Best Practices for Multi-Cloud Observability

If The Notorious BIG – the artist behind the iconic song "Mo Money Mo Problems" – had been an IT operations engineer, he might instead have labeled his hit "Mo Clouds Mo Problems."

Why? Because the more clouds you have to manage and monitor, the more problems you're likely to run into. For example, when organizations opt for a multi-cloud architecture – meaning one that involves multiple public and/or private clouds – they face cloud monitoring and observability challenges that don't apply in single-cloud environments.

That's why multi-cloud architectures should be accompanied by multi-cloud observability strategies. Keep reading for tips on why multi-cloud observability is important, what makes it uniquely challenging, and best practices for devising an observability strategy that conquers these challenges.

What is observability?

In the cloud and any other type of IT environment, observability is the ability to understand what's happening inside the environment based on external outputs, such as logs, metrics, and traces. In other words, when you observe an IT environment, you collect and analyze data from the environment to infer its internal state.

Observability builds on the principles of monitoring, a practice IT teams have long used to measure the health and performance of digital resources.

But whereas monitoring focuses on collecting data, observability goes deeper by correlating and analyzing multiple types of data in order to gain comprehensive visibility.

The role of observability in multi-cloud operations

The ability to analyze data comprehensively makes observability especially important when managing complex systems that involve multiple components, like multi-cloud architectures.

Basic monitoring might suffice if you're managing a monolithic app hosted on an on-prem server, but not when you need to support a collection of distributed applications across multiple cloud environments.

This is why observability and multi-cloud architectures go hand-in-hand. In most cases, it's virtually impossible to gain reliable visibility into multi-cloud infrastructure and workloads without effective observability tools and practices.

Common challenges of multi-cloud observability

While a multi-cloud observability strategy is important for virtually any organization that adopts a multi-cloud architecture, implementing multi-cloud observability is not usually easy. IT teams must overcome a number of challenges:

• Tool diversity: The monitoring and observability tools built into each cloud platform are different, and they typically do not integrate easily with each other or support competing cloud environments. As a result, multi-cloud observability sometimes requires the ability to juggle multiple tools.
• Disparate configurations: Each cloud platform also has its own Identity and Access Management (IAM) framework and other configuration settings. This is another factor that complicates multi-cloud observability because it means teams must be able to work across disparate configurations and understand the nuances of each cloud they are supporting.
• Architectural complexity: Multi-cloud architectures involve multiple cloud environments and services. This complexity can complicate observability and troubleshooting by making it challenging to pinpoint root causes. For example, imagine that an application is hosted in one cloud but processes data stored in another cloud. If the app begins experiencing high latency, you'd need to figure out whether the issue stems from a problem with the app itself, the cloud environment that hosts it, the other cloud platform where the data resides or the network that connects the two clouds.
• Compliance and security challenges: The more clouds you are managing, and the more data you are collecting and analyzing from them, the greater the risk that you'll accidentally expose resources to attack by, for example, storing sensitive data in an insecure location or accidentally applying a configuration that leads to a breach.

Best practices for multi-cloud observability

There is no "one simple trick" for solving all your multi-cloud observability woes. But there are several best practices that can help streamline the process of observing complex, multi-cloud environments:

Standardizing monitoring approaches

For starters, organizations should strive to standardize their monitoring tools and processes. For example, they could implement an observability pipeline that uses a standardized observability framework, like OpenTelemetry, to collect data from across all of their environments. This mitigates the challenges of having to rely on disparate tools within each cloud platform to collect observability data.

Prefer open source, standards-based solutions

More generally, open source, standardized observability frameworks, monitoring tools and data analytics solutions help to simplify multi-cloud observability. This is because they free organizations from becoming locked into cloud platform provider tools that don't integrate with each other or work well across clouds.

Centralize data collection and analysis

The greater your ability to store and analyze observability data in a central location, the easier it becomes to observe a multi-cloud architecture. This is another area where observability pipelines can help by pulling data from across all of your cloud environments and directing it to a central destination for analysis.

Manage data security across clouds

When working with complex multi-cloud architectures and data sets, it's critical to build security into the processes used to collect and analyze information. For example, data should typically be encrypted before you extract it from one of your clouds. You could also consider steps like anonymizing or minimizing data while it is in transit.

Once again, this is an area where observability pipelines can help. Observability pipelines allow you to apply protections and transformations like data encryption, anonymization and so on while data is moving between cloud platforms – which means you can effectively secure the data even if your data collection tools don't provide these capabilities natively.

Choosing the right multi-cloud observability tools

When selecting cloud monitoring and observability tools capable of supporting a multi-cloud strategy, look for features like the following:

• Compliance with open data collection and observability standards, like OpenTelemetry, help ensure interoperability between tools.
• The ability to integrate with other data collection and analytics tools so that you can collect and interpret data using whichever approach works best for your team.
• Support for all of the cloud platforms, environments or services you need to manage.
• The ability to correlate data, not just collect it. Correlation is critical for gaining context on performance issues and getting quickly to root-cause problems.

The ability to operate efficiently without consuming excessive amounts of CPU, memory, or disk space. This is important because the more resources your observability tools use, the greater the strain they place on your cloud environments and the higher they're likely to cost you to operate because you typically have to pay for the CPU, memory, and disk that your tools consume.

Conquering multi-cloud observability with BindPlane

BindPlane offers observability solutions built from the ground up for the complex, multi-cloud world we live in. You can seamlessly collect data from across all of the clouds you use, process it, secure it and analyze it using open, standards-based tooling.

Learn more about how BindPlane can help solve multi-cloud observability challenges by requesting a demo.