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Using Helm with Kubernetes

  • By Gcore
  • April 11, 2023
  • 5 min read
Using Helm with Kubernetes

Kubernetes is a powerful orchestration system, however, it can be really hard to configure its deployment process. Specific apps can help you manage multiple independent resources like pods, services, deployments, and replica sets. Yet, each must be described in the YAML manifest file.

It’s not a problem for a single trivial app, but during production, it’s best to simplify this process: search, use, and share already implemented configurations, deploy these configurations, create configuration templates, and deploy them without effort. In other words, we need an extended version of a package manager like APT for Ubuntu or PIP for Python to work with the Kubernetes cluster. Luckily, we have Helm as a package manager.

What is Helm?

Helm is an open-source package manager for Kubernetes that allows developers and operators to package, configure, and deploy applications and services onto Kubernetes clusters easily. It was inspired by Homebrew for macOS and now is a part of the Cloud Native Computing Foundation.

In this article, we will explore Helm 3.x which is the newest version at the time of writing this article.

Searches on Helm Hub for PostgreSQL from dozens of different repositories

Helm can install software and dependencies, upgrade software, configure software deployments, fetch packages from repositories, alongside managing repositories.

Some key features of Helm include:

  • Role-based access controls (RBAC)
  • Golang templates which allows you to work with configuration as text
  • Lua scripts to process configuration as an object
  • Deployment versions control system

Templates allow you to configure your deployments by changing few variable values without changing the template directly. Helm packages are called charts, and they consist of a few YAML configuration files and templates that are rendered into Kubernetes manifest files.

The basic package (chart) structure:

  • chart.yaml – a YAML file containing information about the chart
  • LICENSE (optional) – a plain text file containing the license for the chart
  • README.md (optional) – a human-readable README file
  • values.yaml – the default configuration values for this chart
  • values.schema.json (optional) – a JSON Schema for imposing a structure on the values.yaml file
  • charts/ – defines chart dependencies (recommended to use the dependencies section in chart.yaml)
  • crds/ – Custom Resource Definitions
  • templates/ – directory of templates that when combined with values, will generate valid Kubernetes manifest files

Templates give you a wide range of capabilities. You can use variables from context, apply different functions (such as ‘quote’, sha256sum), use cycles and conditional cases, and import other files (also other templates or partials).

What are Helm’s abilities?

  1. As you operate Helm though a Command Line Interface (CLI), the helm search command allows you to search for a package by keywords from the repositories.
  2. You can inspect chart.yaml, values.yaml, and README.md for a certain package. along with creating your own chart with the helm create <chart-name> command. This command will generate a folder with a specified name in which you can find the mentioned structure.
  3. Helm can install both folder or .tgz archives. To create a .tgz from your package folder, use the helm package <path to folder> command. This will create a <package_name> package in your working directory, using the name and version from the metadata defined in the chart.yaml file.
  4. Helm has built-in support for installing packages from an HTTP server. Helm reads a repository index hosted on the server, which describes what chart packages are available and where they are located. This is how the default stable repository works.
  5. You can also create a repository from your machine with helm serve. This eventually lets you create your own corporate repository or contribute to the official stable one.
  6. You can also call the helm dependencies update <package name> command which verifies that the required charts, as expressed in chart.yaml, are present in charts/ and are in an acceptable version. It will additionally pull down the latest charts that satisfy the dependencies, and clean up the old dependencies.
  7. Apart from Chart and Repository another significant concept you should know is Release which is an instance of a chart running in a Kubernetes cluster. One chart can often be installed many times into the same cluster. And each time it is installed, a new Release is created. So, you can have multiple PostgreSQL in the same cluster, in which each Release will have its own release name. You can think of this like ‘multiple Docker containers from one image’.

How does it work?

Source: developer.ibm.com

Helm client is used for installing, updating and creating charts, as well as compiling and sending them to a Kubernetes API in an acceptable form. The previous version had a client-server architecture, using a program run on a cluster with Kubernetes, called Tiller. This software was responsible for deployment’s lifetime. But this approach led to some security issues which is one of the reasons why all functions are now handled by the client.

Installing Helm 3 is noticeably easier than the previous version since only the client needs to be installed. It is available for Windows, macOS, and Linux. You can install the program from binary releases, Homebrew, or through a configured installation script.

Let’s try an example

1. Let’s start with installing Helm.

bash master $ curl https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current                                 Dload  Upload   Total   Spent    Left  Speed100  6794  100  6794    0     0  25961      0 --:--:-- --:--:-- --:--:-- 25931Error: could not find tillerHelm v3.1.2 is available. Changing from version .Downloading https://get.helm.sh/helm-v3.1.2-linux-amd64.tar.gzPreparing to install helm into /usr/local/binhelm installed into /usr/local/bin/helm

2. Check if everything is installed properly.

master $ helm version --shortV3.1.2+gd878d4d

3. By default, Helm doesn’t have a connection to any of the repositories. Let’s add connection to the most common stable one. (You can check all the available repositories with helm repo list).

master $ helm repo add stable https://kubernetes-charts.storage.googleapis.com/"stable" has been added to your repositories

4. After adding the repository, we should let Helm get updated. The current local state of Helm is kept in your environment in the home location.

master $ helm repo updateHang tight while we grab the latest from your chart repositories......Successfully got an update from the "stable" chart repositoryUpdate Complete. ⎈ Happy Helming!⎈

The Helm command defaults to discovering the host already set in ~/.kube/config. There is a way to change or override the host, but that’s beyond the scope of this scenario.

master $ helm envHELM_BIN="helm"HELM_DEBUG="false"HELM_KUBECONTEXT=""HELM_NAMESPACE="default"HELM_PLUGINS="/root/.local/share/helm/plugins"HELM_REGISTRY_CONFIG="/root/.config/helm/registry.json"HELM_REPOSITORY_CACHE="/root/.cache/helm/repository"HELM_REPOSITORY_CONFIG="/root/.config/helm/repositories.yaml"

5. Let’s search for a WordPress in the Helm Hub

master $ helm search hub wordpressURL                                                     CHART VERSION   APP VERSION     DESCRIPTION https://hub.helm.sh/charts/presslabs/wordpress-...      v0.8.4          v0.8.4          Presslabs WordPress Operator Helm Charthttps://hub.helm.sh/charts/presslabs/wordpress-...      v0.8.3          v0.8.3          A Helm chart for deploying a WordPress site on ...https://hub.helm.sh/charts/bitnami/wordpress            9.0.3           5.3.2           Web publishing platform for building blogs and ...

And also search in our repositories (we have only stable for now).

master $ helm search repo wordpressNAME                    CHART VERSION   APP VERSION     DESCRIPTIONstable/wordpress        9.0.2           5.3.2           DEPRECATED Web publishing platform for building...

6. As mentioned earlier, you can inspect a Chart. For example, let’s take info from chart.yaml for the WordPress chart. 
You can also check helm show readme stable/wordpress and helm show values stable/wordpress.

master $ helm show chart stable/wordpressapiVersion: v1appVersion: 5.3.2dependencies:- condition: mariadb.enabled  name: mariadb  repository: https://kubernetes-charts.storage.googleapis.com/  tags:  - wordpress-database  version: 7.x.xdeprecated: truedescription: DEPRECATED Web publishing platform for building blogs and websites.home: http://www.wordpress.com/icon: https://bitnami.com/assets/stacks/wordpress/img/wordpress-stack-220x234.pngkeywords:- wordpress- cms- blog- http- web- application- phpname: wordpresssources:- https://github.com/bitnami/bitnami-docker-wordpressversion: 9.0.2

7. Let’s create a namespace for WordPress and install a test chart.

master $ kubectl create namespace wordpressnamespace/wordpress created
master $ helm install test-wordpress stable/wordpress --namespace wordpress

The output of this command appears messy just because it’s so big.

You can also set variables, such as:

helm install test-wordpress \  --set wordpressUsername=admin \  --set wordpressPassword=password \  --set mariadb.mariadbRootPassword=secretpassword \    stable/wordpress

8. For now, let’s ensure that everything is deployed correctly:

As you can see, everything has been deployed properly.

Conclusion

Helm is a popular open-source package manager that offers users a more flexible way to manage Kubernetes cluster. You can either create your own, or use public packages from your own or external repositories. Each package is quite flexible and, in most cases, all you need is define the right constants from which the template will be compiled to suit your needs. To create your own chart, you can use the power of Go templates and/or Lua scripts. Each update will create a history unit to which you can rollback anytime you want. With Helm, you have all the power of Kubernetes. And, in the end, Helm allows you to work with role-based access, so you can manage your cluster in a team.

This brings us to the end of this brief article explaining the basics and features of Helm. We hope you enjoyed it and were able to make use of it.

Discover more with Gcore Managed Kubernetes

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Players can access demanding games on smartphones, tablets, or low-powered computers.What are the limitations of cloud GPUs?The limitations of cloud GPUs refer to the constraints and drawbacks organizations face when using remotely hosted graphics processing units accessed over the Internet. They are listed below.Network latency: Cloud GPUs depend on internet connectivity, which introduces delays between your application and the GPU. This latency can slow down real-time applications like gaming or interactive simulations that need immediate responses.Limited control: You can't modify hardware configurations or install custom drivers on cloud GPUs since they're managed by the provider. This restriction limits your ability to improve performance for specific workloads or use specialized software.Data transfer costs: Moving large datasets to and from cloud GPUs can be expensive and time-consuming. Organizations working with terabytes of data often face significant bandwidth charges and upload delays.Performance variability: Shared cloud infrastructure means your GPU performance can fluctuate based on other users' workloads. You might experience slower processing during peak usage times when resources are in high demand.Ongoing subscription costs: Cloud GPU pricing accumulates over time, making long-term projects potentially more expensive than owning hardware. Extended usage can cost more than purchasing dedicated GPUs outright.Security concerns: Your data and computations run on third-party infrastructure, which may not meet strict compliance requirements. Industries handling sensitive information often can't use cloud GPUs due to regulatory restrictions.Internet dependency: Cloud GPUs become completely inaccessible during internet outages or connectivity issues. This dependency can halt critical operations that would otherwise continue with local hardware.How to get started with cloud GPUsYou get started with cloud GPUs by choosing a provider, setting up an account, selecting the right GPU instance for your workload, and configuring your development environment.Choose a cloud GPU provider: Consider your options based on geographic needs, budget, and required GPU models. Look for providers offering the latest NVIDIA GPUs (H100s, A100s, L40S) with global infrastructure for low-latency access. Consider factors like available GPU types, pricing models, and support quality.Create an account and configure billing with your chosen provider: Many platforms offer trial credits or pay-as-you-go options that let you test GPU performance before committing to reserved instances. Set up usage alerts to monitor spending during initial testing.Select the appropriate GPU instance type for your workload: High-memory GPUs like H100s or A100s excel at large-scale AI training, while L40S instances provide cost-effective options for inference and rendering. Match your GPU selection to your specific memory, compute, and budget requirements.Launch your GPU instance: This can be done through the web console, API, or command-line interface. Choose from pre-configured images with popular ML frameworks (PyTorch, TensorFlow, CUDA) already installed, or start with a clean OS image for custom configurations. Deployment typically takes under 60 seconds with modern cloud platforms.Configure your development environment: Connect via SSH or remote desktop, install required packages, and set up your workflow. Use integrated cloud storage for efficient data transfer rather than uploading large datasets through your local connection. Configure persistent storage to preserve your work between sessions.Test with a sample workload: Verify performance and compatibility before scaling up. Run benchmark tests relevant to your use case, monitor resource utilization, and validate that your application performs as expected. Start with shorter rental periods while optimizing your setup.Optimize for production: Implement auto-scaling policies, set up monitoring dashboards, and establish backup procedures. Configure security groups and access controls to protect your instances and data.Start with shorter rental periods and smaller instances while you learn the platform's interface and improve your workflows for cloud environments.Gcore cloud GPU solutionsWhen choosing between cloud and physical GPU solutions for your AI workloads, the decision often comes down to balancing performance requirements with operational flexibility. Gcore cloud GPU infrastructure addresses this challenge by providing dedicated GPU instances with near-native performance while maintaining the flexibility advantages of cloud computing. This is all accessible through our global network of 210+ points of presence with 30ms average latency.Our cloud GPU solutions eliminate the weeks-long procurement cycles typical of physical hardware, allowing you to provision high-performance GPU instances within minutes and scale from single instances to large clusters as your training demands evolve. This approach typically reduces infrastructure costs by 30-40% compared to maintaining fixed on-premise capacity, while our enterprise-grade infrastructure ensures 99.9% uptime for mission-critical AI workloads.Discover how Gcore cloud GPU solutions can accelerate your AI projects while reducing operational overhead.Explore Gcore GPU CloudFrequently asked questionsHow does cloud GPU performance compare to local GPUs?Cloud GPU performance typically delivers 80-95% of local GPU performance while offering instant flexibility and lower upfront costs. Local GPUs provide maximum performance and predictable latency but lack the flexibility to scale resources on demand.What are the security considerations for cloud GPUs?Yes, cloud GPUs have several critical security considerations, including data encryption, access controls, and compliance requirements. Key concerns include securing data in transit and at rest, managing multi-tenant isolation in shared GPU environments, and meeting regulatory standards like GDPR or HIPAA for sensitive workloads.What programming frameworks work with cloud GPUs?Yes, all major programming frameworks work with cloud GPUs including TensorFlow, PyTorch, JAX, CUDA-based applications, and other parallel computing libraries. Cloud GPU providers typically offer pre-configured environments with GPU drivers, CUDA toolkits, and popular ML frameworks already installed.How much do cloud GPUs cost compared to buying hardware?Cloud GPUs cost $0.50-$3.00 per hour while comparable physical GPUs require $5,000-$40,000 upfront plus ongoing maintenance costs. For occasional use, cloud GPUs are cheaper, but heavy continuous workloads favor owned hardware after 6-12 months of usage.

What is hybrid cloud? Benefits, use cases, and implementation

A hybrid cloud is a computing environment that combines private clouds, public clouds, and on-premises infrastructure, enabling data and applications to be shared and managed across these environments.The architecture of hybrid cloud systems includes several key components that work together to create a unified computing environment. Private clouds serve as dedicated environments for sensitive applications requiring control and compliance, while public clouds from major providers offer flexibility and cost-effectiveness for less sensitive workloads.Orchestration software manages workload distribution between these environments based on predefined rules or real-time demand.Understanding the distinction between hybrid cloud and multi-cloud approaches is important for organizations planning their cloud strategy. While hybrid cloud connects private and public environments into a single, integrated system, multi-cloud involves using multiple separate cloud services without the same level of integration. This difference affects how data flows between systems and how resources are managed across platforms.The benefits of hybrid cloud extend beyond simple cost savings to include improved flexibility, enhanced security, and better compliance capabilities.Organizations can keep sensitive data in private environments while using public cloud resources for variable workloads, creating an optimized balance of control and flexibility. This approach allows businesses to meet specific regulatory requirements while still accessing the latest cloud technologies.What is hybrid cloud?Hybrid cloud is a computing environment that combines private clouds, public clouds, and on-premises infrastructure, allowing data and applications to be shared and managed across these different environments. This approach gives organizations the flexibility to keep sensitive data on private infrastructure while using public cloud resources for flexible workloads that need to handle varying demand.How does hybrid cloud architecture work?Hybrid cloud architecture works by connecting private clouds, public clouds, and on-premises infrastructure through orchestration software and secure networking to create a unified computing environment. This integrated approach allows organizations to move workloads and data seamlessly between different environments based on specific requirements like security, performance, or cost.The architecture operates through four core components working together. Private clouds handle sensitive data and applications that require strict control and compliance, typically running on dedicated on-premises infrastructure or through private hosting providers.Public clouds from major providers manage flexible workloads and applications that need rapid resource expansion, offering cost-effective computing power for variable demands. Orchestration software acts as the central management layer, automatically distributing workloads between environments based on predefined rules, real-time demand, or performance requirements. Secure networking connections, including VPNs and dedicated links, ensure data integrity and cooperation between all environments.The system enables flexible resource allocation by monitoring application performance and automatically growing resources up or down across environments.When a private cloud reaches capacity, the orchestration layer can burst workloads to public cloud resources while maintaining security protocols. This flexibility allows organizations to keep critical data on-premises while taking advantage of public cloud flexibility for less sensitive operations, creating the best balance of control, security, and cost-effectiveness.What's the difference between hybrid cloud and multi-cloud?Hybrid cloud differs from multi-cloud primarily in architecture integration, vendor strategy, and operational management approach. Hybrid cloud combines private and public cloud environments with on-premises infrastructure into a unified, interoperable system, while multi-cloud uses multiple independent cloud providers without requiring integration between them.The architectural approach mainly differs in its design philosophy. Hybrid cloud creates a single, cohesive environment where workloads can move seamlessly between private clouds, public clouds, and on-premises systems through orchestration software and secure networking.Multi-cloud maintains separate, distinct cloud environments from different providers, with each serving specific functions independently without cross-platform integration or data sharing.Vendor strategy and risk management differ between these approaches. Hybrid cloud typically involves fewer providers but focuses on a deep integration between private infrastructure and selected public cloud services to balance security, compliance, and flexibility needs. Multi-cloud deliberately spreads workloads across multiple cloud vendors to avoid vendor lock-in, reduce dependency risks, and access best-of-breed services from different providers.Operational complexity and cost structures vary considerably.Hybrid cloud requires advanced orchestration tools and networking to manage unified operations across integrated environments, often resulting in higher initial setup costs but streamlined ongoing management. Multi-cloud involves managing multiple separate vendor relationships, billing systems, and operational processes, which can increase administrative overhead but provides greater flexibility in cost optimization and service selection. According to Precedence Research (2023), the global hybrid cloud market reached $125 billion, reflecting strong enterprise adoption of integrated cloud strategies.What are the key benefits of hybrid cloud?The key benefits of hybrid cloud refer to the advantages organizations gain from combining private clouds, public clouds, and on-premises infrastructure in a single computing environment. The key benefits of hybrid cloud are listed below.Cost optimization: Organizations can run routine workloads on cost-effective private infrastructure while using public cloud resources only when needed. This approach reduces overall IT spending by avoiding over-provisioning of expensive on-premises hardware.Enhanced security and compliance: Sensitive data stays within private cloud environments that meet strict regulatory requirements, while less critical applications can use public cloud services. This separation helps organizations maintain compliance with industry standards like HIPAA or PCI-DSS.Improved flexibility: Companies can handle traffic spikes by automatically shifting workloads from private to public cloud resources during peak demand. This flexibility prevents performance issues without requiring permanent infrastructure investments.Business continuity: Hybrid cloud provides multiple backup options across different environments, reducing the risk of complete system failures. If one environment experiences issues, workloads can continue running on alternative infrastructure.Faster new idea: Development teams can quickly access advanced public cloud services like machine learning tools while keeping production data secure in private environments. This setup accelerates time-to-market for new applications and features.Workload optimization: Different applications can run in their most suitable environments based on performance, security, and cost requirements. Database-heavy applications might perform better on-premises, while web applications benefit from public cloud flexibility.Reduced vendor lock-in: Organizations maintain flexibility by avoiding dependence on a single cloud provider or infrastructure type. This independence provides negotiating power and reduces the risk of service disruptions from any single vendor.What are common hybrid cloud use cases?Common hybrid cloud use cases refer to practical applications in which organizations combine private clouds, public clouds, and on-premises infrastructure to meet specific business needs. The common hybrid cloud use cases are listed below.Disaster recovery and backup: Organizations store critical data backups in public cloud while maintaining primary operations on private infrastructure. This approach provides cost-effective off-site protection without requiring duplicate physical facilities.Cloud bursting for peak demand: Companies handle normal workloads on private clouds but automatically scale to public cloud during traffic spikes. E-commerce sites use this method during holiday sales to manage sudden increases in customer activity.Data sovereignty and compliance: Businesses keep sensitive data on-premises to meet regulatory requirements while using public cloud for non-sensitive applications. Financial institutions often store customer records privately while running analytics workloads in public environments.Development and testing environments: Teams use public cloud resources for development and testing to reduce costs, then use production applications on private infrastructure. This separation allows experimentation without affecting critical business operations.Application modernization: Organizations gradually migrate legacy applications by keeping core systems on-premises while moving supporting services to public cloud. This phased approach reduces risk while enabling access to modern cloud services.Edge computing integration: Companies process data locally at edge locations while connecting to centralized cloud resources for analysis and storage. Manufacturing facilities use this setup to monitor equipment in real-time while storing historical data in the cloud.Hybrid analytics and AI: Businesses combine on-premises data with cloud-based machine learning services to gain insights while maintaining data control. Healthcare providers analyze patient data locally while using cloud AI tools for diagnostic assistance.What are the challenges of hybrid cloud implementation?Challenges of hybrid cloud use refer to the technical, operational, and planned obstacles organizations face when combining private clouds, public clouds, and on-premises infrastructure into a unified computing environment. The challenges of hybrid cloud use are listed below.Complex integration requirements: Connecting different cloud environments with existing on-premises systems requires careful planning and technical work. Organizations must ensure that applications, data, and workflows can move smoothly between private and public clouds while maintaining performance standards.Security and compliance concerns: Managing security across multiple environments creates additional risks and complexity. Organizations must maintain consistent security policies, data protection standards, and regulatory compliance across private clouds, public clouds, and on-premises systems.Skills and expertise gaps: Hybrid cloud environments require specialized knowledge that many IT teams don't currently have. Organizations often struggle to find professionals who understand both traditional infrastructure management and modern cloud technologies.Data management complexity: Moving and synchronizing data between different environments can be challenging and costly. Organizations must carefully plan data placement, backup strategies, and disaster recovery procedures across multiple platforms.Network connectivity issues: Reliable, high-speed connections between private and public cloud environments are essential but can be expensive to establish. Poor network performance can create bottlenecks that reduce the benefits of hybrid cloud architecture.Cost management difficulties: Tracking and controlling expenses across multiple cloud providers and on-premises infrastructure can be complicated. Organizations often find it hard to predict costs and may experience unexpected charges from different services and data transfer fees.Vendor lock-in risks: Choosing specific cloud platforms or technologies can make it difficult to switch providers later. Organizations must balance the benefits of integrated services with the flexibility to change their hybrid cloud plan over time.How to develop a hybrid cloud strategyYou develop a hybrid cloud plan by assessing your current infrastructure, defining clear objectives, and creating a roadmap that balances workload placement, security requirements, and cost optimization across private and public cloud environments.First, conduct a complete audit of your existing IT infrastructure, applications, and data. Document which systems handle sensitive information, which applications experience variable demand, and what compliance requirements you must meet. This assessment forms the foundation for deciding what stays on-premises versus what moves to public cloud.Next, define specific business objectives for your hybrid approach. Determine if you're prioritizing cost reduction, improved flexibility, disaster recovery, or regulatory compliance. Set measurable goals like reducing infrastructure costs by 20% or improving application use speed by 50%.Then, classify your workloads based on sensitivity, performance requirements, and compliance needs. Place highly regulated data and mission-critical applications on private infrastructure, while identifying variable or development workloads that can benefit from public cloud elasticity.Select the right mix of private and public cloud services that align with your workload classification. Evaluate providers based on their integration capabilities, security certifications, and pricing models. Ensure your chosen platforms can communicate effectively through APIs and management tools.Design your network architecture to enable secure, high-performance connectivity between environments. Plan for dedicated connections, VPNs, or hybrid networking solutions that maintain data integrity while allowing cooperation workload movement between private and public resources.Establish governance policies that define when and how workloads move between environments. Create automated rules for scaling to public cloud during peak demand and returning to private infrastructure during normal operations. Include data residency requirements and security protocols in these policies.Finally, use monitoring and management tools that provide unified visibility across all environments. Choose platforms that track performance, costs, and security across your hybrid infrastructure, enabling you to improve resource allocation and identify improvement opportunities.Start with a pilot project involving non-critical workloads to test your hybrid architecture and refine your processes before migrating essential business applications.Gcore hybrid cloud solutionsWhen building a hybrid cloud architecture that can handle both sensitive workloads and flexible applications, the underlying infrastructure becomes the foundation for success. Gcore's hybrid cloud solutions address these complex requirements with 210+ points of presence worldwide and 30ms average latency, ensuring your private and public cloud components work together smoothly. Our edge cloud infrastructure supports the demanding connectivity requirements that hybrid environments need, while our AI infrastructure capabilities help you process workloads effectively across different cloud layers.Explore how Gcore's global infrastructure can support your hybrid cloud plan. Frequently asked questionsWhat's the difference between hybrid cloud and private cloud?Hybrid cloud combines private cloud, public cloud, and on-premises infrastructure into one integrated environment, while private cloud is a dedicated computing environment used exclusively by one organization. Hybrid cloud offers flexibility to move workloads between environments based on security, compliance, and cost needs, whereas private cloud provides maximum control and security but lacks the flexibility and cost benefits of public cloud resources.Is hybrid cloud more expensive than public cloud?Yes, hybrid cloud is typically more expensive than public cloud due to the complexity of managing multiple environments and maintaining private infrastructure alongside public cloud services.How secure is hybrid cloud compared to on-premises infrastructure?Hybrid cloud security is comparable to on-premises infrastructure when properly configured, offering similar data protection with added flexibility. Organizations can maintain sensitive data on private infrastructure while using public cloud resources for less critical workloads, creating a security model that matches their specific risk tolerance.What skills are needed to manage hybrid cloud?Managing hybrid cloud requires technical skills in cloud platforms, networking, security, and automation tools. Key competencies include virtualization technologies. API management, infrastructure-as-code, identity management, and monitoring across multiple environments.How long does hybrid cloud implementation take?Hybrid cloud implementation typically takes 6-18 months, depending on your existing infrastructure complexity and integration requirements. Organizations with established on-premises systems and clear data governance policies can complete basic hybrid deployments in 3-6 months, while complex enterprise environments requiring wide security configurations and legacy system integration may need 12-24 months.

What is cloud networking: benefits, components, and implementation strategies

Cloud networking is the use and management of network resources, including hardware and software, hosted on public or private cloud infrastructures rather than on-premises equipment. Over 90% of enterprises are expected to adopt cloud networking solutions by 2025, indicating rapid industry-wide adoption for IT infrastructure modernization.Cloud networking operates through advanced technologies that separate traditional hardware dependencies from network management. Software-Defined Networking (SDN) serves as a core technology, decoupling network control from hardware to allow centralized, programmable management and automation of network configurations.This approach enables organizations to manage their entire network infrastructure through software interfaces rather than physical device manipulation.The main components of cloud networking include several key elements that work together to create flexible network environments. Virtual Private Clouds (VPCs) provide isolated virtual network environments within the cloud, allowing organizations to define IP ranges, subnets, and routing for enhanced security and control. Virtual network functions (VNFs) replace traditional hardware devices like firewalls, load balancers, and routers with software-based equivalents for easier use and improved flexibility.Cloud networking delivers significant advantages that transform how organizations approach network infrastructure management.These solutions can reduce network operational costs by up to 30% compared to traditional on-premises networking through reduced hardware requirements, lower maintenance overhead, and improved resource use. Cloud networks can scale bandwidth and compute resources within seconds to minutes, demonstrating superior agility compared to traditional manual provisioning methods.Understanding cloud networking has become essential for modern businesses seeking to modernize their IT infrastructure and improve operational effectiveness. This technology enables organizations to build more flexible and cost-effective network solutions that adapt quickly to changing business requirements.What is cloud networking?Cloud networking is the use and management of network resources through virtualized, software-defined environments hosted on cloud infrastructure rather than traditional on-premises hardware. This approach uses technologies like Software-Defined Networking (SDN) to separate network control from physical devices, allowing centralized management and programmable automation of network configurations. Virtual Private Clouds (VPCs) create isolated network environments within the cloud. In contrast, virtual network functions replace traditional hardware like firewalls and load balancers with flexible software alternatives that can scale within seconds to meet changing demands.How does cloud networking work?Cloud networking works by moving your network infrastructure from physical hardware to virtualized, software-defined environments hosted in the cloud. Instead of managing routers, switches, and firewalls in your data center, you access these network functions as services running on cloud platforms.The core mechanism relies on Software-Defined Networking (SDN), which separates network control from the underlying hardware. This means you can configure, manage, and modify your entire network through software interfaces rather than physically touching equipment.When you need a new subnet or firewall rule, you simply define it through an API or web console, and the cloud platform instantly creates the virtual network components.Virtual Private Clouds (VPCs) form the foundation of cloud networking by creating isolated network environments within the shared cloud infrastructure. You define your own IP address ranges, create subnets across different availability zones, and set up routing tables exactly like you would with physical networks. The difference is that all these components exist as software abstractions that can be modified in seconds.Network functions that traditionally required dedicated hardware appliances now run as Virtual Network Functions (VNFs).Load balancers, firewalls, VPN gateways, and intrusion detection systems all operate as software services that you can use, scale, or remove on demand. This approach can reduce network operational costs by up to 30% compared to traditional on-premises networking while providing the flexibility to scale bandwidth and compute resources within seconds to minutes.What are the main components of cloud networking?The main components of cloud networking refer to the key technologies and services that enable network infrastructure to operate in virtualized cloud environments. They are listed below.Software-defined networking (SDN): SDN separates network control from hardware devices, allowing centralized management through software controllers. This approach enables automated network configuration and policy enforcement across cloud resources.Virtual private clouds (VPCs): VPCs create isolated network environments within public cloud infrastructure, giving organizations control over IP addressing, subnets, and routing. They provide secure boundaries between different workloads and applications.Virtual network functions (VNFs): VNFs replace traditional hardware appliances like firewalls, load balancers, and routers with software-based alternatives. These functions can be deployed quickly and scaled on demand without physical hardware constraints.Cloud load balancers: These distribute incoming network traffic across multiple servers or resources to prevent overload and maintain performance. They automatically adjust traffic routing based on server health and capacity.Network security services: Cloud-native security tools include distributed firewalls, intrusion detection systems, and encryption services that protect data in transit. These services combine directly with cloud infrastructure for consistent security policies.Hybrid connectivity solutions: VPN gateways and dedicated network connections link on-premises infrastructure with cloud resources. These components enable secure data transfer between different network environments.Network monitoring and analytics: Real-time monitoring tools track network performance, bandwidth usage, and security events across cloud infrastructure. They provide visibility into traffic patterns and help identify potential issues before they affect users.What are the benefits of cloud networking?The benefits of cloud networking refer to the advantages organizations gain when they move their network infrastructure from physical hardware to virtualized, cloud-based environments. The benefits of cloud networking are listed below.Cost reduction: Cloud networking eliminates the need for expensive physical hardware like routers, switches, and firewalls. Organizations can reduce network operational costs by up to 30% compared to traditional on-premises networking through reduced maintenance, power consumption, and hardware replacement expenses.Instant flexibility: Cloud networks can scale bandwidth and compute resources within seconds to minutes based on demand. This flexibility allows businesses to handle traffic spikes during peak periods without over-provisioning resources during normal operations.Centralized management: Software-Defined Networking (SDN) enables administrators to control entire network infrastructures from a single dashboard. This centralized approach simplifies configuration changes, policy enforcement, and troubleshooting across distributed locations.Enhanced security: Virtual Private Clouds (VPCs) create isolated network environments that prevent unauthorized access between different applications or tenants. Cloud networking achieves compliance with strict standards like GDPR and HIPAA through built-in encryption and access controls.High availability: Cloud providers maintain network uptime SLAs of 99.99% or higher through redundant infrastructure and automatic failover mechanisms. This reliability exceeds what most organizations can achieve with on-premises equipment.Reduced complexity: Network-as-a-Service (NaaS) models eliminate the need for specialized networking staff to manage physical infrastructure. Organizations can focus on their core business while cloud providers handle network maintenance and updates.Global reach: Cloud networking enables instant use of network resources across multiple geographic regions. This global presence improves application performance for users worldwide without requiring physical infrastructure investments in each location.What's the difference between cloud networking and traditional networking?Cloud networking differs from traditional networking primarily in infrastructure location, resource management, and flexibility mechanisms. Traditional networking relies on physical hardware like routers, switches, and firewalls installed and maintained on-premises, while cloud networking delivers these functions as virtualized services managed remotely through cloud platforms.Infrastructure and management approachesTraditional networks require organizations to purchase, install, and configure physical equipment in data centers or office PoPs. IT teams must handle hardware maintenance, software updates, and capacity planning manually.Cloud networking operates through software-defined infrastructure where network functions run as virtual services. Administrators manage entire network configurations through web interfaces and APIs, enabling centralized control across multiple locations without physical hardware access.Flexibility and speedTraditional networking scales through hardware procurement processes that often take weeks or months to complete. Adding network capacity requires purchasing equipment, scheduling installations, and configuring devices individually.Cloud networks scale instantly through software provisioning, allowing organizations to add or remove bandwidth, create new network segments, or use security policies in minutes. This agility enables businesses to respond quickly to changing demands without infrastructure investments.Cost structure and resource allocationTraditional networking involves significant upfront capital expenses for hardware purchases, plus ongoing costs for power, cooling, and maintenance staff. Organizations must estimate future capacity needs and often over-provision to handle peak loads.Cloud networking operates on pay-as-you-go models where costs align with actual usage. According to industry case studies (2024), cloud networking can reduce network operational costs by up to 30% compared to traditional on-premises networking through improved resource effectiveness and reduced maintenance overhead.What are common cloud networking use cases?Common cloud networking use cases refer to the specific scenarios and applications in which organizations use cloud-based networking solutions to meet their infrastructure and connectivity needs. Below are some common cloud networking use cases.Hybrid cloud connectivity: Organizations connect their on-premises infrastructure with cloud resources to create cooperative hybrid environments. This approach allows companies to maintain sensitive data locally while using cloud services for flexibility.Multi-cloud networking: Businesses distribute workloads across multiple cloud providers to avoid vendor lock-in and improve redundancy. This plan enables organizations to choose the best services from different providers while maintaining consistent network policies.Remote workforce enablement: Companies provide secure network access for distributed teams through cloud-based VPN and zero-trust network solutions. These implementations support remote work by ensuring employees can safely access corporate resources from any location.Application modernization: Organizations migrate legacy applications to cloud environments while maintaining network performance and security requirements. Cloud networking supports containerized applications and microservices architectures that require flexible connectivity.Disaster recovery and backup: Businesses replicate their network infrastructure in the cloud to ensure continuity during outages or disasters. Cloud networking enables rapid failover and recovery processes that reduce downtime and data loss.Global content delivery: Companies distribute content and applications closer to end users through cloud-based edge networking solutions. This approach reduces latency and improves user experience for geographically dispersed audiences.Development and testing environments: Teams create isolated network environments in the cloud for application development, testing, and staging. These environments can be quickly provisioned and torn down without affecting production systems.How to implement a cloud networking strategyYou implement a cloud networking plan by defining your network architecture requirements, selecting appropriate cloud services, and establishing security and connectivity frameworks that align with your business objectives.First, assess your current network infrastructure and identify which components can move to the cloud. Document your existing bandwidth requirements, security policies, and compliance needs to establish baseline requirements for your cloud network design.Next, design your Virtual Private Cloud (VPC) architecture by defining IP address ranges, subnets, and routing tables. Create separate subnets for different application tiers and establish network segmentation to isolate critical workloads from less sensitive traffic.Then, establish connectivity between your on-premises infrastructure and cloud resources through VPN connections or dedicated network links. Configure hybrid connectivity to ensure cooperation communication while maintaining security boundaries between environments.After that, use Software-Defined Networking (SDN) controls to centralize network management and enable automated configuration changes. Set up network policies that can flexibly adjust bandwidth allocation and routing based on application demands.Configure cloud-native security services, including network access control lists, security groups, and distributed firewalls. Apply the principle of least privilege by restricting network access to only necessary ports and protocols for each service.Use network monitoring and analytics tools to track performance metrics like latency, throughput, and packet loss. Establish baseline performance measurements and set up automated alerts for network anomalies or capacity thresholds.Finally, create disaster recovery and backup procedures for your network configurations. Document your network topology and maintain version control for configuration changes to enable quick recovery during outages.Start with a pilot using non-critical workloads to validate your network design and performance before migrating mission-critical applications to your new cloud networking environment.Learn more about building a faster, more flexible network with Gcore Cloud.Frequently asked questionsWhat's the difference between cloud networking and SD-WAN?Cloud networking is a broad infrastructure approach that virtualizes entire network environments in the cloud. At the same time, SD-WAN is a specific technology that connects and manages multiple network locations through software-defined controls. Cloud networking includes virtual networks, security services, and compute resources hosted by cloud providers, whereas SD-WAN focuses on connecting branch offices, data centers, and cloud resources through intelligent traffic routing and centralized management.Is cloud networking secure?Yes, cloud networking is secure when properly configured, offering advanced security features like encryption, network isolation, and centralized access controls. Major cloud providers maintain 99.99% uptime SLAs and comply with strict security standards, including GDPR and HIPAA, through technologies like Virtual Private Clouds that isolate network traffic.How much does cloud networking cost compared to traditional networking?Cloud networking costs 20-40% less than traditional networking due to reduced hardware expenses, maintenance, and staffing requirements. Organizations save on upfront capital expenditures while gaining predictable monthly operational costs through subscription-based cloud services.How does cloud networking affect network performance?Cloud networking can both improve and reduce network performance depending on your specific setup and requirements.Cloud networking typically improves performance through global content delivery networks that reduce latency by 40-60%, automatic growing that handles traffic spikes within seconds, and advanced routing that optimizes data paths. However, performance can decrease if you're moving from a well-optimized local network to a poorly configured cloud setup, or if your applications require extremely low latency that adds overhead from internet routing and virtualization layers.What happens if cloud networking services experience outages?Cloud networking outages cause service disruptions, including loss of connectivity, reduced application performance, and potential data access issues lasting from minutes to several hours. Most major cloud providers maintain 99.99% uptime guarantees and use redundant systems to reduce outage impact through automatic failover to backup infrastructure.

What is object storage? Benefits, use cases, and how it works

Object storage is a data storage architecture that manages data as discrete units called objects, each containing the data itself, metadata, and a unique identifier. Unlike traditional storage methods, object storage systems can scale to exabyte-scale capacity by adding storage nodes, supporting massive unstructured data growth.Object storage operates through a flat address space without hierarchical file systems, where each object is stored in a flat data environment and accessed directly via its unique identifier. This architecture eliminates the need for directory structures and enables multiple access paths to the same data.The OSD standard specifies 64-bit identifiers for partitions and objects, creating a vast address space for object storage systems.The storage approach differs especially from file storage, which organizes data hierarchically in folders, and block storage, which breaks data into blocks with unique addresses. Object storage's flat structure allows for more flexible data organization and retrieval patterns. Each storage method serves different use cases based on how applications need to access and manage data.Object storage systems provide several key advantages, including automatic data distribution across multiple storage nodes for high durability and availability.These systems typically maintain a replication factor of three or more copies of each object across different nodes. The metadata in object storage is extensible and user-definable, allowing rich descriptive information to be stored alongside data, which supports advanced data management and analytics capabilities.This storage architecture has become essential for modern applications dealing with large amounts of unstructured data, from backup and archival systems to content distribution and big data analytics platforms.What is object storage?Object storage is a data storage architecture that manages data as discrete units called objects, each containing the data itself, metadata, and a unique identifier. Unlike file storage systems that organize data in hierarchical folders or block storage that splits data into addressed blocks, object storage uses a flat address space where each object can be accessed directly through its unique ID. This approach eliminates directory structures and enables multiple access paths to the same data, making it ideal for storing and retrieving large amounts of unstructured data like photos, videos, documents, and web content.The architecture stores objects across multiple storage nodes with automatic replication to ensure high durability and availability.Each object includes rich, user-definable metadata that provides detailed information about the stored data, supporting advanced search capabilities and data management workflows. Object storage systems can scale to exabytes of capacity simply by adding more storage nodes, making them perfect for organizations dealing with massive data growth. The flat namespace design means there's no performance degradation as storage volumes increase, unlike traditional hierarchical file systems that can slow down with deep directory structures.How does object storage work?Object storage works by managing data as discrete units called objects, where each object contains the actual data, descriptive metadata, and a unique identifier for direct access. Unlike traditional file systems that organize data in hierarchical folders, object storage uses a flat address space where every object can be retrieved directly using its unique ID, eliminating the need for complex directory structures.The system stores each object across multiple storage nodes to ensure high availability and durability. When you upload data, the object storage system automatically creates copies (typically three or more) and distributes them across different nodes in the storage cluster.This replication protects against hardware failures and ensures your data remains accessible even if individual nodes go offline.Each object includes rich, extensible metadata that you can customize to store descriptive information about your data. This metadata enables powerful search capabilities and automated data management policies. For example, you might store creation dates, content types, access permissions, or business-specific tags that help organize and retrieve data later.Object storage excels at handling unstructured data like photos, videos, documents, and sensor data.The flat namespace design allows systems to scale to exabyte-level capacity by simply adding more storage nodes. You access objects through RESTful APIs using standard HTTP methods, making it easy to combine with web applications and cloud services. This architecture delivers cost-effective storage with high durability while simplifying data management compared to traditional storage approaches.How does object storage compare to file storage and block storage?Object storage differs from file storage and block storage by using a predominantly different architecture. It stores data as discrete objects with metadata and unique identifiers in a flat namespace rather than hierarchical directories or fixed-size blocks.Storage architecture differencesFile storage organizes data in a hierarchical structure with folders and subfolders, similar to your computer's file system. You access files through specific paths like `/folder/subfolder/file.txt`. Block storage breaks data into fixed-size chunks (blocks) that get stored across multiple locations, with each block having a unique address.Applications reassemble these blocks when accessing data.Object storage eliminates both approaches. It stores each piece of data as a complete object containing the actual data, rich metadata, and a globally unique identifier. These objects live in a flat address space called buckets or containers, with no directory structure to navigate.Flexibility and performanceObject storage scales to exabyte levels by simply adding more storage nodes to the cluster.The flat namespace means you don't hit the performance bottlenecks that hierarchical file systems face with millions of files in directories. Block storage scales well but requires more complex management as you add storage volumes.File storage performance degrades as directory structures grow deep and wide. Object storage maintains consistent performance because it accesses data directly through unique identifiers rather than traversing directory trees.Data access methodsYou access object storage through REST APIs using HTTP methods (GET. PUT. DELETE), making it perfect for web applications and cloud services.File storage uses traditional file system protocols like NFS or SMB. Block storage requires mounting as volumes to operating systems, then formatting with file systems.This API-based access makes object storage ideal for applications that need to store and retrieve unstructured data, such as images, videos, backups, and documents, from anywhere on the Internet.Cost and use casesObject storage typically costs $0.01 to $0.02 per GB per month, making it the most economical option for large-scale data storage. Block storage costs more due to higher performance requirements, while file storage falls somewhere between.Object storage works best for backup and archiving, content distribution, big data analytics, and storing static web content.Block storage suits databases and applications requiring low-latency access. File storage fits traditional applications needing shared file access across multiple users or systems.What are the key benefits of object storage?The key benefits of object storage refer to the advantages organizations gain from using this data storage architecture that manages information as discrete units with metadata and unique identifiers. The key benefits of object storage are listed below.Massive flexibility: Object storage systems can scale to exabytes of data by simply adding storage nodes to the cluster. This horizontal growing approach supports the explosive growth of unstructured data without requiring complex restructuring of the storage architecture.High durability and availability: Object storage systems automatically replicate data across multiple nodes, typically maintaining three or more copies of each object. This replication provides extremely high durability rates, with leading services offering 99.999999999% (11 nines) durability, meaning virtually no risk of data loss.Cost effectiveness: Cloud object storage typically costs $0.01 to $0.02 per GB per month, making it highly cost-effective for storing large volumes of data. The flat pricing model and elimination of complex directory structures reduce both storage and management costs.Rich metadata support: Each object can store wide, user-definable metadata alongside the actual data, enabling advanced search, classification, and analytics capabilities. This metadata richness supports automated data management policies and intelligent data processing workflows.Simplified data management: The flat namespace eliminates complex directory hierarchies, making data organization and retrieval more straightforward. Objects are accessed directly via unique identifiers, reducing the complexity of data location and management tasks.Global accessibility: Object storage provides multiple access methods, including REST APIs, making data accessible from anywhere with proper authentication. This accessibility supports distributed applications and remote data access scenarios across different geographic locations.What are common object storage use cases?Object storage use cases refer to specific applications and scenarios in which organizations use object storage systems to manage unstructured data at scale. The use cases are listed below.Backup and archiving: Object storage provides cost-effective long-term data retention with high durability guarantees. Organizations can store backup copies of critical data with automated replication across multiple locations, ensuring data protection against hardware failures or disasters.Content distribution: Media companies and websites use object storage to serve static content like images, videos, and documents to global audiences. The flat namespace structure allows effective content delivery without complex directory management.Big data analytics: Data scientists store massive datasets in object storage for processing by analytics platforms and machine learning algorithms. The rich metadata capabilities enable easy data discovery and organization for analytical workloads.Cloud-native applications: Modern applications built for cloud environments use object storage to handle user-generated content, application logs, and temporary files. The flexible architecture supports applications that need to grow storage capacity flexibly.Disaster recovery: Organizations replicate critical data to object storage systems in different geographic locations as part of their disaster recovery plan. The automatic replication features ensure data remains accessible even during major outages.IoT data storage: Internet of Things devices generate continuous streams of sensor data that object storage systems can ingest and store effectively. The ability to handle millions of small files makes it ideal for IoT applications.Medical imaging: Healthcare organizations store large medical images like MRIs, CT scans, and X-rays in object storage systems. The metadata capabilities allow medical professionals to tag and search images based on patient information and diagnostic data.What are data lakes, and how do they relate to object storage?A data lake is a centralized repository that stores vast amounts of raw data in its native format until it's needed for analysis or processing. Data lakes can store structured, semi-structured, and unstructured data from multiple sources without requiring a predefined schema, making them highly flexible for organizations dealing with diverse data types. This approach allows companies to capture and store all their data first, then determine how to process and analyze it later based on specific business needs.Object storage serves as the foundational technology that makes data lakes possible and flexible. Object storage manages data as discrete units called objects, each containing the data itself, metadata, and a unique identifier, stored in a flat address space without hierarchical directory structures. This architecture perfectly supports data lake requirements because it can handle massive volumes of unstructured data like log files, sensor data, images, and videos that don't fit well into traditional databases.The relationship between data lakes and object storage is complementary. Object storage provides the underlying infrastructure while data lakes represent the architectural approach to data management. Object storage systems can scale to exabytes of data by adding storage nodes, supporting the massive unstructured data growth that data lakes are designed to accommodate. The rich metadata capabilities of object storage also enable data lakes to maintain detailed information about stored data, making it easier to catalog, search, and govern large datasets across the organization.How to choose the right object storage solutionYou choose the right object storage solution by evaluating your data requirements, performance needs, flexibility demands, security requirements, and cost considerations across different use options.First, assess your data volume and growth projections over the next 2-3 years. Calculate your current unstructured data size, including videos, images, documents, and backups, then add a 30-40% buffer for unexpected growth to avoid frequent migrations.Next, determine your access patterns and performance requirements. Hot data that you access frequently needs low-latency retrieval, while cold archival data can tolerate slower access times in exchange for lower storage costs.Then, evaluate your flexibility needs based on whether you expect gradual growth or sudden spikes in data volume. Look for solutions that can scale to exabyte-level capacity without requiring major infrastructure changes or performance degradation.Compare use models between cloud-based, on-premises, and hybrid solutions. Cloud object storage typically costs $.Examine security and compliance features, including encryption at rest and in transit, access controls, audit logging, and regulatory compliance certifications. Verify that the solution meets your industry requirements, such as HIPAA for healthcare or GDPR for European data.Test API compatibility and combination capabilities with your existing applications and workflows. Most solutions support S3-compatible APIs, but verify performance and feature parity for your specific use cases.Finally, analyze the total cost of ownership, including storage fees, data transfer charges, API request costs, and any additional features like cross-region replication or advanced analytics capabilities.Start with a proof-of-concept using a small dataset to validate performance, costs, and combinations before committing to full-scale use.Gcore object storage solutionsWhen choosing an object storage solution for your organization, the technical requirements we've discussed (flexibility, durability, and performance) must translate into real-world infrastructure capabilities. Gcore Object Storage delivers on these fundamentals with S3-compatible APIs, automatic data replication across multiple nodes, and cooperation to handle growing data volumes without the complexity of traditional storage hierarchies.What sets Gcore apart is the combination of enterprise-grade reliability with cost-effective pricing, offering the 99.999999999% durability you need for critical unstructured data while maintaining competitive per-GB rates. The platform's global edge locations ensure low-latency access to your objects worldwide, whether you're serving static web content, managing backup archives, or supporting big data analytics workflows.Explore how Gcore Object Storage can simplify your data management plan. Frequently asked questionsWhat's the difference between object storage and blob storage?There's no difference - "blob storage" and "object storage" are two names for the same technology. Blob (Binary Large Object) is simply Microsoft's terminology for what the industry calls object storage, where data is stored as discrete units with metadata and unique identifiers in a flat namespace rather than hierarchical folders.How much does object storage cost compared to other storage types?Object storage costs 50-70% less than traditional file or block storage, with cloud pricing around $0.01-$0.02 per GB monthly compared to $0.05-$0.10 for high-performance alternatives.Can object storage replace all my other storage needs?No, object storage can't replace all your storage needs because it's designed specifically for unstructured data and lacks the performance characteristics required for databases, operating systems, and applications that need low-latency block-level access.Object storage excels at storing photos, videos, backups, and static web content. However, you'll still need block storage for virtual machines and databases, plus file storage for shared network drives and collaborative workspaces.What is S3 compatibility and why does it matter?S3 compatibility means storage systems can use Amazon S3's API commands and protocols, allowing applications built for S3 to work with other storage providers without code changes. This matters because it prevents vendor lock-in and lets organizations switch between storage providers while keeping their existing applications, tools, and workflows intact.Is object storage secure for sensitive data?Yes, object storage is highly secure for sensitive data through multiple layers of protection, including encryption at rest and in transit, access controls, and data replication across geographically distributed nodes. Enterprise object storage systems typically offer 99.999999999% (11 nines) durability and support compliance frameworks like SOC 2, HIPAA, and GDPR for regulated industries.

What is block storage? Benefits, use cases, and implementation

Block storage is a data storage method that divides data into fixed-size chunks called blocks, each with a unique logical block address (LBA). Over 70% of enterprise mission-critical applications rely on block storage for data persistence, making it one of the most widely adopted storage architectures in modern computing environments.Block storage operates by treating data as uniform blocks rather than files in folders, which enables the operating system to access storage as a continuous range of LBAs. This approach abstracts the physical location of data on the storage media, allowing for effective random read and write operations.The system can achieve latency as low as sub-millisecond on NVMe SSDs, making it ideal for performance-sensitive applications.The architecture of block storage differs from file storage and object storage in how it organizes and accesses data. While file storage uses hierarchical directory structures and object storage employs metadata-rich containers, block storage provides raw storage volumes that operating systems can format with any file system. This flexibility makes block storage the underlying foundation for other storage types, offering greater control over data organization and access patterns.Block storage delivers several key advantages for enterprise environments, including high-performance random access, consistent low latency, and support for transactional workloads.Major cloud providers offer block storage services with performance specifications reaching up to 256,000 IOPS and 4,000 MB/s throughput. These capabilities make block storage particularly valuable for databases, virtual machine storage, and applications requiring predictable performance characteristics.Understanding block storage is important for IT professionals because it forms the backbone of most enterprise storage infrastructures and directly impacts application performance, data availability, and system flexibility in both on-premises and cloud environments.What is block storage?Block storage is a data storage method that divides data into fixed-size chunks called blocks, each assigned a unique logical block address (LBA) for independent access. The operating system treats these blocks as a continuous range of addresses, abstracting the physical location of data on storage media like HDDs, SSDs, or NVMe drives. This approach enables effective random read/write operations since each block can be accessed directly without reading through other data, making it ideal for applications requiring high performance and low latency.Block storage serves as the foundational layer for other storage types, such as file and object storage. It's typically accessed over networks using protocols such as iSCSI over Ethernet or SCSI over Fibre Channel.How does block storage work?Block storage works by dividing data into fixed-size chunks called blocks, each assigned a unique logical block address (LBA) that the operating system uses to locate and access information. The system treats each block as an independent unit, typically ranging from 512 bytes to 4 KB in size, allowing for effective random read and write operations across the storage medium.When you save data, the block storage system breaks it into these uniform blocks and distributes them across available storage space on physical media like HDDs, SSDs, or NVMe drives. The operating system maintains a mapping table that tracks which LBAs correspond to specific physical locations, creating an abstraction layer that hides the complexity of data placement from applications and users.The key advantage of this approach is that blocks can be accessed independently and in any order, making it ideal for applications requiring high performance and low latency.Unlike file storage systems that organize data hierarchically in folders, block storage presents a flat address space where each block is directly addressable. This design enables consistent throughput and supports demanding workloads like databases and virtual machines that need predictable storage performance.Block storage typically connects over network protocols such as iSCSI over Ethernet or SCSI over Fibre Channel, allowing multiple servers to access the same storage resources. The system requires a file system layer to organize these raw blocks into recognizable files and directories for end users.How does block storage compare to file storage and object storage?Block storage compares to file storage and object storage by operating at different levels of data abstraction and serving distinct use cases. Block storage divides data into fixed-size chunks with unique addresses, file storage organizes data in hierarchical folders, and object storage manages data as discrete objects with metadata.Performance and access patternsBlock storage delivers the highest performance with sub-millisecond latency on modern NVMe drives and effectively supports random read/write operations. It provides direct access to storage blocks without file system overhead, making it ideal for databases and virtual machines that require consistent high IOPS.File storage offers good performance for sequential access but can struggle with random operations due to file system processing. Object storage prioritizes flexibility over speed, with higher latency but excellent throughput for large file transfers.Architecture and flexibilityBlock storage requires a file system layer to organize blocks into usable files and directories, giving applications complete control over data layout. File storage includes built-in file system management with features like permissions, metadata, and hierarchical organization.Object storage uses a flat namespace where each object contains data, metadata, and a unique identifier, eliminating the need for complex directory structures and enabling virtually unlimited flexibility.Use cases and applicationsBlock storage excels in scenarios demanding low latency and high performance, such as database storage, virtual machine disks, and enterprise applications requiring consistent throughput. File storage works best for shared access scenarios like network file shares, content management systems, and collaborative environments where multiple users need simultaneous access. Object storage suits applications requiring massive flexibility, such as backup systems, content distribution, data archiving, and cloud-native applications that can handle eventual consistency.What are the key benefits of block storage?The key benefits of block storage refer to the advantages organizations gain from using this foundational data storage method that divides information into fixed-size chunks with unique addresses. The key benefits of block storage are listed below.High performance: Block storage delivers exceptional speed with sub-millisecond latency on modern NVMe SSDs and can achieve up to 256,000 IOPS. This performance makes it ideal for demanding applications like databases and real-time analytics.Flexible scalability: Storage capacity can be expanded or reduced independently without affecting application performance. Organizations can add or remove storage blocks as needed, paying only for what they use.Direct hardware access: Block storage provides raw, unformatted storage that applications can access directly at the hardware level. This direct access eliminates file system overhead and maximizes throughput for performance-critical workloads.Snapshot capabilities: Point-in-time copies of data can be created instantly without interrupting operations. These snapshots enable quick backup, recovery, and testing scenarios while consuming minimal additional storage space.Multi-protocol support: Block storage works with various network protocols, including iSCSI, Fibre Channel, and NVMe over Fabrics. This compatibility allows it to be combined with existing infrastructure and diverse operating systems.Data persistence: Storage volumes maintain data independently of compute instances, ensuring information survives server failures or restarts. This separation provides reliability for mission-critical applications that can't afford data loss.Fine-grained control: Administrators can configure specific performance characteristics, encryption settings, and access permissions for individual storage volumes. This granular control enables optimization for different application requirements and security policies.What are common block storage use cases?Common block storage use cases refer to the specific applications and scenarios where organizations use block-level storage solutions to meet their data management needs. Typical block storage use cases are listed below.Database storage: Block storage provides the high-performance foundation that database systems require for consistent read and write operations. The direct access to individual blocks enables databases to quickly retrieve and update specific data records without processing entire files.Virtual machine storage: Virtual machines rely on block storage to create virtual disks that function like physical hard drives within the virtualized environment. This approach allows each VM to have dedicated storage space with predictable performance characteristics.Boot volumes: Operating systems use block storage as boot volumes to store system files and launch applications during startup. The low-latency access ensures fast boot times and responsive system performance.High-performance computing: Scientific simulations and data analysis workloads depend on block storage for its ability to handle intensive input/output operations. The consistent throughput supports applications that process large datasets or perform complex calculations.Backup and disaster recovery: Block storage serves as a reliable target for backup operations, allowing organizations to create point-in-time snapshots of their data. The block-level approach enables effective incremental backups that only copy changed data blocks.Container persistent storage: Containerized applications use block storage to maintain data persistence beyond the container lifecycle. This ensures that important application data survives container restarts and updates.Enterprise applications: Mission-critical business applications require the consistent performance and reliability that block storage delivers. The predictable latency and throughput support applications like ERP systems and customer databases that can't tolerate storage-related delays.What are Storage Area Networks (SANs), and how do they use block storage?A Storage Area Network (SAN) is a dedicated high-speed network that connects storage devices to servers, providing block-level data access across the network infrastructure. SANs use block storage by presenting storage volumes as raw block devices to connected servers, where each block has a unique logical block address that servers can access directly without file system overhead. This architecture allows multiple servers to share centralized storage resources while maintaining the performance characteristics of directly-attached storage, with enterprise SANs typically delivering sub-millisecond latency through protocols like Fibre Channel or iSCSI. The block storage foundation enables SANs to support mission-critical applications like databases and virtual machine environments that require consistent, high-performance data access.How to implement block storage in cloud environmentsYou use block storage in cloud environments by provisioning virtual block devices that attach to compute instances and configuring them with appropriate file systems and performance settings.First, choose your block storage service from your cloud provider's offerings. Most platforms offer multiple tiers with different performance characteristics, from general-purpose volumes delivering up to 3,000 IOPS to high-performance options supporting over 64,000 IOPS for demanding workloads.Next, create your block storage volume by specifying the size, type, and performance requirements. Start with general-purpose SSD storage for most applications, then upgrade to provisioned IOPS volumes if you need consistent high performance for databases or other I/O-intensive applications.Then, attach the volume to your compute instance through the cloud console or API. The volume appears as a raw block device that your operating system can detect, similar to adding a new hard drive to a physical server.After that, format the attached volume with your preferred file system. Use ext4 for Linux systems or NTFS for Windows, depending on your application requirements and compatibility needs.Mount the formatted volume to your desired directory path and configure automatic mounting on system restart. Update your system's fstab file to ensure the volume mounts correctly after reboots.Configure backup and snapshot policies to protect your data. Most cloud platforms offer automated snapshot scheduling that creates point-in-time copies without downtime, allowing quick recovery from data corruption or accidental deletion.Finally, monitor performance metrics like IOPS, throughput, and latency to ensure your storage meets application requirements. Set up alerts for capacity thresholds and performance degradation to prevent service disruptions.Always test your block storage configuration with your actual workload before going into production, as performance can vary, primarily based on instance type, network conditions, and concurrent usage patterns. Find out more about optimizing your infrastructure with Gcore's high-performance storage solutions.Frequently asked questionsWhat's the difference between block storage and direct-attached storage (DAS)?Block storage and direct-attached storage (DAS) differ in their connection method: block storage connects over a network using protocols like iSCSI, while DAS connects directly to a single server through physical cables like SATA or SAS. Block storage can be shared across multiple servers and accessed remotely, whereas DAS provides dedicated storage exclusively to one connected server.How much does block storage cost compared to other storage types?Block storage costs 20-50% less than file storage for high-performance workloads but costs more than object storage for long-term archival needs. The price difference comes from block storage's direct-attached architecture requiring less processing overhead than file systems, while object storage wins on cost for infrequently accessed data due to its distributed design and lower redundancy requirements.Can block storage be used for backup and archival?Yes, block storage works well for backup and archival with features like point-in-time snapshots, versioning, and long-term retention policies. Many organizations use block storage for both operational backups and compliance archiving due to its reliability and data integrity guarantees.What is IOPS, and why does it matter for block storage?IOPS (Input/Output Operations Per Second) measures how many read/write operations a storage device can perform each second. It matters for block storage because it directly determines application performance and responsiveness. Higher IOPS means faster database queries, quicker virtual machine boot times, and better user experience for applications that frequently access stored data.Is block storage secure for sensitive data?Yes, block storage is secure for sensitive data when properly configured with encryption, access controls, and network security measures. Enterprise block storage systems provide multiple security layers, including data-at-rest encryption, in-transit encryption, and role-based access management to protect sensitive information.How does block storage handle failures and redundancy?Block storage handles failures through data replication across multiple drives and servers, automatically switching to backup copies when primary storage fails. Most enterprise block storage systems maintain 2-3 copies of data with automatic failover that completes in under 30 seconds.

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