
Define the cloud as a network of remote servers accessed over the internet that store, manage, and process data, extending computing resources beyond local equipment.
Discover how cloud roles from end users to builders—BI analysts, data scientists, analytics engineers, and data engineers—use and optimize cloud platforms, pipelines, data lakes, APIs, and machine learning.
Explore how cloud data centers scale computing resources globally, provide internet access, let users pay to access a fraction of resources, and offer redundancy and security measures for data.
Explain the three cloud service levels—IaaS, PaaS, and SaaS—from base infrastructure to preconfigured computing environments, with examples from Amazon Web Services, Microsoft Azure, Google Cloud, Snowflake, and Databricks.
Explore the three cloud infrastructure types—private, public, and hybrid—and compare performance, security, compliance, and cost, with guidance for data professionals who use the same analytics tools regardless of deployment.
Compare the big three cloud providers—AWS, Azure, and Google Cloud Platform—to map storage, compute, data warehouses, data lakes, and data visualization tools.
Assess vendor lock-in risks across cloud providers and implement mitigation strategies such as data portability, multi-cloud, migration planning, and regular audits to safeguard cost, innovation, and compliance.
Explore how cloud data stacks enable secure data access and how architecture varies by business needs. Data engineers tailor stacks, and teams may use multiple cloud back ends.
This course is a high-level introduction to the world of cloud computing.
The cloud ecosystem has grown exponentially in recent years, now storing more than half of the world’s corporate data. Yet most people who interact with cloud services are unaware of what’s going on behind the scenes.
In this course, we’ll set the stage by defining what cloud computing means and why companies rely on it, draw comparisons against traditional on-premise computing, and explore how different types of data professionals interact with cloud technology.
From there we’ll dig into the core components of cloud architecture, compare different types of cloud services and infrastructure, and review important topics like security, virtualization, cost control, and more.
Next, we’ll explore the modern cloud landscape, and compare services offered by key players like AWS, Microsoft Azure, and Google Cloud. We’ll introduce public and private cloud providers, data platforms and software products, and discuss how to mitigate the risk of vendor lock-in.
Last but not least, we’ll walk through unique demos and real-world use cases to showcase how you can begin to leverage these services as a data professional, including workflows built on AWS, Azure, GCP and Snowflake.
COURSE OUTLINE:
Cloud 101
Introduce the basics of cloud computing, including what it is, why companies use it, and the way different data roles interact with it
Cloud Architecture
Understand the core components of cloud computing and cloud infrastructure, as well as the types of cloud services and architecture
The Cloud Landscape
Review some of the major players in the cloud computing industry for data analytics, and compare their similarities and differences
Cloud Data Stacks
Demo simple data analytics pipelines using combinations of cloud products and services (or “stacks”), including AWS, MySQL Workbench, GCP, Looker, Azure, Snowflake, and more
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Ready to dive in? Join today and get immediate, LIFETIME access to the following:
2 hours of high-quality video
4 real-world cloud demos & case studies
3 course quizzes
Cloud Basics for Data Professionals ebook (50+ pages)
Expert support and Q&A forum
30-day Udemy satisfaction guarantee
Whether you’re an analyst or data scientist interested in cloud computing or a business leader looking to learn about the cloud landscape, this course is for you.
Happy learning!
-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)
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