databases, data processing, Machine Learning, Internet of Things, relevant APIs in GCP, wider Google ecosystem
- Working with Cloud Engine, creating and setting up a virtual machine instance, managing disks, snapshots, scopes, basic Linux operations, accessing Google Cloud services from the VM, scripting, preemptible VMs.
Day 7
- Scaling and automation, load balancing for VMs, managed instance groups, Cloud CDN, containers, Deployment Manager, YAML files, Terraform.
Day 8
- Working with App Engine, deployment, versioning, rollbacks, standard/flexible environments. - Introduction to Kubernetes Engine, clusters, pods, ReplicaSets, accessing to applications, monitoring.
Day 9
- Stateless applications in GKE, health checks, accessing external services, volumes and persistent storage, ConfigMaps and secrets, deployment patterns, autoscaling. - Helm, Ingress control, availability in clusters and workloads, security, DaemonSets, stateful applications, workloads, finite tasks, init containers.
Day 10
- Other serverless computing options in GCP, Cloud Run, Cloud Functions. - Scheduler, PubSub, Composer, Firebase for Back-end as a Service, creating an API, application security, managing application data, migration from local to GCP, using GCP APIs within applications.
Google Cloud Platform
Day 11
- Software development lifecycle and testing, Continuous Integration and Delivery, Site Reliability Engineering fundamentals, incident response, Cloud Security Scanner, Binary Authorization.
Day 12
- Overview of networking in GCP, protocols, using Compute Engine instances within networks, routing, virtual private clouds, virtual private networks, hybrid networking, configuring roles, accounts, firewall rules, subnets.
Day 13
- Controlling access to networks, load balancing, hybrid connectivity, network design and deployment, monitoring networks.
Day 14
- Working with BigQuery, schema design, writing, saving, and sharing queries, importing and exporting data, BigQuery ML, integration with Data Studio.
Day 15
- Other managed storage systems in GCP: Cloud Bigtable, Cloud Spanner, Cloud SQL, Cloud Storage, Cloud Datastore, Cloud Memorystore.
Google Cloud Platform
Day 16
- Data pipeline and processing design: Data cleansing, data acquisition/import/transformation, integrating with new data sources. - Tools for working with batch and streaming data: Cloud Dataflow and Dataproc, Apache Beam, Hadoop, Spark, and Kafka. - Provisioning resources, monitoring and adjusting pipelines.
Day 17
- Data lakes and warehouses in GCP, using Cloud Storage and BigQuery as infrastructure.
Day 18
- Introduction to Machine Learning, most popular ML algorithms, fundamental ML concepts such as training/validation/testing, supervised/unsupervised learning, optimization, bias-variance trade-off, overfitting, and evaluation metrics - Using pre-built ML services such as Vision API, AutoML, and Dialogflow. - Deployment of ML pipelines, Cloud Machine Learning Engine, Kubeflow.
Day 19
- Cloud related decisions in practical business context, cost optimization, planning cloud solutions, migration, backup and recovery. - Isolated and secure network design. - Integration with G-Suite, authorization, access, control, configuration, endpoints, Drive Enterprise. - Introduction to cutting edge technologies with Cloud technology.