Increased costs and wasted resources are on the rise as software systems have moved from monolithic applications to distributed, service-oriented architectures. As a result, over the past few years, interest in observability has seen a marked rise. Observability, borrowed from its control theory context, has found a real sweet spot for organizations looking to answer the question “why,” that monitoring alone is unable to answer.
Since leaving university, I have always been involved with data, learning so much during my time as an automation engineer and big data engineer working for an automotive tier 1 manufacturing company and as a data architect for an IT consultancy in the SAP and BI domain. And today, I count myself very fortunate to have a job at Kensu that I am truly passionate about. As a Technical Solution Architect, I get to help organizations with data health and data engineering problems on a daily basis!
During QlikWorld ’23 in Las Vegas, we were thrilled to announce the general availability of Data Console in Talend Data Inventory. With data-driven decision making becoming more crucial for organizations, it’s never been more important for users to have access to high quality data.
As digital transformation and the consequent move towards cloud-native continues to accelerate, and customer demands increase, traditional approaches to debugging and troubleshooting are limited and insufficient. Developers must quickly understand the relationships between user sessions, topology, and end-to-end transactions during incidents.
Logs are the core of the human-machine interface for software developers and operators. Historically, they are very much like caveman paintings. They were our first attempt to express and understand how our software was working. For several decades, logs were an island of calm in a rapidly changing technological ecosystem. Logs remained the same even as software services became web-based and grew in scale.
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.
A lot has been written about data observability by authors, analysts, and vendors over the past few years as it is becoming an increasingly important component of organizations' data architectures. This blog will examine why organizations need data observability and how they should approach the buying experience/cycle.
Harness the power of data observability across your modern data stack.
