API providers need to observe their APIs to get meaningful data about whether and how they are consumed in practice. API observability is a form of monitoring that passively logs API traffic to an observability service. Different from traditional API monitoring, with API observability you: Monitor interactions to improve developer experience Understand how customers use your API Troubleshoot your API Observing REST APIs is well understood and supported, but not every API is a REST API.

As far back as the 1920s, Service Level Agreements (SLA) were used to guarantee a certain level of service between two parties. Back then, it was the on-time delivery of printed AR reports. Today, SLAs define service standards such as uptime and support responsiveness to ensure reliability. The benefit of having an SLA in place is that it establishes trust at the start of new customer relationships and sets expectations.

The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another. But instead of getting bogged down in the “what” of observability, I think it’s more valuable to address the “why.” What are we trying to accomplish with observability? What is the end goal?

When explaining what Data Observability Driven Development (DODD) is and why it should be a best practice in any data ecosystem, using food traceability as an analogy can be helpful. The purpose of food traceability is to be able to know exactly where food products or ingredients came from and what their state is at each moment in the supply chain. It is a standard practice in many countries, and it applies to almost every type of food product.

“Without clean data, or clean enough data, your data science is worthless.” Michael Stonebraker, adjunct professor, MIT AI is one of the fastest-growing and most popular data-driven technologies in use. Nine in ten of Fortune 1000 companies currently have ongoing investments in AI. So you may be wondering: how could there possibly be another AI winter?

Almost every organization is a data organization. Yet, as data proliferates, data ecosystems have become increasingly complex, making it harder for organizations to control and prevent data quality issues from occurring. Data Observability gives organizations the ability to monitor data usage throughout the data ecosystem.

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