Public cloud computing: A complete guide for data teams

How does a public cloud work

Public clouds pool large fleets of servers, storage, and networking, then virtualize and slice them into services you can request through a console, CLI, or API. Under the hood, orchestration layers schedule workloads, allocate storage, isolate tenants, and meter usage for billing.

Essential characteristics

• On-demand self service so teams provision compute or storage without filing tickets
  

• Broad network access so services are reachable from many devices and networks
  

• Resource pooling to serve multiple tenants with logical isolation
  

• Rapid elasticity to scale up or down programmatically
  

• Measured service to meter usage for chargeback or showback
  

These characteristics come directly from the NIST definition that standardized cloud terminology across the industry.

Service models

• Infrastructure as a service offers virtual machines, disks, and networks you manage
  

• Platform as a service provides managed runtimes and databases that abstract the OS
  

• Software as a service delivers complete applications you consume as a subscriber
  

Using the right model for each workload determines your operating burden and the security controls you must own. 

Common building blocks

• Compute types, from VMs to serverless functions and managed Kubernetes
  

• Storage types, including object, block, and file services, often with tiered pricing and durability guarantees such as Amazon S3’s “11 nines” of durability for objects
  

• Managed data services, including warehouses, lakehouses, streaming, and ML platforms
  
  

Types of public clouds

Public clouds can be viewed through a few useful lenses.

By service category

• IaaS for the most control over OS and network
  

• PaaS for faster delivery on managed platforms
  

• SaaS for complete business applications
  

The service category you choose determines security responsibility, patching scope, and expected operational effort.

By compute paradigm

• Virtual machines for general workloads and lift-and-shift migrations
  

• Containers and managed Kubernetes for portability and microservices at scale
  

• Serverless functions and event services for bursty, short-lived tasks
  

Kubernetes adoption is now mainstream, with industry surveys showing the vast majority of organizations using it in production or evaluation, which is why most enterprises standardize on EKS, AKS, or GKE for new services.

By tenancy and isolation

• Multi-tenant services deliver efficiency with logical isolation
  

• Single-tenant or dedicated options add isolation for compliance or performance
  
  

Real-world examples of public clouds

• Amazon Web Services supports global streaming at Netflix scale, with AWS used for compute, storage, and virtual studio workflows.
  

• Google Cloud powers Spotify’s analytics and content delivery, leveraging Google’s global network and developer-friendly managed services.
  

• Microsoft Azure underpins Adobe Experience Platform, including analytics and data services used to deliver real-time customer profiles.
  

These examples underscore why public cloud is attractive for internet-scale distribution and data-intensive analytics.

Advantages of public cloud computing

• Elasticity at global scale so you can right-size capacity minute by minute rather than over-provision for peak demand
  

• Faster time to value with managed databases, streaming, and AI services that minimize undifferentiated heavy lifting
  

• Built-in durability and availability such as S3’s design target of 99.999999999 percent object durability and multi-AZ replication defaults for resilience
  

• Modern engineering workflows using containers and managed Kubernetes, now widely adopted across organizations, which accelerates delivery and standardizes operations
  

• Financial alignment with pay-as-you-go and committed-use discounts that can match project lifecycles, supported by FinOps practices that drive cost visibility and waste reduction priorities across the industry
  

For data leaders, these advantages matter most when data can stay close to cloud compute and when governance is uniform. Cloudera’s hybrid data platform gives consistent data management, security, and governance across public clouds and data centers, so teams can place each workload where it fits best without fragmenting controls.

Disadvantages of public cloud computing

• Data egress and network costs can dominate TCO for data-heavy architectures. Each hyperscaler charges for outbound internet transfer and inter-region traffic, which means architecture choices on placement, caching, and data gravity directly affect the bill.
  

• Vendor lock-in risks increase as you adopt provider-specific services and proprietary APIs, raising switching costs later
  

• Operational blind spots occur without disciplined observability, budget ownership, and FinOps guardrails. Many organizations have shifted priorities to waste reduction and commitment management for precisely this reason.
  

• Compliance and data residency constraints can limit where you place datasets
  

• Repatriation signals are noisy. Some firms move certain workloads back for cost or latency, yet broad industry analysis finds the overall impact is often overstated, and most enterprises settle on hybrid rather than abandoning the public cloud.
  

Practical takeaway, not a scare tactic: model data movement early, tag costs by product and dataset, and design for portability where it matters.

Public cloud vs private cloud

Public and private clouds share core traits but diverge on who owns the stack, how resources are shared, and what tradeoffs you accept on elasticity, cost, and control.

• Ownership: Public cloud is provider operated; private cloud is enterprise operated or run for the exclusive use of one organization
  

• Tenancy: Public cloud is multi-tenant by default; private cloud is single-tenant
  

• Elasticity: Public cloud offers on-demand scale; private cloud is bounded by owned or contracted capacity
  

• Cost model: Public cloud is usage-based opex; private cloud combines capex with opex
  

• Control: Public cloud offers less hardware control; private cloud gives full stack control
  

• Typical use: Public cloud suits internet-scale and variable workloads; private cloud suits sensitive data, steady workloads, and specialized compliance
  

NIST’s definitions remain the neutral reference for aligning terminology and deployment choices.

Hybrid cloud vs public cloud

Hybrid means combining public and private cloud infrastructures with connectivity and orchestration that allow data and workloads to move or be shared. It accepts the reality that not every dataset or system should live in one place forever.

For data teams, hybrid works when governance is consistent and movement is purposeful. Cloudera’s hybrid data platform was built for that. It provides consistent security and governance through SDX across clouds and on-premises, while services like Cloudera Data Warehouse, Cloudera DataFlow, and Data Hub run natively in AWS, Azure, and Google Cloud. That lets you run streaming pipelines near cloud producers, warehouse workloads where analytics teams sit, and still maintain lineage and policies centrally.

Further reading inside Cloudera’s site: the hybrid data platform overview, the open data lakehouse approach powered by Apache Iceberg, and data lineage capabilities that show how data moves and transforms across systems.

Public cloud services that matter to data teams

Storage and lakehouse

Object storage is the backbone of analytics in the public cloud. Durability targets like S3’s “11 nines,” abundant capacity, and tiered pricing make it ideal for data lakes. A lakehouse pattern adds table formats and ACID semantics to treat object storage like a warehouse. Cloudera’s open data lakehouse approach uses Apache Iceberg for an open standard that supports multiple engines without copying data. 

Data warehousing and analytics

Managed warehouses deliver elastic, SQL-first analytics. Cloudera Data Warehouse runs as a cloud-native service with autoscaling virtual warehouses, integrated with streaming and data engineering to serve BI at petabyte scale. For teams standardizing on Cloudera, that means one governance fabric and consistent SQL with fewer data pipelines to babysit.

Streaming and integration

Modern data products ingest events continuously. Cloudera DataFlow brings Apache NiFi-powered pipelines to AWS, Azure, and Google Cloud, managed from a control plane with environment-aware deployments. That reduces bespoke glue code and speeds up data distribution to warehouses, lakes, and ML services.

Managed public cloud and public cloud management

You can outsource parts of cloud operations to managed public cloud services and platforms that standardize provisioning, governance, and lifecycle management.

• Cloudera Platform standardizes security, governance, and data services across AWS, Azure, and Google Cloud, with a single control plane for provisioning and policy. That is valuable when you have multiple business units and regions that must comply with the same controls.
  

• Cloudera Data Hub lets you spin up workload-specific clusters and manage them through a consistent console or CLI across clouds.
  

Public cloud management also includes cost stewardship. The FinOps Foundation’s recent surveys show organizations prioritizing reduction of waste and better commitment management. Bake cost attribution into architecture from day one, and review egress and inter-region traffic regularly, since those fees are where many forecasts go sideways. 

Public cloud security

Security in public cloud is a shared responsibility. The provider secures the infrastructure and managed service layers, and you secure your data, identities, configurations, and code. Each hyperscaler documents this model with service-specific nuances, including reliability responsibilities. Treat these documents as control-mapping inputs for your internal policies. 

Key practices for data-centric security in public cloud:

• Identity and access: Enforce least privilege with centralized IAM, short-lived credentials, and workload identity
  

• Encryption: Use default encryption at rest and enforce in-transit TLS, with managed keys or customer-managed keys as appropriate
  

• Network segmentation: Use VPCs, private endpoints, and micro-segmentation patterns to reduce blast radius
  

• Configuration baselines: Align to frameworks like the Cloud Security Alliance Cloud Controls Matrix and your chosen benchmarks, then audit continuously
  

• Observability: Route logs and metrics into managed monitoring stacks and SIEM, set budgets and anomaly alerts, and test incident response regularly
  

Where data governance gets hard, Cloudera’s Shared Data Experience (SDX) provides unified security and governance services that apply consistently across public cloud and on-premises deployments. Pair SDX with data lineage to track provenance and impact for regulatory reporting, quality troubleshooting, and developer self-service. 

Public cloud security checklist for data workloads

• Define shared-responsibility boundaries for each service, and capture them in control mappings
  

• Centralize IAM and enforce least privilege with short-lived credentials
  

• Encrypt data at rest and in transit, with HSM or KMS when required
  

• Use private networking, service endpoints, and layered segmentation
  

• Automate config baselines, compliance checks, and drift remediation
  

• Implement lineage and cataloging, so producers and consumers see provenance
  

• Integrate monitoring, alerting, and incident response, with runbooks tested against real events

Provider documentation on shared responsibility and industry frameworks like the CSA Cloud Controls Matrix are baseline references for this checklist. Cloudera SDX plus data lineage covers the governance and evidence side across hybrid estates.

Learn more about how Cloudera on cloud improves the elasticity and scalability of data analytics .