Cloud Basics for Data Professionals

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.

Drive the cloud's rise, delivering cheaper computing and vast resources for ai and software as a service, while 60% of corporate data already sits in the cloud.

Companies turn to the cloud for scalable, on-demand computing as they outgrow local servers; the lecture compares on premise versus cloud options and introduces a hybrid approach.

Compare on premise and cloud computing, highlighting scalability and cost considerations. Assess security and reliability across global data centers and cloud providers like AWS.

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.

Define the cloud as a network of internet-based computing resources that you access remotely, and outline how data analysts, scientists, and engineers interact with and build this infrastructure.

Explore how the cloud scales computing power beyond a single pc or on premise server by examining cpu, storage, ram, network interface card, and software.

Explore on premise servers that provide a pool of computing resources accessible from personal computers, store databases, boost power and storage, support multiple users, and require space and skilled maintenance.

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.

Assess cloud security by recognizing data breach risks and outlining measures like multi-factor authentication, encryption, firewalls, continuous monitoring, backups, and disaster recovery for cloud storage.

Explains cloud storage options from cheap hard drives to premium ssd storage, covers flat files, databases, data warehouses, data lakes and data lake house, plus oltp and olap.

Cloud compute options include CPUs, GPUs, and TPUs, with power and cost tradeoffs; CPUs cover most data analysis, while GPUs and TPUs enable high-performance machine learning, followed by virtualization.

Explore virtualization and how partitioning and pooling physical hardware creates virtual resources, enabling scalable cloud computing and pay-for-what-you-need efficiency for data professionals.

Master cloud cost control by shutting down unused resources, optimizing query efficiency, and using CPU over GPU or TPU when appropriate; consult IT to justify resource choices.

Explore a Microsoft Azure cloud portal by creating a virtual machine or SQL database with Azure Data Factory, and see how cost management and serverless vs provisioned compute shape spend.

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.

Explore cloud service models—IaaS, PaaS, SaaS—and how data engineers and analysts use them, with private public hybrid clouds, pipelines, SQL, and tools like Power BI, Tableau Cloud, Google Sheets.

Explore the public cloud trio of AWS, Azure, and Google Cloud Platform and their roles in data analytics, hybrid cloud, and vendor lock-in avoidance.

Compare the big three cloud providers—AWS, Azure, and Google Cloud Platform—to map storage, compute, data warehouses, data lakes, and data visualization tools.

Explore leading private cloud providers, including VMware, Dell EMC, IBM, and Red Hat, highlighting virtualization, private and hybrid cloud, security, and platform as a service for data analytics.

Discover platform as a service cloud data platforms such as Snowflake, Databricks, and Salesforce Data Cloud, and how they leverage infrastructure as a service to deliver data warehousing and analytics.

Explore cloud software products, from spreadsheet and data viz tools to data integration and ETL solutions, and compare local versus cloud versions, collaboration benefits, and the rise of subscription models.

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.

Highlight cloud landscape, with AWS, Azure, and GCP holding 70% of the public market, and note private clouds, IaaS/PaaS, and tools like Snowflake, Databricks, and Power BI for data stacks.

Explore how data professionals choose a stack for cloud architectures, balancing end-user outputs, data sources, data volume, and budget.

Demonstrates simple cloud architectures on AWS, from flat files to S3 and RDS, showing analysts how compute and storage power cloud databases and how to query with MySQL Workbench.

Explore an azure workflow connecting a sql database to power bi via a windows virtual desktop, with data loaded from azure blob storage.

Explore a Google Cloud Platform workflow using Looker Studio and BigQuery for BI. Upload CSV data to Google Cloud Storage, query in BigQuery, and visualize in Looker Studio.

Explore Snowflake as a platform-as-a-service data warehouse for multi-cloud workloads, reading data from Google Cloud Storage with Azure back end, using SQL and Python worksheets, built-in dashboards, and BI connectors.

Explore real-world cloud case studies on AWS and Azure, tracing end-to-end data pipelines from diverse sources through ingestion, processing, and visualization to monitoring and security controls.

Explore cloud analytics workflows across GCP and Snowflake, integrating diverse data sources into BigQuery and Snowflake. Use Python, Docker, Looker, and Jupyter for preprocessing, analysis, and visualization.

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.

The cloud is hardware and software accessed via the internet, enabling scalable and cost-effective computing. Recognize IaaS, PaaS, SaaS, with AWS, Azure, Google Cloud Platform, Snowflake, and Databricks.