Hybrid Cloud Observability Guide

What is Hybrid Cloud Observability?

Observability is the ability to gain insight into the internal state of a system or process through its external outputs, without having to modify or interfere with the system itself. In the context of computer systems, observability refers to the ability to monitor and understand the behavior of a software application or system, including its performance, health, and errors.

Hybrid Cloud Observability refers to the ability to gain insights into the performance and behavior of applications and services that run across a hybrid cloud environment— which combines public and private cloud infrastructure, as well as on-premises infrastructure, to create a single, integrated infrastructure

Observability is a relatively new term in IT and computing. It represents a natural progression from the data collection techniques used in Application Performance Monitoring (APM) and Network Performance Monitoring (NPM). Observability addresses the challenges posed by cloud-native application deployments, which are becoming more distributed, dynamic, and faster. It complements APM and NPM monitoring by providing better insights.

Hybrid cloud observability tools provide real-time monitoring and analysis of both on-premises and cloud-based IT infrastructure and applications. This enables effective monitoring, troubleshooting, and debugging of applications and networks to meet customer expectations, service level agreements (SLAs), and other business needs. It enables IT teams to track the performance, availability, and health of their hybrid cloud environments and detect issues before they affect end users.

Why do we Need Hybrid Cloud Observability?

For many years, APM software has been the go-to tool for monitoring and troubleshooting traditional distributed applications. APM collects, aggregates, and analyzes telemetry data against key performance indicators (KPIs) and presents the results in a dashboard to alert the support and operations teams to abnormal conditions that need to be addressed to prevent or resolve issues.

However, organizations are now adopting modern development practices such as agile development, DevOps, continuous integration and deployment (CI/CD), hybrid cloud,  and cloud-native technologies such as microservices, Kubernetes, Docker containers, and serverless architecture. As a result, they are bringing more services to the market faster than ever before. They are deploying new application components so frequently, in so many different languages and platforms, and for such varying periods (even for seconds or fractions of a second in the case of serverless architecture) that APM’s once-per-minute data sampling can no longer keep up.

To address these concerns, a lot of higher-quality telemetry data is needed to create a highly accurate, context-rich, fully correlated record of every transaction. This is where hybrid cloud observability comes in.

Hybrid cloud observability is a crucial requirement for organizations that use both public and private cloud infrastructures. It enables them to monitor and manage the performance and availability of their applications and services across multiple cloud environments. By providing complete visibility into the functioning of applications and services, hybrid cloud observability helps in identifying and resolving issues quickly, reducing downtime, and enhancing system reliability. It also enables organizations to scale their applications and services without compromising on performance and availability, optimize their infrastructure, and reduce costs by identifying and eliminating inefficiencies and redundancies.

Hybrid Cloud Observability is important because it allows organizations to ensure that their applications and services are running smoothly, and to detect and resolve issues quickly before they cause downtime or impact end-users. In a hybrid cloud environment, where applications and services are distributed across multiple clouds and on-premises infrastructure, it can be challenging to gain visibility and control over the entire infrastructure. Hybrid Cloud Observability provides a unified view of the hybrid cloud infrastructure, which enables organizations to optimize performance, improve reliability, and reduce the mean time to resolution (MTTR) of issues.

How Does Hybrid Observability Work?

Observability platforms continuously discover and collect performance telemetry by integrating with multiple observability tools, as well as existing instrumentation present in application and infrastructure components. This allows for the seamless flow of data between different tools and ensures that all data is available in a single location for analysis.

In hybrid observability, data is collected from multiple sources such as logs, metrics, traces, and dependencies. This data is then analyzed and correlated to provide a complete view of the system’s behavior. The approach also allows for the identification of correlations between different types of data, which helps in identifying the root cause of problems.

The four main sources of data are described as follows:

  1. Logs These are detailed, timestamped, complete, and unchanging records of application events. Logs can be used to create a precise, millisecond-by-millisecond record of every event, along with its surrounding context, which developers can use for troubleshooting and debugging purposes.
  2. Metrics Also known as time series metrics are fundamental measures of application and system health over a specified period, such as how much memory or CPU capacity an application uses over five minutes or how much latency an application experiences during a spike in usage.
  3. Traces Record the entire end-to-end journey of every user request, starting from the UI or mobile app through the entire distributed architecture and back to the user.
  4. Dependencies (also called dependency maps) These reveal how each application component is reliant on other components, applications, and IT resources.

After gathering this telemetry, the observability platform correlates it in real-time to provide IT teams with complete, contextual information—the what, where, and why of any event that could indicate, cause, or be used to address an application performance issue. An overview of parallel application and hardware landscapes can also be helpful.

Observability and Cloud-Native Deployments

Observability is a critical component of cloud-native deployments, as it enables teams to monitor and troubleshoot complex, distributed systems in real time. Both hybrid cloud observability and cloud-native deployments rely on modern, cloud-native technologies and practices to deliver greater agility, scalability, and resilience. By collecting and analyzing data from logs, metrics, and traces, observability tools can provide deep visibility into the performance and behavior of cloud-native applications, allowing teams to detect and diagnose issues quickly and optimize their operations.

In a hybrid cloud environment, observability becomes even more important, as teams must be able to monitor and manage applications and infrastructure across multiple clouds and data centers. This requires a comprehensive observability solution that can provide a unified view of the entire hybrid cloud environment, including metrics, logs, and traces from all components.

AIOps In Hybrid Cloud Observability

Artificial intelligence for operations (AIOps) is a set of technologies and practices that use artificial intelligence (AI) and machine learning (ML) to automate and optimize IT operations. AIOps is particularly relevant in hybrid cloud observability because of the complexity of managing and monitoring applications and infrastructure across multiple cloud environments. It is a valuable technology for organizations that need to manage complex, distributed systems across multiple cloud environments.

Many observability platforms include AIOps capabilities to automatically identify patterns and correlations in the data, making it easier to weed out noise (data unrelated to issues) and identify potential problems. AIOps can help organizations improve their hybrid cloud observability by providing a more proactive and automated approach to monitoring and troubleshooting. By analyzing vast amounts of data from logs, metrics, and traces, AIOps tools can identify patterns and anomalies that may be difficult to detect manually, enabling teams to detect and diagnose issues more quickly and efficiently.

AIOps can also help organizations optimize their operations by automating routine tasks and processes, such as provisioning and scaling, based on data-driven insights. This can help teams reduce the time and effort required to manage their hybrid cloud environments, enabling them to focus on more strategic tasks that add value to the business.

Another benefit of AIOps in hybrid cloud observability is the ability to provide more accurate and predictive insights into system performance and behavior. By analyzing historical data and using machine learning algorithms, AIOps tools can predict potential issues before they occur, enabling teams to proactively address them and prevent downtime or performance issues. By leveraging AI and ML to automate and optimize IT operations, AIOps can help organizations improve their hybrid cloud observability, detect and diagnose issues more quickly and efficiently, optimize their operations, and deliver a better user experience to their customers.

Benefits of Hybrid Cloud Observability

Hybrid cloud observability offers several benefits to organizations that need to manage complex, distributed systems across multiple cloud environments.

Firstly, it provides a unified view of the entire hybrid cloud environment, enabling teams to monitor and manage applications and infrastructure from a single dashboard. This allows organizations to gain greater visibility into the performance and behavior of their systems, detect and diagnose issues quickly, and optimize their operations to deliver a better user experience.

Secondly, hybrid cloud observability helps organizations improve their operational efficiency by providing real-time insights into the health and performance of their systems. This allows teams to proactively identify and address issues before they impact users, reducing downtime and improving overall system reliability.

Thirdly, hybrid cloud observability enables organizations to leverage data-driven insights to make better decisions about their infrastructure and application performance. By collecting and analyzing data from various sources, observability tools can provide valuable insights into system performance trends, usage patterns, and potential issues, helping teams make informed decisions about system design and optimization.

Lastly, hybrid cloud observability is critical for ensuring compliance and security in a multi-cloud environment. By monitoring and analyzing system logs and metrics, observability tools can help organizations identify and address security risks and compliance issues before they become a problem, enabling them to maintain a secure and compliant infrastructure across all cloud environments.

Challenges and Key Considerations for Hybrid Cloud Observability 

While hybrid cloud observability offers many benefits, there are also several challenges and key considerations that organizations need to address to ensure the success of their observability strategy.

Here are some of the current challenges and key considerations for hybrid cloud observability:

  • Data Collection One of the biggest challenges in hybrid cloud observability is the collection and management of data from various sources. To get a complete view of system performance and behavior, observability tools need to collect data from multiple sources, including logs, metrics, and traces. This can be difficult in a hybrid cloud environment, where data may be distributed across multiple cloud providers and on-premises infrastructure.
  • Tool Integration Another challenge in hybrid cloud observability is the integration of multiple tools and platforms. Organizations may use a variety of observability tools, such as monitoring and logging tools, that need to be integrated to provide a unified view of system performance. This can be complicated by the fact that different tools may use different data formats and protocols.
  • Scalability As the size and complexity of hybrid cloud environments grow, observability tools need to be able to scale to meet the needs of the organization. This requires a highly scalable and resilient observability infrastructure that can handle large amounts of data and traffic without compromising performance.
  • Security and Compliance Hybrid cloud observability raises security and compliance concerns, as sensitive data may be collected and analyzed across multiple cloud environments. Organizations need to ensure that their observability tools are secure and comply with relevant regulations and standards, such as GDPR and HIPAA.
  • Talent and Training Finally, hybrid cloud observability requires skilled personnel who can operate and manage observability tools effectively. Organizations need to invest in training and developing their teams to ensure that they have the necessary skills and knowledge to manage complex observability environments.

By addressing these challenges and key considerations, organizations can ensure that their observability infrastructure is robust, scalable, and able to provide valuable insights into system performance and behavior.

The Future of Hybrid Cloud Observability

The future of hybrid cloud observability is likely to be shaped by several trends and developments in the coming years.

One trend is the increasing adoption of cloud-native technologies and architectures, such as Kubernetes and microservices, which are designed to enable greater agility, scalability, and resilience in application development and deployment. As more organizations adopt these technologies, hybrid cloud observability tools will need to evolve to support these new environments and provide deeper visibility into the performance and behavior of cloud-native applications.

Another trend is the growing importance of artificial intelligence (AI) and machine learning (ML) in observability, which can help organizations analyze vast amounts of data in real time and identify patterns and anomalies that may be difficult to detect manually. By leveraging AI and ML, observability tools can provide more accurate and proactive insights into system performance and behavior, enabling teams to optimize their operations and deliver a better user experience.

In addition, the increasing complexity of hybrid cloud environments and the proliferation of data sources will require more sophisticated observability solutions that can provide a unified view of all systems and data. This will require the integration of multiple observability tools and platforms, as well as the development of new technologies and standards to enable seamless interoperability between different systems and data sources.

As hybrid cloud environments become more critical to business operations, the importance of observability for ensuring compliance and security will continue to grow. Observability tools will need to provide more advanced security and compliance features, such as real-time threat detection and automated compliance checks, to help organizations maintain a secure and compliant infrastructure across all cloud environments.