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The Augmented Tech Leader: How AI Can Empower Your Engineering Team
AI tools can fundamentally transform the way engineering managers lead teams by not only speed and convenience but deeper insights and impact. By embracing AI-driven solutions in code review, project management, and daily operations, we can unlock new levels of effectiveness in the AI era. Making Code Reviews Smarter Traditional code reviews are time-consuming and…
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AI and Machine Learning: Transforming Observability
As the shift from Observability 1.0 to 2.0 unfolds, several key factors are driving this transformation, such as the increasing complexity of software systems and the need for a more comprehensive approach to monitoring. One of the most significant advancements in Observability 2.0 is the integration of artificial intelligence (AI) and machine learning (ML) into…
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Dynamic Alerting in Observability 2.0: Responding to Ever-Changing Systems
As we continue our exploration of the transition from Observability 1.0 to 2.0, we’ve discussed the importance of metrics, logs, and traces, as well as the impact of AI and ML on monitoring tools. Another crucial aspect of Observability 2.0 is dynamic alerting, which enables organizations to adapt to rapidly changing systems and respond more…
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The Role of End-to-End Visibility in Observability 2.0
Throughout our series on the transition from Observability 1.0 to 2.0, we’ve explored topics such as data correlation, AI and machine learning integration, and dynamic alerting. Another essential aspect of Observability 2.0 is end-to-end visibility, which allows teams to gain a comprehensive view of their entire system and its dependencies. In this post, we’ll discuss…
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Observability-Driven Development: Aligning Monitoring and Software Development
Our series on the transition from Observability 1.0 to 2.0 has discussed various aspects of modern monitoring, such as data correlation, AI and machine learning integration, dynamic alerting, and end-to-end visibility. One crucial element of Observability 2.0 is the integration of observability practices into the software development process itself. In this post, we’ll discuss the…
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Cannabis: A New Strain of E-commerce Technology – SXSW 2023
Online ordering, on-demand delivery, audience targeting, AI-assistants and digital transactions are all e-commerce capabilities that we take for granted, but without this kind of tech, today’s growing legal cannabis industry would be struggling to come to fruition. Though still young, the legal cannabis industry is emerging at a moment when e-commerce technology is just mature…
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Embracing Context: The Power of Correlation in Observability 2.0
As we’ve discussed in previous posts, the transition from Observability 1.0 to 2.0 is driven by the growing complexity of modern software systems and the need for more comprehensive monitoring solutions. A key aspect of Observability 2.0 is its emphasis on context and correlation, which enables teams to link data across multiple sources and gain…
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The Pillars of Observability 2.0: Metrics, Logs, and Traces
As we continue to explore the transition from Observability 1.0 to 2.0, it’s crucial to understand the foundational elements of this new approach to monitoring. Observability 2.0 revolves around three key pillars: metrics, logs, and traces. Together, they provide a comprehensive view of system performance and enable teams to diagnose and resolve issues more effectively.…
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From Observability 1.0 to 2.0: Why the Shift is Essential
Dive into the three pillars of observability 2.0 – metrics, logs, and traces – and understand their significance in enabling a comprehensive monitoring strategy.
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The Dawn of Observability 1.0
As we prepare to embrace the next era software systems, it’s essential to look back and understand the beginnings of observability in software systems. Observability laid the groundwork for modern monitoring and enabled us to maintain and manage complex systems effectively. We’ll delve into the roots of Observability, its core components, and how it has…