Introduction

Memory management is a critical component of computer systems and software development, ensuring that a system uses memory resources efficiently. It refers to the processes and techniques used to allocate, track, and free memory during the execution of programs. Proper memory management is essential for system performance, stability, and reliability, particularly in low-level programming, operating systems, and large-scale applications.

Memory management allows programs to allocate and deallocate memory dynamically during their execution, making optimal use of available resources. This guide will explore what memory management is, its major use cases, how memory management works at a system level, the architecture of memory management, and the basic workflow of managing memory effectively. We’ll also provide a step-by-step guide on how to get started with memory management in a variety of programming environments.

What is Memory Management?

Memory management refers to the process of controlling and coordinating computer memory, allocating and freeing memory blocks, and ensuring that memory is used efficiently throughout a program’s lifecycle. In modern computing, this includes both primary memory (RAM) and secondary memory (storage).

Memory management has two main goals:

1. Efficient Memory Allocation: Ensuring that memory is allocated to processes and programs in such a way that resources are used optimally.
2. Memory Deallocation: Ensuring that memory that is no longer needed is properly released to prevent memory leaks and wasted resources.

Memory management is implemented in both hardware and software. The operating system (OS) is responsible for managing memory, but application-level programming languages also have specific memory management mechanisms (e.g., automatic garbage collection in languages like Java or manual memory allocation in C and C++).

Types of Memory Management

• Static Memory Allocation: Memory is allocated at compile time, and its size is fixed throughout the program’s execution.
• Dynamic Memory Allocation: Memory is allocated during the program’s execution (at runtime), which allows for more flexibility and efficient use of memory resources.

In low-level programming languages like C and C++, developers are required to manage memory explicitly, while in languages like Java or Python, memory management is handled by the garbage collector.

Major Use Cases of Memory Management

Memory management plays a crucial role in many areas of computing and software engineering. Here are some of the major use cases:

1. Efficient Application Performance

By managing memory properly, an application can run efficiently without consuming excessive resources. This ensures that other processes or applications on the system aren’t slowed down by unnecessary memory consumption.

• Use Case Example: In large-scale applications like video games or database management systems, memory management is critical to optimize performance and prevent crashes or slowdowns.

2. Handling Large Data Structures

Memory management allows programs to efficiently work with large data structures (e.g., arrays, trees, graphs) without running out of memory. This is crucial in fields like machine learning, data science, and big data.

• Use Case Example: Allocating memory dynamically for large training datasets in machine learning algorithms.

3. Preventing Memory Leaks

A memory leak occurs when a program allocates memory but fails to release it once it’s no longer needed. Proper memory management ensures that memory is freed when no longer required, preventing leaks and optimizing memory use.

• Use Case Example: In long-running applications like web servers or real-time applications, memory leaks can cause crashes if not properly managed.

4. Supporting Multitasking and Multiprocessing

Memory management ensures that multiple processes or threads can run simultaneously on a system without interfering with each other, providing isolation and stability.

• Use Case Example: Memory management in operating systems like Linux or Windows is critical for multitasking environments, where multiple applications are running concurrently.

5. Embedded Systems and Real-Time Applications

In embedded systems (e.g., IoT devices, medical equipment), where resources are limited, memory management ensures that every byte of memory is used effectively.

• Use Case Example: In an embedded system controlling a drone or a sensor network, efficient memory management is essential to avoid crashes or malfunctions.

How Memory Management Works: Architecture

Memory management is implemented at both the hardware level and the software level. Here’s how memory management is typically structured:

1. Memory Segmentation

Memory is typically divided into several regions, each serving a specific purpose:

• Code Segment (Text Segment): Stores the program’s executable instructions.
• Data Segment: Holds global variables and static variables.
• Heap: Dynamically allocated memory used by applications during runtime.
• Stack: Stores function call information, local variables, and function parameters.

2. Memory Allocation Techniques

• Static Allocation: Fixed memory allocation at compile time (e.g., global variables).
• Dynamic Allocation: Memory is allocated at runtime using functions like malloc() or new in languages like C/C++.

3. Memory Management Units (MMUs)

The Memory Management Unit (MMU) is a hardware component responsible for translating logical addresses into physical addresses in memory. It plays a significant role in virtual memory systems by enabling paging and segmentation.

4. Virtual Memory

Virtual memory enables a system to use disk space as additional memory, allowing programs to access more memory than physically available by paging parts of the program into and out of physical memory.

5. Garbage Collection (GC)

In higher-level programming languages, garbage collection automatically manages memory by identifying and freeing up memory that is no longer being used. Languages like Java and Python handle memory management through garbage collectors, which run in the background and reclaim unused memory.

Basic Workflow of Memory Management

The basic workflow of memory management involves several steps:

1. Memory Allocation: Allocating memory when required by the program. This can be done statically (at compile time) or dynamically (at runtime).
2. Memory Use: The program utilizes the allocated memory for storing data, executing instructions, and processing.
3. Memory Deallocation: Once memory is no longer needed, it must be properly released back to the system to avoid memory leaks.
4. Garbage Collection (Optional): In languages with automatic memory management, garbage collectors periodically reclaim unused memory automatically.

Step-by-Step Getting Started Guide for Memory Management

Here’s how you can get started with memory management in C (manual management) and Python (automatic management):

Step 1: Install Development Environment

For languages like C/C++, ensure you have a development environment with tools like GCC or Clang installed. For Python, just install Python from the official website.

Step 2: Memory Management in C (Manual)

In C, memory management is done manually using functions like malloc(), free(), and realloc().

Example in C:

#include <stdio.h>
#include <stdlib.h>

int main() {
    int *ptr = (int*)malloc(sizeof(int));  // Allocating memory for an integer
    if (ptr == NULL) {
        printf("Memory allocation failed\n");
        return 1;
    }
    
    *ptr = 10;  // Storing value in dynamically allocated memory
    printf("Value: %d\n", *ptr);

    free(ptr);  // Freeing allocated memory
    return 0;
}

In this example, malloc() allocates memory, and free() deallocates it to avoid memory leaks.

Step 3: Memory Management in Python (Automatic)

In Python, memory management is handled automatically by the garbage collector, which reclaims memory when objects are no longer in use.

Example in Python:

def create_list():
    my_list = [1, 2, 3, 4, 5]
    return my_list

data = create_list()
print(data)  # Accessing the list

# Python automatically handles memory management, no need to free memory explicitly

In this example, Python automatically handles the creation and destruction of memory for objects like my_list when they are no longer referenced.

Step 4: Understanding Memory Leaks

A memory leak occurs when memory that is no longer needed is not properly deallocated. In C/C++, this can happen if malloc() or new is used without corresponding calls to free() or delete. In Python, memory leaks can occur if objects are not properly dereferenced.

Example of Memory Leak in C:

// This will cause a memory leak because malloc() is not paired with free()
int* ptr = (int*)malloc(sizeof(int));  // Memory allocated
// No call to free(ptr), resulting in a memory leak

A DevOps Engineer plays a critical role in ensuring that development, operations, and IT teams work together effectively to deliver high-quality software in a fast and efficient manner. The daily work of a DevOps Engineer involves a mix of tasks, including automation, system monitoring, incident resolution, and collaboration with various teams to improve the overall software lifecycle.

In this post, we will explore the typical daily tasks of a DevOps Engineer, shedding light on the responsibilities they handle and the tools they use to ensure smooth software development and operations.

1. Managing CI/CD Pipelines

Major Responsibilities:

• Monitor Continuous Integration (CI):
  • DevOps Engineers ensure that continuous integration (CI) pipelines are running smoothly. This involves checking the automated build and test processes, verifying that code changes integrate correctly, and catching any issues early in the development cycle.
• Manage Continuous Deployment (CD):
  • They oversee continuous delivery (CD) pipelines, ensuring that code changes automatically flow through testing and deployment environments. DevOps Engineers make sure that deployments happen quickly and reliably without breaking production systems.
• Automate Testing and Deployment:
  • A key task is automating repetitive tasks like testing, integration, and deployment. DevOps Engineers typically work with Jenkins, GitLab CI/CD, CircleCI, or other tools to automate these processes.
• Troubleshoot Pipeline Failures:
  • If there are any pipeline failures, DevOps Engineers dive into logs and metrics to troubleshoot and resolve issues, ensuring that code can be deployed quickly and with minimal manual intervention.

2. Infrastructure Management and Automation

Major Responsibilities:

• Provision and Manage Infrastructure:
  • DevOps Engineers are responsible for managing infrastructure in both on-premise and cloud environments (AWS, Azure, Google Cloud). They ensure that the underlying infrastructure is properly provisioned, configured, and scalable to meet the needs of the applications.
• Infrastructure as Code (IaC):
  • DevOps Engineers write and maintain Infrastructure as Code (IaC) scripts using tools like Terraform, AWS CloudFormation, or Ansible to automate the creation and management of infrastructure resources.
• Cloud Services and Virtual Machines:
  • They regularly interact with cloud services, managing virtual machines, containers, and databases. Automation is key to ensuring that the infrastructure is consistent across environments, minimizing configuration drift.
• Ensure Cost Efficiency:
  • DevOps Engineers also monitor resource utilization, helping to optimize cloud spending by scaling down unused resources or using reserved instances to reduce costs.

3. System Monitoring and Incident Management

Major Responsibilities:

• Real-Time Monitoring of Systems:
  • DevOps Engineers set up and maintain real-time monitoring systems using tools like Prometheus, Grafana, Datadog, or the ELK Stack. They track application performance, system health, and infrastructure metrics to ensure everything runs smoothly.
• Incident Response and Troubleshooting:
  • When issues arise in the system, such as performance degradation or downtime, DevOps Engineers are the first to respond. They troubleshoot and resolve incidents to minimize disruption, often using log aggregation tools like Splunk or ELK Stack to diagnose issues.
• Root Cause Analysis:
  • After resolving incidents, DevOps Engineers perform root cause analysis (RCA) to understand the underlying issues and implement corrective actions to prevent recurrence. This could involve fixing bugs, optimizing code, or improving system architecture.
• Alert Management:
  • They configure alerting systems that notify teams of potential issues, allowing for quick resolution. Ensuring that the right people receive alerts at the right time is a crucial task in maintaining system uptime.

4. Collaboration with Development and Operations Teams

Major Responsibilities:

• Facilitate Cross-Functional Communication:
  • DevOps Engineers work as a bridge between the development (Dev) and operations (Ops) teams, fostering communication and collaboration. They ensure that developers have the tools and resources needed for efficient coding, testing, and deployment.
• Support Developers in CI/CD Process:
  • They help development teams integrate new code into the system by ensuring that CI/CD pipelines are correctly set up. DevOps Engineers work closely with developers to optimize deployment workflows and streamline the overall process.
• Collaborate on Automation Projects:
  • They collaborate with various teams to automate manual processes, such as deployments, environment configuration, and scaling. Their goal is to reduce friction and improve efficiency across the development and operations workflows.
• Provide Technical Guidance:
  • DevOps Engineers often provide guidance on best practices for system design, cloud usage, and automation. They act as technical mentors for other team members, helping them optimize the deployment and infrastructure management process.

5. Security and Compliance

Major Responsibilities:

• Implement DevSecOps Practices:
  • DevOps Engineers work to integrate security practices into the software delivery pipeline, a practice known as DevSecOps. This includes ensuring that security is baked into CI/CD processes, performing code scanning for vulnerabilities, and automating security testing.
• Ensure Compliance:
  • They also ensure that the deployed applications comply with industry regulations such as GDPR, HIPAA, or PCI-DSS. This includes setting up security monitoring, access control, and audit trails.
• Access Control Management:
  • DevOps Engineers manage access control policies to ensure that only authorized personnel can access critical systems and data. They implement tools like Vault or AWS IAM to manage secrets and credentials securely.
• Proactive Security Audits:
  • They perform regular security audits of infrastructure and software deployments, ensuring compliance with security best practices. This includes using tools like Snyk or Aqua Security to scan for vulnerabilities.

6. Continuous Improvement and Optimization

Major Responsibilities:

• Evaluate System Performance:
  • DevOps Engineers constantly evaluate system performance and identify bottlenecks in the infrastructure. They use tools like Grafana or New Relic to monitor application performance and optimize performance across various services.
• Automate Repetitive Tasks:
  • One of their primary objectives is to automate repetitive tasks to reduce manual intervention. This could include automating database backups, patch management, and scaling applications based on usage patterns.
• Scaling Systems:
  • DevOps Engineers ensure that systems are scalable, particularly during peak demand periods. They configure auto-scaling mechanisms using tools like Kubernetes or AWS Auto Scaling to handle changes in traffic or resource needs.
• Optimize Resource Usage:
  • DevOps Engineers regularly monitor resource consumption and optimize usage to reduce costs and improve efficiency. This includes optimizing cloud infrastructure to avoid overprovisioning and ensuring that resources are used efficiently.

A Day in the Life of a DevOps Engineer

The daily work of a DevOps Engineer is dynamic, challenging, and multifaceted. From managing CI/CD pipelines to automating infrastructure, responding to incidents, and improving system performance, the role is central to the efficient delivery of software and the reliability of IT operations.

DevOps Engineers serve as a crucial link between development, operations, and security teams, ensuring that software is developed, deployed, and maintained quickly, securely, and reliably. As organizations continue to adopt DevOps practices, the demand for skilled DevOps Engineers continues to grow, making this a vital and rewarding career path.

In modern software development, both DevOps (Development and Operations) and SRE (Site Reliability Engineering) are methodologies aimed at improving collaboration, efficiency, and reliability in IT operations. While they share some common goals, such as delivering software quickly and ensuring system reliability, their approaches differ in key areas. DevOps focuses on collaboration between development and operations teams, while SRE is a discipline developed by Google that focuses on applying engineering principles to operations.

In this post, we will explore the key differences between DevOps and SRE, outlining their major features, roles, and methodologies to help clarify their unique contributions to software delivery and operations.

1. Definition and Scope

DevOps:

• Cultural and Collaborative Approach:
  • DevOps is a cultural movement that brings together development (Dev) and operations (Ops) teams to enhance collaboration and communication throughout the software lifecycle.
• End-to-End Lifecycle:
  • DevOps spans the entire software development lifecycle (SDLC), including planning, coding, building, testing, deployment, and monitoring. It aims to automate and streamline these processes for faster delivery and higher-quality applications.
• Focus on Continuous Integration and Delivery (CI/CD):
  • DevOps emphasizes continuous integration (CI) and continuous delivery (CD) practices, automating the process of building, testing, and deploying applications to ensure frequent, reliable releases.

SRE:

• Engineering-Focused Approach:
  • Site Reliability Engineering (SRE) is a practice that applies software engineering principles to IT operations with the goal of creating scalable and highly reliable software systems.
• Focus on Reliability and Availability:
  • SRE’s primary focus is on ensuring that systems are reliable, available, and performant. It involves using automation, metrics, and monitoring to achieve reliability at scale.
• Service Level Objectives (SLOs):
  • SREs define and monitor service level objectives (SLOs), which are metrics that help ensure the reliability and performance of applications in production. These metrics are used to guide decision-making around resource allocation and system improvements.

2. Objectives and Goals

DevOps:

• Accelerate Software Delivery:
  • DevOps aims to speed up the delivery of software by improving collaboration between development and operations teams. The goal is to deliver software faster without compromising quality.
• Automation and Efficiency:
  • By automating manual tasks such as deployment, testing, and monitoring, DevOps increases the efficiency and consistency of development and operations processes.
• Enhanced Collaboration and Communication:
  • DevOps breaks down the silos between development, testing, and operations teams, fostering a culture of collaboration and shared responsibility for application quality and performance.

SRE:

• Focus on Reliability and Performance:
  • SRE prioritizes system reliability, ensuring that applications are always available and performing at the desired level. SREs set SLOs (Service Level Objectives) and use them to balance reliability with new feature development.
• Scalability and Efficiency:
  • SREs apply engineering best practices to ensure that systems can handle growth and scale efficiently while maintaining high reliability and performance.
• Incident Response and Management:
  • SRE teams are responsible for monitoring systems, detecting incidents, and responding to issues quickly. They aim to minimize downtime and prevent outages, ensuring that customers experience seamless service.

3. Automation and Tools

DevOps:

• CI/CD Tools:
  • DevOps relies heavily on CI/CD tools such as Jenkins, GitLab CI, Travis CI, and CircleCI to automate the build, test, and deployment pipelines.
• Infrastructure Automation:
  • DevOps uses tools like Terraform, Ansible, and AWS CloudFormation for automating infrastructure provisioning, configuration, and management. These tools allow infrastructure to be managed as code, enabling repeatability and scalability.
• Collaboration Tools:
  • DevOps also integrates collaboration tools such as Slack, Microsoft Teams, and Jira to ensure smooth communication and project management across teams.

SRE:

• Monitoring and Metrics Tools:
  • SRE heavily relies on monitoring tools like Prometheus, Grafana, Datadog, and New Relic to collect, visualize, and analyze performance data, ensuring systems are meeting SLOs and SLIs (Service Level Indicators).
• Incident Management and Alerting:
  • Tools like PagerDuty, Opsgenie, and VictorOps are used for incident management, enabling teams to quickly respond to alerts and minimize service disruptions.
• Automation for Reliability:
  • SREs use tools such as Kubernetes for orchestration and Google Cloud Operations to automate scaling, fault tolerance, and disaster recovery processes.

4. Roles and Responsibilities

DevOps:

• Collaboration Across Development and Operations:
  • In a DevOps environment, developers, operations engineers, QA teams, and security professionals collaborate across the entire software lifecycle.
• Shared Responsibility:
  • Developers are responsible for writing code and implementing features, while operations engineers handle infrastructure, deployments, and system reliability. In DevOps, everyone shares responsibility for application quality and performance.
• Focus on Speed and Quality:
  • DevOps teams focus on delivering software quickly while maintaining high-quality standards through automation and testing.

SRE:

• Reliability Engineering:
  • SREs primarily focus on engineering solutions to improve system reliability and performance. Their goal is to ensure that production systems remain up and running at all times and meet predefined SLOs.
• Incident Management:
  • SREs are responsible for identifying, diagnosing, and fixing issues in production environments. They work to minimize downtime and perform post-incident analysis to prevent recurrence.
• Optimizing System Efficiency:
  • SREs work closely with development teams to optimize infrastructure, ensure systems scale effectively, and continuously monitor performance for improvements.

5. Key Metrics and Measurement

DevOps:

• Deployment Frequency:
  • DevOps focuses on measuring deployment frequency to assess how quickly teams can release new features, bug fixes, and improvements to production.
• Lead Time:
  • Lead time measures how long it takes from writing code to successfully deploying it to production. DevOps seeks to reduce lead time to speed up development cycles.
• Mean Time to Recovery (MTTR):
  • MTTR is an important metric for DevOps teams, indicating how quickly a system can be restored after a failure.

SRE:

• Service Level Objectives (SLOs):
  • SRE teams define and track SLOs, which are metrics that set reliability targets (e.g., 99.9% uptime). These metrics help guide decision-making regarding system reliability.
• Error Budget:
  • SREs use error budgets to balance reliability and innovation. If the error budget is exceeded (i.e., reliability falls below the agreed-upon level), teams must focus on fixing reliability issues before adding new features.
• Mean Time Between Failures (MTBF):
  • MTBF measures the average time between system failures. SREs work to maximize MTBF by improving system reliability and reducing incidents.

6. Collaboration and Approach

DevOps:

• Cultural Transformation:
  • DevOps is not just about tools but also about cultural change within organizations. It requires breaking down silos between teams and fostering collaboration to ensure that all members contribute to both the development and operational success of software.
• Continuous Improvement:
  • DevOps promotes a continuous feedback loop where development teams are constantly improving based on feedback from operations teams, end users, and automated testing.
• Faster Software Releases:
  • DevOps aims to streamline software delivery, ensuring that applications are released faster and more frequently while maintaining quality.

SRE:

• Reliability Engineering:
  • SRE focuses on operational reliability and applies engineering practices to IT operations. The goal is to automate operational tasks and ensure systems remain highly available.
• Proactive Monitoring:
  • SREs use monitoring and logging tools to ensure proactive identification and resolution of potential issues before they affect users.
• Balancing Innovation with Reliability:
  • SRE ensures that innovation (e.g., new features) is balanced with reliability through the use of SLOs, error budgets, and proactive problem-solving.

DevOps vs SRE – Complementary Practices

While DevOps and SRE share a common goal of improving the efficiency, speed, and reliability of IT operations, they differ in focus and approach:

• DevOps is centered on collaboration and automation between development and operations teams to speed up software delivery.
• SRE applies engineering principles to ensure the reliability, availability, and performance of production systems.

Ultimately, DevOps and SRE are complementary practices. Organizations can benefit from integrating both methodologies: DevOps for faster software delivery and SRE for high reliability and operational excellence. By doing so, teams can ensure that their software is both delivered quickly and runs smoothly in production.

In the world of IT, DevOps and IT Operations (ITOps) are two critical concepts that shape the way organizations develop, deploy, and manage software applications. While both aim to enhance the efficiency and reliability of IT systems, they focus on different aspects of the software lifecycle and have distinct goals. Understanding the differences between these two approaches is crucial for organizations looking to optimize their IT infrastructure and development workflows.

In this post, we will explore the key differences between DevOps and ITOps, outline their major features, and highlight how they work together to support high-performing IT environments.

1. Definition and Scope

DevOps:

• Collaborative Culture:
  • DevOps is a set of practices and a cultural philosophy that aims to unify and automate the work of software development (Dev) and IT operations (Ops). It emphasizes collaboration, communication, and integration between traditionally separate teams.
• Focus on Continuous Delivery:
  • DevOps focuses on speeding up the software development and delivery process by automating continuous integration (CI), continuous delivery (CD), and continuous testing. The goal is to shorten the development lifecycle, deliver features faster, and maintain high software quality.
• End-to-End Involvement:
  • DevOps teams are involved throughout the software lifecycle, from planning and development to testing, deployment, and monitoring in production. This end-to-end involvement allows for faster feedback and iteration.

ITOps:

• Traditional IT Management:
  • ITOps focuses on managing and maintaining an organization’s IT infrastructure, including servers, networks, databases, and other hardware and software systems. It is primarily concerned with keeping IT systems running smoothly, secure, and reliable.
• Operational Excellence:
  • ITOps is focused on optimizing the performance, availability, and reliability of IT services. It deals with tasks like system monitoring, security patches, backups, and troubleshooting.
• Reactive and Proactive Management:
  • ITOps teams address issues both reactively (e.g., fixing problems as they arise) and proactively (e.g., anticipating and preventing potential issues). It’s about maintaining the health of the IT infrastructure on an ongoing basis.

2. Focus Areas and Objectives

DevOps:

• Accelerating Software Delivery:
  • The primary focus of DevOps is to accelerate the delivery of software applications by fostering collaboration between development and operations teams. By automating repetitive tasks and integrating CI/CD pipelines, DevOps enables faster software releases and higher-quality applications.
• Automation and Efficiency:
  • DevOps emphasizes automation in areas like infrastructure provisioning, configuration management, testing, and deployment. Automation increases efficiency, reduces human error, and accelerates the software delivery process.
• Collaboration and Communication:
  • DevOps is built around the idea of breaking down silos between development and operations teams. This collaboration ensures better communication, faster decision-making, and a smoother workflow throughout the development cycle.

ITOps:

• Infrastructure Management and Optimization:
  • ITOps is centered around the management of IT infrastructure. Its focus is on ensuring that systems, servers, networks, and databases operate efficiently and without disruption. The goal is to provide stable and secure infrastructure to support applications and users.
• Service Continuity and Availability:
  • A key goal of ITOps is ensuring that critical IT services remain available and secure. This involves monitoring system health, preventing outages, ensuring compliance with security policies, and handling capacity planning.
• Incident and Problem Management:
  • ITOps teams are responsible for responding to incidents (e.g., system failures or downtime) and ensuring the underlying infrastructure is running smoothly. They work to minimize downtime and optimize the system’s performance.

3. Automation and Tools

DevOps:

• CI/CD Tools:
  • DevOps heavily relies on CI/CD tools like Jenkins, GitLab, CircleCI, and Travis CI to automate the process of building, testing, and deploying applications. This automation speeds up the software release cycle and reduces the chances of human error.
• Infrastructure as Code (IaC):
  • Tools like Terraform, Ansible, and AWS CloudFormation are used to automate infrastructure management through code. This ensures consistency across environments and improves the scalability of applications and infrastructure.
• Monitoring and Logging:
  • DevOps teams use tools like Prometheus, Grafana, and the ELK Stack to monitor applications and infrastructure. This helps detect issues early, ensuring quick resolution and improving system performance.

ITOps:

• System Monitoring and Alerts:
  • ITOps uses traditional system monitoring tools like Nagios, Zabbix, and SolarWinds to track server health, network traffic, and overall infrastructure performance. These tools alert the team to any potential problems.
• Configuration Management:
  • ITOps teams use configuration management tools such as Puppet, Chef, and SaltStack to ensure consistency in server configurations, software installations, and security settings.
• Patch Management and Security:
  • ITOps relies on security and patch management tools, such as WSUS (Windows Server Update Services) or Red Hat Satellite, to ensure systems are up to date-and protected from vulnerabilities.

4. Collaboration and Responsibilities

DevOps:

• Cross-Functional Collaboration:
  • DevOps emphasizes a collaborative approach where development, testing, and operations teams work together throughout the software lifecycle. This ensures faster feedback, better code quality, and quicker resolution of issues.
• Shared Accountability:
  • In DevOps, teams are jointly responsible for both the success and failure of applications. Development teams are accountable for code quality and functionality, while operations teams focus on infrastructure reliability and performance.
• Continuous Feedback:
  • DevOps encourages continuous feedback loops, where performance data, user feedback, and incident reports are shared regularly across teams to improve the development process and user experience.

ITOps:

• Specialized Roles:
  • ITOps is typically more specialized, with teams focusing on specific aspects of IT infrastructure management, such as networking, server maintenance, security, and cloud management. These roles are separate from the development teams.
• Siloed Responsibilities:
  • In traditional ITOps environments, there may be a lack of collaboration between development and operations teams. However, some modern ITOps models are beginning to incorporate more cross-functional collaboration to align with DevOps practices.
• Incident Management:
  • ITOps is responsible for resolving incidents and minimizing downtime. Their focus is on maintaining system uptime, security, and troubleshooting issues in the infrastructure that might impact the application’s performance.

5. Impact on Development and Operations

DevOps:

• Faster Time-to-Market:
  • By automating workflows and improving collaboration, DevOps accelerates the software development process, allowing organizations to release software faster and more frequently.
• Improved Quality and Reliability:
  • Continuous testing, integration, and monitoring ensure that code is thoroughly tested before it reaches production, reducing the likelihood of bugs and performance issues in the final product.
• Scalable and Agile Development:
  • DevOps fosters a more agile environment, where developers can quickly iterate on new features, make changes, and scale applications based on feedback and demand.

ITOps:

• Operational Stability and Continuity:
  • ITOps focuses on maintaining the stability and continuity of IT services. It ensures that all infrastructure components are functioning properly and that services are available to meet business needs.
• Risk Mitigation:
  • ITOps teams play a key role in mitigating risks related to system downtime, security vulnerabilities, and compliance violations. They ensure that infrastructure is secure and compliant with industry standards.
• Cost Optimization:
  • ITOps teams often work to optimize resource utilization and manage costs related to IT infrastructure. They may oversee cloud cost management, server optimization, and capacity planning to ensure cost-effective operations.

DevOps vs ITOps – Complementary Practices

While DevOps and ITOps serve different roles in software development and IT management, they are complementary. DevOps emphasizes collaboration, automation, and the rapid delivery of software, while ITOps focuses on managing and maintaining the underlying IT infrastructure that supports those applications. Both are critical for creating efficient, high-performance IT environments.

Organizations that successfully integrate both DevOps and ITOps practices can benefit from faster software releases, stable and reliable infrastructure, and a collaborative work environment that drives continuous improvement across teams.

A DevOps platform is an integrated set of tools and services that facilitates the implementation of DevOps practices, such as continuous integration (CI), continuous delivery (CD), collaboration between development and operations teams, and automation of manual tasks. These platforms enable organizations to streamline and automate the software development lifecycle, enhancing speed, quality, and efficiency in delivering applications.

A DevOps platform typically includes features for source code management, building, testing, deploying, monitoring, and managing infrastructure. By using these platforms, organizations can reduce errors, improve system reliability, and accelerate product delivery. In this post, we will explore what makes a DevOps platform, its key components, and how these platforms are transforming software development.

1. Source Code Management and Version Control

Major Features:

• Centralized Code Repository:
  • A core feature of any DevOps platform is the inclusion of source code management tools like Git, SVN, or Mercurial. These tools allow teams to collaborate on code by storing it in a centralized repository, making it easy for developers to contribute, review, and track changes.
• Branching and Merging:
  • Version control systems support branching and merging workflows, which allow developers to work on features, fixes, and experiments independently. This makes it easy to merge changes back into the main codebase, ensuring that new features or fixes don’t disrupt the stability of the application.
• Collaboration and Code Review:
  • Built-in collaboration features like pull requests and merge requests facilitate peer reviews, ensuring that code meets quality standards before being merged into the main branch. This fosters collaboration and improves code quality.
• Tracking Changes and History:
  • Version control tools keep detailed records of code changes, allowing teams to revert to previous versions if necessary. This ensures that development can continue smoothly even if issues arise in new changes.

2. Continuous Integration and Continuous Delivery (CI/CD)

Major Features:

• Automated Build and Test Pipelines:
  • A DevOps platform automates the process of continuously integrating and testing code changes. CI tools like Jenkins, GitLab CI, and CircleCI allow teams to automatically trigger builds and tests whenever new code is pushed to the repository, ensuring early detection of issues.
• Automated Deployment:
  • CD tools like AWS CodePipeline, Travis CI, and GitHub Actions help automate the deployment process, allowing applications to be delivered to testing, staging, or production environments with minimal manual intervention. This speeds up the release cycle and improves efficiency.
• Pipeline as Code:
  • DevOps platforms allow teams to define their build and deployment pipelines as code, often using YAML or JSON. This approach ensures that the pipeline configuration is version-controlled, repeatable, and consistent across environments.
• Rollback Capabilities:
  • In case of deployment failures, CI/CD platforms offer rollback mechanisms, allowing teams to quickly revert to a stable version, minimizing downtime and ensuring system reliability.

3. Infrastructure Management and Automation

Major Features:

• Infrastructure as Code (IaC):
  • A key feature of DevOps platforms is the ability to automate infrastructure provisioning through IaC. Tools like Terraform, AWS CloudFormation, and Ansible allow infrastructure to be defined and managed using code, which is version-controlled and repeatable.
• Automated Provisioning and Scaling:
  • DevOps platforms can automatically provision cloud resources (e.g., virtual machines, databases) and scale them according to the needs of the application. Cloud-native platforms like Google Kubernetes Engine (GKE) and Amazon ECS offer container orchestration that helps with the scaling and management of applications.
• Environment Consistency:
  • IaC ensures that environments (dev, staging, production) are consistently configured. This reduces discrepancies between environments and helps prevent issues that arise when code behaves differently in different settings.
• Monitoring and Reporting:
  • DevOps platforms typically offer integrated monitoring tools to track the health and performance of infrastructure, alerting teams to any potential issues. These tools provide insights into resource utilization, uptime, and system performance, allowing for proactive management.

4. Collaboration and Communication

Major Features:

• Integrated Collaboration Tools:
  • DevOps platforms integrate collaboration tools like Slack, Microsoft Teams, and Jira for real-time communication between development, operations, and other teams. These tools enable better communication, faster problem-solving, and alignment on project goals.
• Task and Issue Tracking:
  • Platforms such as Jira, Trello, or GitHub Projects offer integrated task management and issue tracking, helping teams stay organized and prioritize work. This visibility into project progress improves transparency and accountability.
• Documentation and Knowledge Sharing:
  • Many DevOps platforms provide tools for documentation and knowledge sharing, such as Confluence and Wiki pages. This ensures that team members can access project information, best practices, and troubleshooting guides when needed.
• Centralized Feedback Loops:
  • DevOps platforms enable continuous feedback between developers, QA engineers, and operations teams. Automated testing, monitoring, and production logs provide valuable feedback that can drive improvements in the software development process.

5. Security Integration (DevSecOps)

Major Features:

• Automated Security Testing:
  • A DevOps platform integrates security practices into the CI/CD pipeline, ensuring that vulnerabilities are identified and addressed early in the development process. Tools like SonarQube, Snyk, and OWASP ZAP can automatically scan code for security flaws, reducing the risk of security breaches.
• Continuous Compliance Monitoring:
  • DevOps platforms often include tools for automating compliance checks to ensure that applications adhere to regulatory requirements such as GDPR, HIPAA, or PCI DSS. This ensures that security and compliance are integrated throughout the software lifecycle.
• Identity and Access Management (IAM):
  • Robust IAM features ensure that only authorized personnel can access sensitive environments and data. DevOps platforms typically offer integration with cloud IAM services (e.g., AWS IAM, Azure Active Directory) to enforce secure access controls.
• Encryption and Secrets Management:
  • DevOps platforms often include tools for managing sensitive data, such as HashiCorp Vault for secrets management. This ensures that sensitive credentials, API keys, and other secrets are securely handled and stored.

6. Monitoring and Observability

Major Features:

• Real-Time Monitoring:
  • DevOps platforms include monitoring tools to provide visibility into application performance and infrastructure health. Tools like Prometheus, Grafana, and New Relic allow teams to monitor system performance in real-time and detect potential issues before they affect users.
• Log Aggregation and Analysis:
  • DevOps platforms often integrate with centralized logging systems like the ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk, allowing teams to aggregate logs from multiple sources and gain insights into system behavior and user interactions.
• Alerting and Incident Management:
  • Integrated alerting systems notify teams when issues arise, enabling rapid incident response. DevOps platforms often integrate with incident management tools like PagerDuty or Opsgenie to streamline the process of addressing critical issues and reducing downtime.
• Performance Optimization:
  • Continuous monitoring provides valuable data that can be used for performance optimization. DevOps platforms allow teams to analyze system metrics and user behavior to identify bottlenecks, optimize resource usage, and improve the user experience.

The Power of DevOps Platforms

A DevOps platform is essential for organizations looking to accelerate their software delivery process while maintaining high standards of quality, security, and reliability. By integrating tools for source code management, continuous integration, infrastructure automation, collaboration, security, and monitoring, DevOps platforms provide a comprehensive solution for modern software development.

These platforms not only streamline the development lifecycle but also foster a culture of collaboration and continuous improvement. As businesses increasingly adopt DevOps practices, the use of integrated platforms will continue to be a critical enabler of innovation and operational efficiency.

THE PANDEMIC HAS compelled enterprises to rapidly move their critical workload to the cloud to ensure seamless functioning of business. As cloud is gaining momentum, and enterprises are frantically looking for ways to optimise their network, storage and agility, edge computing has turned out to be the perfect solution.

To understand where edge computing fits in the whole spectrum of IT infrastructure, we need to begin with the basics— Understanding what actually is edge computing. “Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, that is, at the “edge” of the system. This approach reduces the need to bounce data back and forth between the cloud and device while maintaining consistent performance. This reduces latency in data transmission and computation, thereby enhancing agility.

With regards to infrastructure, edge computing is a network of local micro data centers for storage and processing purposes. At the same time, the central data centre oversees the proceedings and gets valuable insights into the local data processing. However, we need to be mindful that edge computing is a kind of expansion of cloud computing architecture— an optimised solution for decentralised infrastructure.

The ultimate purpose of edge computing is to bring compute, storage, and network services closer to endpoints and end users to improve overall application performance. Based on this knowledge, IT architects must identify and document instances where edge computing can address existing network performance problems.

How does edge computing work?
In traditional enterprise computing, data is produced at a user’s computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. Results of that work are then conveyed back to the end-user. However, if we consider the number of devices that are connected to a company’s server, also the volume of data it generates, it is far too much for a traditional IT infrastructure to accommodate.
So, IT architects have shifted focus from the central data centre to the logical edge of the infrastructure—taking storage and computing resources from the data centre and moving those resources to the point where the data is generated.

There are several reasons for the growing adoption of edge computing:

Due to emerging technologies such as IoT and IoB, data is being generated in real-time. Devices enabled by these technologies require a high response time and considerable bandwidth for proper operation.
Cloud computing is centralised. Transmitting and processing massive quantities of raw data puts a significant load on the network’s bandwidth.
Incessant movement of large quantities of data back and forth is beyond reasonable cost-effectiveness and leads to latency.
Processing data at the source and then sending valuable data to the centre is a more efficient solution.
As organisations move back to remote working models, we will witness wider adoption of edge computing as it empowers remote work infrastructure with greater computation and storage capabilities.

Given the Covid-19 pandemic situation around the world, the adoption of cloud technology by different enterprises is visibly accelerating. The adoption rate is expected to continue growing for the foreseeable future. However, it has become evident that a horizontal cloud does not always meet the specific needs of certain industries, especially highly regulated ones such as banking or healthcare. This is where “Industry Cloud” comes in.

“Industry Cloud” explained
Techopedia defines Industry Cloud as a cloud system that has been heavily customized to suit the needs of a specific industry in order to accommodate the business, operatory, legal, regulatory, as well as security and other considerations. The main focus of Industry Cloud is vertical integration rather than horizontal one, which is the focus of general cloud computing.

There are industries that have been very resistant to the change that cloud computing has brought about because it does not provide the very specific needs that a vertical market organization such as a financial or medical institution needs. Such organizations need cloud infrastructure that is specifically tailored to their industries, and thus the term ‘Industry Cloud’ was coined.

An Industry Cloud focuses on specialised processes with tools and business services dedicated to a specific industry. Designed with industry challenges in mind, these clouds can enable organizations jump ahead and deliver value at record pace.

Advantages of Industry Cloud
The key advantage of Industry Cloud is that it addresses the problems of systemic risks that most organisations face with security, regulatory, and compliance. In addition, there are other advantages such as:

Standardized configurations
Improved operational efficiency
Deeper customer engagement
Focused spends
Customized holistic solutions
How does Industry Cloud compare to general cloud computing?
It is worth stating that an industrial cloud is not a new type of technology or a paradigm-shifting concept or solution. It is simply a specific method or way of using cloud computing to handle industrial processes and challenges.

The major difference can be found in how they are integrated into business operations. The Industry Cloud is designed to create more value when managing systems within the boundaries of the industry it is applied to. The general cloud employs a more horizontal approach and is built to function outside the boundaries of a specific industry. Another difference is that the Industry Cloud is equipped with specific industrial features to manage data and schedule tasks which means they can determine what data is to be sent to the cloud and what data is not. On the other hand, general cloud solutions will struggle to handle these specific data requirements.

Overall, the industry-specific cloud has an advantage over the general cloud as it understands data, standards and regulatory policies associated with a specific industry. This helps the organizations in scalability as it occurs within the constraints set by the policies of an industrial niche. Thus, the emergence of the industry-specific cloud is a natural progression of cloud computing to meet the varying and highly personalised industry requirements.

Wrapping up
The war between traditional on-premises data center infrastructure providers and public cloud providers is far from over. In fact, the cloud game has gone a step ahead wherein industries today require more than general-purpose cloud capabilities. They require industry-specific cloud capabilities. As cloud solutions continue to mature, major public cloud providers are investing in solutions to meet specialized industry needs. It’s still early days for industry clouds. Established companies in industries feeling the sting of competition from cloud-native disrupters are especially good prospects for these types of solutions.

Skills development and training experts Amazon Web Services (AWS) re/Start and IN4.0 Group are collaborating to build an inclusive and diverse global pipeline of new cloud talent by focusing on unemployed and underemployed individuals to help them launch a career in the cloud.

Following the success of the IN4.0 talent academy, a 12-week specialist digital skills training programme, and its three-year partnership with AWS, IN4.0 is working with AWS re/Start on programmes in Lancashire and Greater Manchester.

IN4.0 delivered two successful AWS re/Start cohorts earlier this year with 63 learners graduating from the programme in March 2021. Applications are open for the next cohort, which is due to start on Tuesday 4 May.

The collaboration supports learners in building career skills and helping them through the interview process to pursue entry-level roles that require AWS Cloud practitioner knowledge and skills.

IN4.0, operators of HOST, the Home of Skills & Technology, formerly known as The Landing in Media City UK, is working with several employers in the North West region who are already benefitting from ‘work-ready’ graduates to help undertake industrial projects and drive digital transformation within their businesses.

IN4.0 aims to support learners in gaining job-specific skills, connect learners with potential hiring employers, and support them as they embark on cloud careers.

According to the 2019 Global Knowledge IT Skills and Salary Report, nearly 80 per cent of IT decision-makers see hiring as a challenge. While LinkedIn data shows that cloud computing skills have ranked among the top hard skills that companies need the most for the past three years.

AWS re/Start is a 12-week, skills-based training programme that covers fundamental AWS cloud skills as well as practical career skills, such as interviewing and CV writing, to help prepare individuals for an entry-level cloud position. Through real-world scenario-based exercises, labs, and coursework, students build Linux, Python, networking, security, and relational database skills.

The programme also prepares and covers the cost for participants to take the AWS Certified Cloud Practitioner exam, validating their cloud skills with an industry-recognised credential. Ultimately, AWS re/Start can help prepare individuals for entry-level cloud roles in operations, site reliability and infrastructure support.

Mo Isap, CEO of IN4.0 Group said “After working closely with AWS for nearly three years within our talent academy, we are ecstatic to continue this partnership formally as an AWS re/Start collaborating organisation and AWS select consulting partner.

“Securing this prestigious status further extends IN4.0 Group’s commitment to delivering highly skilled technology career starts for individuals from diverse backgrounds, who previously would not have had this opportunity.”

Tejas Vashi, AWS re/Start Global Team Lead said: “The industry demand for cloud adoption is far outpacing the number of new, cloud-savvy workers, leaving organisations struggling to find and hire the talent needed to implement cloud services. To overcome this systematic supply vs demand situation, cloud talent must be cultivated from non-traditional sources. AWS re/Start brings “net-new” talent into the cloud ecosystem by skilling unemployed and underemployed individuals with little technology background and preparing them to launch a cloud career.”

On March 31, Korean telecommunications company KT Corporation announced that it will enter the B2B cloud computing market in Vietnam. Based on its achievements in the cloud business in Korea, KT signed an MoU with FPT Smart Cloud, an affiliate of FPT Group, a leader in Vietnam’s ICT scene.

KT is planning to carry out a project on hybrid cloud computing services optimised for industrial characteristics such as G-Cloud and F-Cloud can be offered in Vietnam.

At the middle of the month, Mat Bao Corporation, a Vietnamese technology company also launched its new cloud server cluster located at the data centre of Vietnam Posts and Telecommunications Group (VNPT). As planned, the server cluster will store information with high security and stability to ensure efficient data security for enterprises.

“With the rapid development and high demand from the market, the investment in upgrading services to bring advanced quality and outstanding products is a prerequisite to ensuring data security at Mat Bao,” Huynh Ngoc Duy, CEO of Mat Bao said.

Earlier, Amazon Web Services, Inc. (AWS), a subsidiary of Amazon.com officially announced AWS EdStart in Vietnam, an online support programme for educational technology startups to develop teaching solutions on the AWS cloud platform.

The AWS EdStart programme provides EdTech startups with resources to quickly get started on AWS – including AWS Promotional Credits and access to training and support. AWS EdStart is a global community of like-minded people and companies that are solving complex educational problems.

At the present, the cloud computing market in Vietnam is led by both domestic and foreign suppliers. The key players in the Vietnamese cloud services market are FPT Corporation, Viettel-CHT, CMC Corporation, IBM Vietnam, Microsoft Vietnam, Amazon Web Services Vietnam, Mat Bao Corporation, NTC Cloud Computing, SAP Asia (Vietnam), HPT Vietnam, Google LLC, and Salesforce.

In 2020, the Vietnamese cloud computing market currently reaches about $133 million, equivalent to VND3.2 trillion ($139.13 million). However, domestic enterprises only account for about 20 per cent of the market while foreign partners hold the remainder.

Bui Quoc Anh, director at Data Centre – Vietnam National University Ho Chi Minh City stated that the international providers, such as AWS, and Google Cloud have a lot of platforms. It is too difficult for domestic providers to build such platforms because of cost, time, and expertise level.

At the moment, the public sector and big enterprises in Vietnam use domestic cloud computing services but mainly infrastructure services.

However, the international players such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, or IBM Cloud do not officially operate in Vietnam because Vietnam’s Network Security Law does not allow, because of data centre locations, although many people and companies use their services.

He noted that the big barriers for international players are Vietnam’s Network Security Law. They cannot provide their services in Vietnam including the public sector, state-owned organisations and big enterprises that work with the public sector. It is challenging for them to open data centres in Vietnam if they do not have enough customers in Vietnam. Maybe, when international companies work in Vietnam, the situation may be changed.

For domestic providers, the big challenges are capital-intensive platforms as well as time and expertise for development. It is difficult for domestic providers to develop fast in a cost-efficient manner for competitiveness. Domestic providers need to find an exclusive or niche segment or a more innovative market for survival and development.

The COVID-19 pandemic has made the digital transformation trend stronger than ever. A survey of IBM Institute for Business Value showed that 56 per cent of Vietnamese enterprises are already using cloud management platforms. Responses indicate that Vietnamese enterprises plan to increase expenditure on hybrid cloud services from the current 41 to 43 per cent by 2023.

In fact, figures from the Ministry of Information and Communications showed that the market has been growing at 30 per cent on average in recent years while the growth rate reached 40 per cent in 2020, due to the COVID-19 pandemic.

Commenting on this trend, Le Anh Vu, creative director of CMC Telecom, stated that in 2021, the cloud computing market will continue to be more active with the strong entrance of small- and medium-sized enterprises, even business households. Also, the push for digital transformation from the state and the growth of domestic clouds such as CMC Cloud, VNPT Cloud, and VNG Cloud will be the premise to speed up the process of cloud computing.

Verizon Business and Amazon Web Services said Monday they are expanding their 5G collaboration to bring private mobile edge computing (MEC) to U.S. enterprise customers.

The solution will deliver a completely dedicated edge compute infrastructure on-premise that enables ultra-low latency, higher levels of security and deeper customization that fully integrates Verizon’s Private 5G networks and Private Edge platform with AWS Outposts, according to the announcement.

This allows Verizon enterprise customers to use the private MEC to deploy real-time enterprise applications like intelligent logistics, robotics, predictive maintenance, factory automation and more to help lower costs and improve efficiency.

Corning Inc., a materials science and advanced manufacturing company, will be the first company to leverage the solution, according to the announcement.

The solution will have use cases in factories, warehouses, and large business campuses and it supports unified connectivity, compute and storage without having the customer having to own the networking and IT infrastructure.

According to Verizon, the MEC solution will support a wide range of industrial applications, like autonomous mobile robots, predictive maintenance, quality assurance and near real-time monitoring and hazard alerts.

“Verizon’s Private 5G Edge platform will give customers a reliable, secure, high bandwidth, low latency connection to AWS services, APIs, and tools running on AWS Outposts,” the Verizon announcement said. “Additionally, two smaller AWS Outposts — 1U and 2U form factors — will give customers options to deploy AWS on-premises in space-constrained locations.”

The edge computing collaboration between the two companies began with the Verizon 5G Edge with AWS Wavelength, which was launched in August. The companies have announced the general availability of 5G mobile edge computing via Wavelength Zones in 10 cities across the U.S.

Private MEC is a natural expansion on the collaboration between the two companies, said Dave Brown, vice president of Elastic Compute Cloud at AWS, in a statement.

“Customers are already leveraging AWS Wavelength’s ability to provide ultra-low latency access to end users for use cases like video distribution, inference at the edge, AR/VR, and connected vehicles,” Brown said. “Now, Private 5G MEC from AWS and Verizon brings ultra-low latency to dedicated, closed, on-premises environments for use cases such as autonomous mobile robots, quality assurance, and hazard alerting.”

It’s been 12 months since lockdown became our reality. Every aspect of our lives has been impacted. Almost every educational institution has faced unprecedented disruption as the pandemic pushed them online much earlier than planned.

As a result, the edtech sector grew by 72% last year, driven largely by cloud computing. At its core, cloud computing refers to running workloads remotely using fully managed computing resources over the internet. These resources primarily consist of computing power and data storage which can be used to run any kind of computer system.

While not traditionally associated with the education sector, cloud computing has helped keep the education market going throughout the pandemic.

1. It opened up education material to the masses
One of the biggest challenges institutions have faced has been making education materials available to students. With libraries closed and no face-to-face teaching since March last year, many students have struggled to access the resources they need to complete their studies.

As a result, the market for digital textbooks has grown significantly. Many universities have partnered with online services such as Perlego to provide their students with access to digital textbooks. Rather than trek to the library or seek out illegal versions, students can now access over 500,000 academic, professional and non-fiction eBooks in one place.

2. It provides students with additional support
There has been a real drive for technology that supports students and staff; a common example being chatbots which are integrated into existing learning management systems (LMS) to assist students and free up teachers’ time. The capacity of this cloud technology allows universities to store information about student preferences and provide customisable solutions to their problems, delivering a bespoke learning experience for each individual. However, we are still at the very early stages of this technology, and there is so much more that can be done for the benefit of learning communities.

3. It saves universities thousands of pounds
Thanks to Infrastructure-as-a-Service (IaaS) cloud computing, organisations are no longer having to buy and maintain their own IT infrastructure. Everything can be hosted within the cloud, making it cost efficient, on-demand, secure, fast, reliable and scalable. This is why most organisations, including educational institutions, have been slowly migrating their on-premise infrastructure to the cloud in recent years. There is a large cost implication with buying and maintaining physical infrastructures, and due to the pandemic, many universities unfortunately experienced massive budget cuts, accelerating their move towards cloud-based technology.

4. It allows people to work at their own pace
With the removal of in-person teaching, many students have suddenly been left to create their own work schedule as they can no longer go to campus for lectures. Cloud computing makes it possible for students to access the most up-to-date version of assignments and receive updates when changes have been made. Students are able to continue to access the best material they need to complete their studies and learn at their own pace.

5. It’s here to stay
The pandemic has pushed educational institutions online earlier than planned, but many of the technologies will remain in place when the pandemic subsides. As students become accustomed to the newly adopted way of learning, educational institutions will want to keep them in place – especially if they are found to be useful and contribute to a better student experience. While students will eventually return to campus and resume in-person teaching, many universities may take the opportunity to offer more online courses and expand their remote student base. Geography will no longer be as big of a barrier to international students seeking higher education from institutions in different countries.

6. It enables collaboration
Cloud technology is enabling students to collaborate with each other more easily online. From video conferencing tools to document sharing, note sharing and collaborative revision tools, these sorts of technologies are being adopted by universities and, in many cases, directly by students themselves. It has been important for universities to be proactive about this shift towards the digital in order to help their students succeed in the best way possible online. Many of these tools are going to completely change the way in which students learn and collaborate with each other for good.

Navigating this new landscape of possible cloud technologies that can facilitate learning to meet the short-term needs brought on by the pandemic, as well as putting in place solutions that will work for the long-term, is a challenge that all universities face – but one that’s worth pursuing.

Edge infrastructure can integrate with central cloud data centers to deliver performance levels that wouldn’t be possible using the cloud alone.

You’ve heard of cloud computing. You’ve probably also heard of edge computing. But do you understand the differences between the cloud and the edge?

That’s a simple question with a relatively complex answer since both cloud and edge architectures can come in various forms. Let’s try to break down the similarities and differences between cloud computing and edge computing and explain how they relate to each other.

What is the cloud?
Cloud computing, of course, is an architecture in which data or processing are offloaded to servers hosted in a remote data center.

Clouds come in many forms – public, private and hybrid – and there are a wide variety of cloud services available, ranging from basic virtual machines and storage to serverless functions, IoT device management, and even satellite communication controls.

What is the edge computing?
In an edge architecture, data or processing are offloaded to remote infrastructure that is not necessarily a traditional data center. That work is done “closer” to end-users – in terms of geographic and network proximity –than a conventional data center.

One of the factors that makes edge complicated is that edge infrastructures can take a variety of forms. Edge infrastructure could consist of traditional data centers that are simply closer to users than a centralized cloud data center. But edge infrastructure could also be a network of IoT sensors that stores and processes data. It could even be end-user devices themselves, like smartphones, onto which data storage and processing are directly outsourced.

Cloud vs. edge
The varying forms of edge infrastructure make it difficult in some respects to distinguish cloud architectures from edge architectures. For instance, some hybrid clouds are arguably also edge architectures if they mix cloud data centers with local data centers in a way that brings some workloads closer to the network edge. A multi-region public cloud could also be considered a form of edge architecture if it includes regions that place workloads closer to the network edge than they would be in a single-region environment.

That said, the general distinction between edge computing and cloud computing is that in an edge architecture, workloads are close enough to end-users to deliver latency rates that are measured in single- or double-digit milliseconds. Many applications now require such low latency. For example, self-driving and autonomous vehicles do not have the time to send a video stream to a data center for image analysis to decide whether a pedestrian is in the car’s path? The data analysis, decision-making, and action must be done on the vehicle, which is essentially a complex edge device.

A conventional cloud environment is not usually capable of that level of performance. For example, latency between different AWS regions is typically measured in the hundreds of milliseconds (and that is inter-region, intra-cloud latency, which is likely to be lower in many cases than latency between the cloud and an end-user).

Edge computing as an extension of cloud
It’s worth noting, too, that edge computing is not an alternative to cloud computing as much as it is an extension of it. Edge infrastructure can integrate with central cloud data centers to deliver performance levels that wouldn’t be possible using the cloud alone.

Viewed from this perspective, the somewhat hazy relationship between edge and cloud comes into sharper focus. Don’t worrying about what counts as an edge architecture and what counts as the cloud in contexts where the distinctions are ambiguous. Think of edge and cloud as complementary setups that, when combined, deliver an end-user experience that adds up to more than the sum of its parts.

Most enterprises are still migrating workloads from data centers to the cloud. Although saving money is generally not the driving force for such migrations, it doesn’t hurt if money is saved along the way for better scaling or reliability, or an end to IT capital expenses.

The financial benefits of cloud computing, though not as inevitable as years of hype made folks expect, are real. The following are the top five strategies CIOs can use to reap those cost benefits.

1. Avoid lift and shift
It gets easier to shift a workload or a set of integrated workloads into a cloud environment with minimal modification. If a cloud migration is motivated by a looming data center lease expiration, a lift and shift of virtual machines (VMs) and data sets may be the only way to meet the deadlines. However, it will probably come with a cost increase for providing the shifted services in the near term.

Systems that were not designed to run in the cloud usually don’t take advantage of the features of cloud that hold the greatest potential for savings: the ability to scale infrastructure dynamically both up and down in response to load, and the ability to scale horizontally by spinning up new VMs or containers. Between them, these features allow IT to pay for only as much infrastructure as the workload needs, when it needs it. In-house resources must always be present to handle the peak load of the system and shifting a workload to the cloud unchanged usually means it is overprovisioned most of the time. As a result, enterprises wind up paying more to run in the cloud.

Nemertes Research found that IT shops on average spend about 12% more to run a workload in the cloud than in their own data center.

Therefore, IT should be seeking to refactor workloads to be more cloud-friendly wherever possible or seeking to replace pre-cloud architectures with cloud-native ones built on the premise of just-in-time infrastructure. In Nemertes’ research on cloud operations, the organizations best at this were, on average, running workloads for just 40% of what they had spent on premises.

To facilitate a cloud migration, IT should have in place a consistent workload placement process (WPP) to follow in evaluating workloads for migration. The WPP should take into account the architecture of the workload, prioritizing cloud-friendly systems over cloud-hostile ones, and weigh numerous other factors such as the risk associated with hosting in cloud versus on premises (it is often lower risk to provide service from the cloud now), the performance requirements of the workload and so on.

2. Utilize commitment and tiering
Too many cloud teams fail to take full advantage of committed instance pricing, and therefore are unable to get discounted rates on their resources. They do so out of an overabundance of either caution or optimism — not wanting to make a long-term commitment on a system that might be refactored or replaced in the near term.

CIOs should set up a process for sequencing such changes and utilize as much long-term pricing as they can based on the schedule that falls out of it. Any slight overcommitment will be more than balanced by savings.

3. Pay attention to data gravity
Surprise expenses often come in the form of data egress charges — the cost of moving data out of a cloud environment into another or back on premises. The WPP must include consideration of data affinities and should be used to group together systems that produce heavy traffic among themselves.

4. Use PaaS
Nemertes’ research on cloud operations found that organizations can save on average 15% to 20% on staff costs by shifting to database as a service or application server environments consumed as PaaS.

5. Empty racks, rooms, buildings
One of the primary goals should be supporting infrastructure in data centers and to realize savings there as fast as possible. Being able to empty racks entirely means also being able to reduce cooling and UPS service.

Ultimately, the important takeaway is that financial benefits of cloud computing are possible but not inevitable. All CIOs, even if they are pursuing cloud for reasons other than cost savings, should factor the possibility of savings into their decisions and planning, aiming to realize them when it doesn’t prevent them achieving their strategic goals for cloud.

AWS today closed out its first re:Invent keynote with a focus on edge computing. The company launched two smaller appliances for its Outposts service, which originally brought AWS as a managed service and appliance right into its customers’ existing data centers in the form of a large rack. Now, the company is launching these smaller versions so that its users can also deploy them in their stores or office locations. These appliances are fully managed by AWS and offer 64 cores of compute, 128GB of memory and 4TB of local NVMe storage.

In addition, the company expanded its set of Local Zones, which are basically small extensions of existing AWS regions that are more expensive to use but offer low-latency access in metro areas. This service launched in Los Angeles in 2019; starting today it’s also available in preview in Boston, Houston and Miami. Soon, it’ll expand to Atlanta, Chicago, Dallas, Denver, Kansas City, Las Vegas, Minneapolis, New York, Philadelphia, Phoenix, Portland and Seattle. Google, it’s worth noting, is doing something similar with its Mobile Edge Cloud.

The general idea here — and that’s not dissimilar from what Google, Microsoft and others are now doing — is to bring AWS to the edge and to do so in a variety of form factors.

As AWS CEO Andy Jassy rightly noted, AWS always believed that the vast majority of companies, “in the fullness of time” (Jassy’s favorite phrase from this keynote), would move to the cloud. Because of this, AWS focused on cloud services over hybrid capabilities early on. He argues that AWS watched others try and fail in building their hybrid offerings, in large part because what customers really wanted was to use the same control plane on all edge nodes and in the cloud. None of the existing solutions from other vendors, Jassy argues, got any traction (though AWS competitors would surely deny this) because of this.

The first result of that was VMware Cloud on AWS, which allowed customers to use the same VMware software and tools on AWS they were already familiar with. But at the end of the day, that was really about moving on-premises services to the cloud.

With Outposts, AWS launched a fully managed edge solution that can run AWS infrastructure in its customers’ data centers. It’s been an interesting journey for AWS, but the fact that the company closed out its keynote with this focus on hybrid — no matter how it wants to define it — shows that it now understands that there is clearly a need for this kind of service. The AWS way is to extend AWS into the edge — and I think most of its competitors will agree with that. Microsoft tried this early on with Azure Stack and really didn’t get a lot of traction, as far as I’m aware, but it has since retooled its efforts around Azure Arc. Google, meanwhile, is betting big on Anthos.

“Companies from all sectors of the German economy are using the AWS cloud to promote innovation, substantially cut costs, securely use their data, and at the same time support their growth in Germany, Europe, and worldwide,” said Klaus Bürg, General Manager for Amazon Web Services in the DACH region, at the opening ceremony. “The new AWS office in Munich with the Digital Builders Showroom, the Industrial Software Showcase, and the Event Area is another part of our ongoing investment to further promote cloud initiatives in Germany.”

What the Munich Office Offers

The Munich office comprises three distinct areas, each targeting a different aspect of cloud adoption: a hands-on innovation showroom, an industrial manufacturing showcase, and a community event space. Together they are intended to make cloud and emerging technologies more accessible and tangible for German organisations.

Digital Builders Showroom

Developed in partnership with Boston Consulting Group (BCG) and Siemens, the Digital Builders Showroom gives organisations of any size the opportunity to learn about and experiment with AWS cloud technologies. The showroom is structured around a series of interactive stations where visitors can see real AWS-based applications in operation, including Smart Factory and Smart City solutions.

Consultants with both technical and management backgrounds are on hand to demonstrate how technologies including Artificial Intelligence (AI), Deep Learning, Computer Vision, Edge Computing, and Natural Language Processing (NLP) can drive business growth and innovation.

“In collaboration with AWS, we will bring digital transformation to life for German companies of all sizes in the Digital Builders Showroom,” said Matthias Tauber, CEO of Boston Consulting Group Germany. “We want to make digital transformation more tangible. To achieve this, our consultants will work closely with the experts at AWS. We will help organizations of all sizes better understand disruptive technologies such as AI, Computer Vision, Edge Computing, and Internet of Things (IoT), and help promote innovation in all German companies.”

Industrial Software Showcase

Built in partnership with Siemens Digital Industries Software, the Industrial Software Showcase focuses specifically on manufacturing. It demonstrates how cloud technologies can improve every step of the manufacturing lifecycle — from initial design through production to ongoing inspection and maintenance. Visitors can gain hands-on experience by testing advanced manufacturing applications themselves.

“Manufacturing companies are undergoing rapid change. New analytical methods, machine learning, IoT, and other technologies that are changing basic operating procedures are all contributing to this change,” said Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software. “AWS is a pioneer and market leader in cloud computing. Working closely together, we want to help industrial companies make the cultural and technological changes necessary to succeed in these disruptive times.”

Event Space and Community Hub

Beyond the showrooms, the Munich office includes meeting rooms, a large auditorium, and co-working areas where customers and AWS Partner Network (APN) partners can collaborate with AWS business and technical specialists. Regular training and certification courses are also held in dedicated public classrooms, giving developers and technical professionals structured pathways to expand their cloud knowledge.

The AWS Loft programme — a series of events designed to help startups build and scale on AWS — will also run out of this space. The first AWS Loft at the Munich office was scheduled to open for two weeks starting 4 November 2019.

AWS Hiring and Expansion Across Germany

The Munich office opening is part of a broader AWS expansion across Germany. At the time of the announcement, AWS stated plans to hire more than 500 people during 2019 across its data centres, development centres, and offices in Berlin, Cologne, Dresden, Frankfurt, Hamburg, Munich, and Walldorf. Amazon also announced plans to create a total of 2,800 new jobs in Germany by the end of 2019.

Original reporting sourced from innovationorigins.com. Opening ceremony quotes and figures are attributed to statements made at the event.

The designation certifies 15 channel players as experts in addressing customers’ security and compliance issues.

Amazon Web Services is helping end users handle the ever-harder security and compliance requirements tied to endpoints, applications and data, especially as more people work outside of a physical office, act as contractors and as organizations pursue digital transformation.

To address the challenges, the cloud giant has certified the first channel partners, 15 total, throughout the world as experts in the new AWS End User Computing Competency. In short, the associated solutions allow end users to access company resources from anywhere.

The partners “can support your company’s need to provision, protect and get intelligence from endpoint devices, end-user apps and data on AWS,” Renata Melnyk, senior partner program manager at AWS, wrote in a blog.

Earning the AWS Competency designation was not easy. The 15 partners underwent “rigorous validation,” Melnyk wrote, as well as assessments of the security, performance and reliability of their AWS offerings and practices. Validation included:

• A minimum of four unique, in-depth AWS case studies specific to completed End User Computing technology projects.
• Demonstrated domain expertise in End User Computing technologies and workloads, complete with “innovative solutions” running on AWS.
• At least 12 months’ worth of blog posts, videos, press articles, and so on, available to the public.
• Documented access management strategy, including but not limited to: IAM users, symmetric access keys, asymmetric X.509 certificates, console passwords, and hardware or virtual multifactor authentication devices.
• Evidence of stringent data and security protections.
• Demonstrations of capabilities, processes, customer examples.

All that rigor proves the certified partners can enable a range of benefits. Alscient, a U.K.-based integrator, positions its AWS End User Computing Competency as facilitating a standardized desktop build that “can be securely provisioned in minutes, reducing corporate risk to common web threats.”

From there, a cloud-hosted platform hosts user management, while secure desktop access comes with options to restrict accesses as needed; for example, by network IPs and Active Directory domains, the company explains on its website.

New York-based Innovation Solutions also holds the AWS End User Computing Competency designation.

“Our singular focus is to enable every one of our customers to become a technology company,” said Justin Copie, Innovative Solutions CEO said in a press release. “Our hope is to enable growth, not only within our own business, but across technology companies in the region. If we can help raise the tide, then all boats rise. Together we win.”

Olikka, an Australian systems integrator, agreed.

“This latest achievement further demonstrates AWS’ commitment to their partners and, most importantly, customers,” said Ross Gangemi, solutions director for Olikka.

In specific, the 15 partners with the End User Computing Competency help customers with strategy, professional services, infrastructure management, repeatable intellectual property, and optimization of end-user computing technologies on AWS, Melnyk noted.

“When you deploy End User Computing solutions on AWS, you can maintain security over your data, improve user productivity, simplify IT operations and enable builders to be more innovative,” she wrote. “End User Computing services on AWS include Amazon WorkSpaces, Amazon AppStream 2.0, Amazon WorkDocs and Amazon WorkLink.”

She went on to describe the offerings as those that provide a resource brokering mechanism (control plane), visualization/remote display protocol, and an application execution environment, with either the control plane and/or application execution environment running on AWS. Along the way, the certified partners are tasked with adhering to AWS best practices for building cloud infrastructure.

The new AWS End User Computing Competency Advanced and Premier Consulting Partners are: Alscient Limited, API Systems, BlazeClan Technologies, CloudHesive, CMD Solutions, Consegna, EagleDream, Innovative Solutions, Nuvens, Olikka, Privo, Rebura, RedNight, Serverworks and SynchroNet.

What Is Ballista?

Ballista is a distributed computing platform introduced by software engineer Andy Grove. It is written in Rust and built on top of Kubernetes, using a Rust implementation of Apache Arrow as its in-memory data model. The project was positioned as an attempt to bring Apache Spark-style distributed query execution to the Rust ecosystem.

Background: From DataFusion to Ballista

Ballista grew out of Grove’s earlier work on the DataFusion project — an in-memory query engine built on Apache Arrow. Grove started DataFusion approximately eighteen months before the Ballista announcement, with the original goal of creating a full distributed computing platform in Rust capable of competing with Apache Spark. That goal proved overly ambitious at the time.

“Unsurprisingly, this turned out to be an overly ambitious goal at the time and I fell short of achieving that. However, some very good things came out of this effort. We now have a Rust implementation of Apache Arrow with a growing community of committers, and DataFusion was donated to the Apache Arrow project as an in-memory query execution engine and is now starting to see some early adoption.”

— Andy Grove

After stepping back from DataFusion and Arrow to focus on other work, Grove returned with a new proof-of-concept: Ballista. This second attempt benefited directly from the groundwork laid by DataFusion and the Rust Arrow implementation.

How Ballista Works

A Ballista cluster is composed of individual pods running inside a Kubernetes cluster. Applications are deployed using the Ballista CLI, which handles communication with Kubernetes. Service discovery within the cluster is handled natively via Kubernetes service discovery mechanisms.

At the time of announcement, Ballista did not yet include a distributed query planner. This meant that application developers were responsible for manually constructing the query plans to be distributed and executed across cluster nodes.

Roadmap for v1.0.0

Grove outlined the following milestones on the Ballista roadmap for the v1.0.0 release:

• Distributed query planner: Automate the construction and distribution of query plans across the cluster.
• Full DataFusion plan and expression support: Cover all DataFusion logical plans and expressions within the distributed execution layer.
• User-defined code execution: Allow custom user code to run as part of a distributed query.
• Interactive SQL over gRPC: Enable interactive SQL queries to be issued against a live cluster using gRPC.
• Arrow Flight protocol and Java bindings: Add support for the Arrow Flight wire protocol and expose Java language bindings.

Impact on the Apache Arrow Ecosystem

Even in its early proof-of-concept stage, Ballista had already produced tangible contributions to the broader Apache Arrow project. Development work on Ballista resulted in three DataFusion pull requests being merged into the Apache Arrow codebase, helping to shape the requirements and direction of the DataFusion query engine.

Why It Matters for DevOps and Data Engineering Teams

Ballista represents an important experiment in bringing memory-safe, high-performance distributed data processing to cloud-native infrastructure. By running natively on Kubernetes and leveraging Rust’s performance characteristics alongside Apache Arrow’s columnar memory format, the project aimed to offer a modern alternative to JVM-based frameworks like Apache Spark — with lower overhead and tighter integration with container orchestration.

For teams already running Kubernetes workloads and looking to reduce JVM dependency or improve resource efficiency in data processing pipelines, the direction Ballista pointed toward remains relevant — even if the project has since evolved significantly within the Apache Arrow DataFusion ecosystem.

Note: This article was written based on the original 2019 announcement. The Ballista and DataFusion projects have continued to develop under the Apache Arrow umbrella. Readers should consult the Apache Arrow project website for current status and documentation.

That’s changing. As more industries deepen their cloud adoption, both hyperscalers and specialist vendors have developed vertical cloud solutions — tooling, compliance frameworks, and pre-built applications tailored specifically to the needs of particular sectors. The result is a cloud ecosystem that now serves not just IT teams, but domain experts in healthcare, finance, education, automotive, and manufacturing.

Below is a look at how five industries are integrating cloud computing into their operations — and what makes their use cases distinct.

1. Healthcare

Healthcare is one of the most demanding environments for any technology platform. Patient data is among the most sensitive information in existence, and providers operate under strict regulatory obligations — most notably HIPAA in the United States. Major cloud providers address this through dedicated compliance programmes: HIPAA-eligible services, Business Associate Agreements (BAAs), audit logging, and encryption at rest and in transit.

Beyond secure storage for electronic health records (EHRs), cloud platforms are enabling more sophisticated clinical workflows:

• Machine learning-assisted diagnostics: Cloud-hosted ML models can analyse medical imaging (X-rays, MRIs, CT scans) to flag anomalies and support radiologists in reaching diagnoses faster.
• Predictive analytics: Aggregated patient data can be used to identify high-risk patients, predict readmissions, and recommend treatment pathways.
• Telehealth infrastructure: Scalable video conferencing and secure messaging platforms run on cloud infrastructure, enabling remote consultations at scale.

The shift here is from cloud-as-storage to cloud-as-a-clinical-tool — with the compute power to run workloads that simply weren’t feasible on on-premises hardware.

2. Finance

Financial services firms benefit from many of the same general cloud advantages as any other enterprise — reduced capital expenditure on server infrastructure, elastic scaling, and pay-as-you-go pricing. But the finance vertical also benefits from increasingly specialised cloud capabilities.

• SaaS for back-office operations: Banks, insurance companies, and asset managers have adopted SaaS platforms for accounting, compliance reporting, claims processing, and customer relationship management.
• AI and machine learning for capital markets: Quantitative trading desks and investment management firms use cloud-powered compute clusters to run complex financial models, backtest strategies, and process large volumes of market data in near real time.
• Regulatory compliance: Financial regulators in many jurisdictions have issued cloud guidance, and providers have built tools — such as immutable audit logs, data residency controls, and fine-grained IAM policies — to help firms meet their obligations.

Risk management and fraud detection are also increasingly cloud-native, with streaming data pipelines processing millions of transactions per second to identify anomalous patterns.

3. Education

Educational institutions — from K-12 schools to large universities — face a distinctive challenge: they must serve a highly varied user base (students, faculty, administrators, and IT staff) while managing tight budgets and complex data privacy requirements such as FERPA in the US.

Cloud adoption in education typically spans several layers:

• Collaborative platforms: Tools like Google Workspace for Education and Microsoft 365 Education provide shared document editing, video conferencing, and assignment submission — all running on cloud infrastructure and accessible from any device.
• Learning Management Systems (LMS): Cloud-hosted LMS platforms allow institutions to scale access to course materials for hundreds or thousands of concurrent students without managing their own servers.
• Student data management: Cloud storage with role-based access controls and compliance certifications helps IT departments protect sensitive student records while making them accessible to authorised staff.
• Scalability: Cloud infrastructure handles the demand spikes common in education — enrolment periods, exam season, or unexpected surges like pandemic-driven remote learning — far more cost-effectively than fixed on-premises capacity.

4. Automotive

The automotive sector involves complex, multi-tier supply chains spanning raw material suppliers, component manufacturers, assembly plants, dealers, and end customers. Coordinating across these tiers in real time is a significant data and integration challenge — one that cloud platforms are well-positioned to address.

• Supply chain visibility: Shared cloud environments allow suppliers and manufacturers to exchange real-time inventory data, reducing delays caused by parts shortages and improving demand forecasting.
• Connected vehicle data: Modern vehicles generate large volumes of telemetry — location, engine diagnostics, driver behaviour, and sensor readings. Cloud data pipelines and analytics platforms ingest and process this data to support predictive maintenance, over-the-air (OTA) software updates, and fleet management.
• Design and simulation: Cloud HPC (high-performance computing) resources allow automotive engineers to run crash simulations and aerodynamic modelling without investing in dedicated on-premises supercomputing clusters.

As the industry moves toward electric and autonomous vehicles, the volume of data generated on and off the road will grow substantially — making cloud infrastructure an architectural necessity, not just a convenience.

5. Manufacturing

Manufacturing has historically been dominated by on-premises, often legacy systems. Cloud adoption is accelerating here, driven by the need to modernise operations, improve visibility across the production floor, and compete on speed and flexibility.

• Cloud ERP: Enterprise resource planning systems — which coordinate procurement, production planning, inventory, and financials — are migrating to cloud-hosted SaaS platforms. This reduces the operational burden of patching and maintaining on-premises ERP software.
• Order fulfilment: Cloud-based order management systems allow manufacturers to route orders of any size through consistent, automated workflows, reducing fulfilment time and error rates.
• IoT and predictive maintenance: Industrial IoT sensors on machinery feed data to cloud platforms that can detect early signs of equipment failure — reducing unplanned downtime through predictive rather than reactive maintenance.
• Digital twins: Some manufacturers use cloud compute to run digital twins — virtual models of physical production processes — to optimise throughput and test process changes before implementing them on the factory floor.

The Shift Toward Vertical Cloud

What these five industries share is a common trajectory: initial cloud adoption for general infrastructure benefits (cost savings, scalability, disaster recovery), followed by deeper integration of industry-specific tooling as cloud providers and ISVs have matured their vertical offerings.

For DevOps and platform engineering teams working within these sectors, the practical implication is clear: the cloud platforms you build on are increasingly aware of your industry’s regulatory requirements, data patterns, and integration needs. Choosing the right managed services, compliance frameworks, and partner ecosystem — rather than building everything from scratch — is how modern engineering teams deliver value faster in regulated and complex environments.

Originally sourced from solutionsreview.com (2019). Updated and expanded for a technical audience.

Why DevOps Matters More Than Ever in the Cloud Era

As software-as-a-service (SaaS) became the dominant delivery model for enterprise computing, the pressure on development teams to ship faster and more reliably grew dramatically. DevOps—the cultural and technical practice of unifying software development and IT operations—emerged as one of the most effective strategies for meeting that demand.

Rather than treating development, QA, and operations as separate silos, DevOps organizations use automation, continuous integration and delivery (CI/CD), infrastructure-as-code, and shared accountability to shorten release cycles, reduce failures, and recover faster when things go wrong. For companies migrating to or building natively on the cloud, these practices are no longer optional—they are foundational.

Core DevOps Practices Driving Modern Software Teams

To understand the breadth of DevOps today, it helps to look at the specific practices and disciplines that define it in production environments:

• Continuous Integration (CI): Developers commit code to a shared repository multiple times per day. Automated builds and tests run on every commit, catching integration issues early.
• Continuous Delivery/Deployment (CD): Code that passes automated testing is automatically prepared—or even deployed—to production, reducing manual handoffs and release risk.
• Infrastructure as Code (IaC): Server provisioning and configuration are defined in version-controlled code, making infrastructure reproducible and auditable.
• Observability and Monitoring: Teams instrument applications and infrastructure to collect metrics, logs, and traces, enabling faster incident detection and root-cause analysis.
• Open Source Tooling: Much of the modern DevOps toolchain—Kubernetes, Prometheus, Terraform, Chef, and others—is open source, lowering barriers to adoption and encouraging community innovation.

Five Practitioners Shaping Modern DevOps

The following five professionals represent a cross-section of DevOps expertise—from platform engineering and open source tooling to cloud-native infrastructure and observability. Their backgrounds illustrate how DevOps spans roles, industries, and technology stacks.

Tara Hernandez — Engineering Manager, Google Cloud

Hernandez is recognized as an early advocate of continuous integration. CI is a discipline that requires developers to merge their changes into a shared codebase frequently—often multiple times per day—with automated validation at every step. At scale, this practice dramatically reduces the cost and complexity of integrating large teams’ work, and it forms the backbone of modern CI/CD pipelines.

Nell Shamrell-Harrington — Chef (now Progress Chef)

Shamrell-Harrington works at Chef, one of the pioneering companies in infrastructure automation and configuration management. A significant milestone in Chef’s history was its decision to make all of its core product code open source—a strategic move that reflects a broader industry trend toward open, community-driven tooling. Open-sourcing infrastructure tools lowers vendor lock-in risk and allows practitioners to contribute fixes and features directly.

Illya Chekrygin — Founding Engineer, Upbound

Chekrygin brings hands-on cloud migration experience from a high-profile project at HBO, where he was involved in moving the company’s streaming service to Amazon Web Services using Kubernetes as the container orchestration layer. This type of large-scale migration—taking a latency-sensitive, high-traffic consumer application and re-platforming it onto managed cloud infrastructure—is one of the most complex challenges in modern DevOps. He later co-founded Upbound, a Seattle-based startup focused on cloud-native infrastructure tooling.

Lee Atchison — New Relic

Atchison has spent years working on cloud strategy at New Relic, a company that provides application performance monitoring (APM) and observability tooling. Observability—the ability to understand a system’s internal state from its external outputs—is a cornerstone of mature DevOps practice. Teams that can measure what their systems are doing in production can iterate faster, respond to incidents more confidently, and make data-driven architectural decisions.

Idit Levine — Co-founder and CEO, Solo.io

Levine focuses on helping organizations adopt cloud-native technologies such as Kubernetes and serverless computing. Solo.io builds tooling around service mesh and API gateway patterns—infrastructure that manages how microservices communicate securely and reliably. As organizations decompose monolithic applications into distributed services, this layer of the stack becomes increasingly important for maintaining reliability, security, and observability across complex environments.

Key Takeaways for DevOps Practitioners

Looking across these five practitioners, several themes emerge that are relevant to any engineering team investing in DevOps today:

• CI/CD is table stakes. Continuous integration and delivery are no longer differentiators—they are the baseline expectation for competitive engineering teams.
• Open source tooling dominates. The most widely adopted DevOps tools are open source. Understanding contribution models and community health is part of evaluating tooling choices.
• Cloud-native migrations require operational discipline. Moving to Kubernetes and managed cloud services demands deep understanding of networking, storage, and reliability engineering—not just containerization.
• Observability is a force multiplier. Teams that instrument their systems well move faster and recover from incidents more efficiently than those operating blind.
• Service mesh and API management are maturing fast. As microservices architectures become common, the infrastructure connecting those services—including service meshes—is an active area of tooling development.

The Broader DevOps Landscape

DevOps is not a product you purchase or a certification you earn—it is an ongoing organizational and technical practice. The practitioners highlighted here represent different entry points: tooling, platform engineering, observability, cloud migration, and emerging infrastructure patterns. What they share is a focus on reducing friction between the act of writing software and delivering reliable value to users.

For engineering leaders evaluating where to invest, the message is consistent: automation, observability, open standards, and cultural alignment between development and operations teams remain the highest-leverage areas for improving software delivery performance.

Original reporting by GeekWire. Speaker details reflect information available at the time of the 2019 GeekWire Cloud Summit announcement.