What you'll learn:
- Understand major components of GCP, why and when to use its Products
- Connect into GCP VMs using SSH
- Build a dataset using BigQuery
- Repeat command from BigQuery in Datalab and Plot a Graph
- Dashboarding in Datastudio
- Machine Learning and AI Fundamentals
- Hadoop
- Application of GCP Products in Real World Applications
[UPDATED CONTENT 2019 Exam]
Storage Solutions
OLAP vs OLTP databases
Consistency concepts.
Transactional consistency for various data storage solutions.
Cloud Storage
Gsutil command line interface.
Datastore
Datastore indexing - what is it, how to update, upload.
BigQuery
Update of BigQuery practicals including authorised views in the new BQ UI.
Concepts of temporary tables.
Types of schemas BQ accepts.
BigTable
BigTable fit for purpose of time-series data.
Cbt command line interface for BigTable.
BigTable consistency concepts and highly available configuration.
Dataflow
Deploying dataflow jobs and what’s running in the background.
Dataflow job monitoring through console -> Cloud Dataflow Monitoring Interface and also gcloud dataflow commands.
Updating a dataflow streaming job on the fly.
Logging of Cloud Dataflow jobs.
Cloud Dataflow Practical - Running job locally and using Dataflow Service
Hadoop & Dataproc
Apache Spark jobs
Stackdriver
Export logs to BigQuery for further analysis, why and how.
Machine Learning Solutions - New Section
Introduction of new GCP ML products and open source products such as Cloud Machine Learning Engine, BigQuery ML, Kubeflow & Spark ML
Cloud AutoML -> AutoML Vision, AutoML Vision Edge
Dialogflow - GCP’s Chatbot builder
Concept of edge computing and distributed computing
Google cloud’s TPU (Tensor Processing Unit)
Common terms in Machine Learning terminology such as features, labels, models, linear and logistic regression, classification, clustering/networks and supervised/unsupervised learning.
Migration into GCP - New Section
How to migrate data into GCP - Transfer Appliance & Storage Transfer Service
Dataprep - New Section
What is Dataprep?
Dataprep practical section, runs Dataflow job in background - nice interface for non-coders
Security on GCP - New Section
Cloud security best practices
Securely interacting with Cloud Storage
Penetration testing
Bastion/Jumphost
Encryption
Data loss prevention api
Live migration
Cloud Composer - New Section
What is cloud composer?
Hi I’m Sam, a big data engineer, full stack web developer and machine learning/AI Enthusiast teaching you GCP in the most efficient and down to earth approach. I will teach you the core components of GCP required to pass the data engineers exam using a real world applications approach. All the practicals in this course show you techniques used by big data engineers on the GCP.
Course is streamlined to aim to get you to pass the GCP Data Engineers Certification. Therefore, it is the most time efficient course to learn about GCP if you want to have a good understanding of GCP’s products and have the intention of becoming a certified data engineer in the future. The course is streamlined to under 5 hours! Learn all about GCP over a weekend or in a day !
Infrastructure solutions will be presented for various use cases as you learn the most when solving real problems! Theory and Practicals will be placed to aim to pass the Data Engineers Exam with the shortest amount of time. In the exam most questions will be targeted on the why and not the how. For example you will be very hard pressed to find a question that asks you to choose the correct code snippet out of the 3 code snippets etc.
Student Feedback:
Hi Samuel. Hope this finds you well. I passed the GCP data engineering exam last week and just want to thank you for your Udemy course that summarises the exam materials so well! Have a good week ahead!
The course is helpful for my preparation of Google Data Engineering Certification Exam. It also gives a good and brief overview of GCP products that is lacking in other courses. The knowledge gained from this course can be applied to using GCP in data scientist and data engineering work.
I had tried coursera courses from google. It's too longer and has lots of marketing pitches. I like your approach. You should create another course like this for AWS or GCP architect.
Course is split up into sections as below:
Introduction - Explore questions, Why Cloud, Why GCP, main differentiators of GCP/main selling points, setup your free GCP account
Compute Engine - Overview of compute engine and pricing innovations, zones & regions, various machine types and practical to spin up VMs and access them via SSH, Mac and Windows supported
Storage Solutions - Overview of GCP’s data storage solutions including Cloud Storage, Cloud Datastore, Cloud Spanner, Cloud SQL, BigQuery & BigTable. We will compare these storage solutions with each other and explain use cases where one storage solution will excel over another.
IAM & Billing - Different member types, roles and permissions, resource hierarchy and billing process
BigQuery - BigQuery Pricing structure, tips for reducing processing cost, Partitioned & Wildcard tables, Authorised views, Practicals in BigQuery using standard SQL
Cloud Datalab - How to use Cloud Datalab in a practical with a live feed from BigQuery to explore the dataset.
Cloud Pub/Sub - Pub/Sub concepts and its components especially decoupling and the uses of Pub/Sub
Hadoop & Dataproc - Overview of hadoop and major components which will be tested in the exam
Cloud Dataflow - What is dataflow, the dataflow model, how and why its used with relation to other GCP Products
Stackdriver - Stackdriver functions such as debugging, error reporting, monitoring, alerting, tracing and logging.
Tensorflow & AI - Brief overview of machine learning and neural networks, play with neural networks with a playground and understand GCP’s AI products and APIs
Case Study - Finally, Put your new learnt GCP knowledge to use in a real world application business case. Similar case studies will be present in the exams.
