Systems | Development | Analytics | API | Testing

The role of data observability for enterprise data integrity

Data observability is a critical aspect of any data-driven business. It refers to the ability to monitor, understand, and troubleshoot data quality issues in real-time. In essence, data observability ensures that data is accurate, reliable, and accessible. It involves collecting and analyzing data from various sources to gain insight into how data is being used, how it is flowing through the system, and whether it is meeting the required quality standards.

Microservice Observability with Kong Mesh

Microservice Observability with Kong Mesh Brought to you by @KongInc Senior Solutions Engineer Ahmed Koshok. In this video, Ahmed Koshok demonstrates how Kong Mesh enables you to monitor and troubleshoot your microservices in real-time. Kong Mesh is a service mesh built on top of the Envoy proxy that provides advanced observability features for microservices. With Kong Mesh, you can collect telemetry data, trace requests across multiple services, and visualize service dependencies.

Extending SmartBear's Modern Developer Focused Observability Capabilities

Today, we are very excited to announce the acquisition of Aspecto, an OpenTelemetry (OTel) pioneer whose capabilities will further extend SmartBear’s modern, developer-focused observability capabilities. Aspecto’s solution discovers modern microservice based architectures and visualizes all the real-time interactions between services and APIs through advanced distributed tracing capabilities.

What Is the Difference Between Observability and Monitoring?

The practice of DevOps — development operations — has taken organizations by storm. According to a 2021 report by Redgate Software, 74 percent of enterprises surveyed say they now use DevOps in some form or fashion, compared with just 47 percent in 2016. DevOps practitioners seek to improve the software development lifecycle by fostering closer collaboration between developers and IT operations teams.

AIOps Observability: Going Beyond Traditional APM

AIOps is an emerging technology that applies machine learning and analytics techniques to IT operations. AIOps enables IT teams to leverage advanced algorithms to identify performance issues, predict outages, and optimize system performance. Nodesource sees significant advantages for developers and teams to increase software quality by leveraging AIOPS.

Why do we need DataOps Observability?

DevOps was started more than a decade ago as a movement, not a product or solution category. DevOps offered us a way of collaborating between development and operations teams, using automation and optimization practices to continually accelerate the release of code, measure everything, lower costs, and improve the quality of application delivery to meet customer needs.

7 Important Capabilities for Data Observability

Organizations need to manage data across ecosystems, develop data pipelines, APIs, insight into their metadata, and try to make sure that silos and data quality issues are managed effectively. Enter data observability platforms. This blog post looks at what drives many organizations to adopt data observability to ensure the health of your data across systems and providers.