Knowledge is power. That’s true for humans, but also for technology systems. The IoT craze we see throughout the business world speaks volumes about this. All those sensors provide valuable information that can be leveraged to optimize uptime, performance, and profit.

In the data world, the discipline of observability has exploded in recent years, largely because of two reasons: 1) Google’s Kubernetes, which has become the de facto standard for building modern (containerized) applications; and, 2) the instrumentation of data ecosystems.

Just as with IoT, the instrumentation of information systems gives administrators the ability to observe, and thus better understand what’s happening. Meanwhile, the growing use of machine learning algorithms is greatly optimizing the historically challenging process of troubleshooting. 

Data observability provides insights into the flow of data across various systems and pipelines, improving data quality, and detecting anomalies or issues in real-time. This is extremely important in today’s highly distributed systems, and always-on Web architectures. 

It also involves monitoring, tracing, and analyzing data to ensure its reliability, security, and compliance. The goal is to make data more transparent and understandable, leading to better decision-making and more efficient data operations.

The Kubernetes Factor

An open-source container orchestration platform, Kubernetes has become increasingly popular for managing and scaling containerized applications. As more organizations adopt Kubernetes to deploy and manage their microservices architecture, the complexity of data pipelines has also increased significantly. This stems from the distributed nature of microservices.

To ensure the seamless functioning of data-intensive applications in a Kubernetes environment, data observability has become crucial. Without proper observability tools, it can be challenging or impossible to effectively track data movements, identify bottlenecks, and troubleshoot issues.

There are many reasons why Kubernetes triggers the need for data observability:

Distributed Systems Complexity: Kubernetes enables the deployment of distributed systems with many interconnected components, making it harder to understand data flows manually.

Dynamic Nature: Kubernetes allows auto-scaling, pod restarts, and rescheduling, resulting in dynamic data movement that requires real-time monitoring.

Containerization: Containers are ephemeral, and traditional monitoring tools might not provide adequate visibility into containerized data processes.

Service Mesh Adoption: Service mesh technologies like Istio or Linkerd are often used with Kubernetes, further adding complexity to data communication within the cluster.

Hybrid Cloud Architectures: Organizations increasingly adopt hybrid cloud solutions with Kubernetes, which demands enhanced observability across multi-cloud environments.

Putting this all together, it’s easy to see why data observability is white-hot these days. In fact, there are even companies now that focus specifically on helping organizations manage their observability data, including transformations and storage. Mezmo is an example of this. 

Top Observability Vendors

The data observability space has seen considerable growth recently, and numerous software vendors provide tools and solutions to help organizations gain insights into their data pipelines. InsideAnalysis compiled the following list of prominent vendors in this space. 

Does your company provide observability software? Let us know, and we’ll consider including you in this list! 

Virtual Summit

DM Radio and 7wData will conduct a Virtual Summit on Sept. 21, 2023, delving into the details of data observability. Want to get involved? Check out the prospectus and let us know!

About Eric Kavanagh

Eric has nearly 30 years of experience as a career journalist with a keen focus on enterprise technologies. He designs and moderates a variety of New Media programs, including The Briefing Room, DM Radio and Espresso Series, as well as GARP’s Leadership and Research Webcasts. His mission is to help people leverage the power of software, methodologies and politics in order to get things done.