ObservCrew ,🙌
Platform engineering is making waves in observability! This week, we're diving into how this surge is reshaping our field, from skyrocketing salaries to new AI-driven tools. Let's explore what this means for the future of system visibility and performance.
TL;DR : Observability Digest 37 🗞️🚀 Platform engineering takes center stage with higher salaries and growing importance in observability
Key Updates:
💼 Platform engineers earn 20% more than DevOps roles
🤖 Hugging Face releases Observers for AI API monitoring
💰 LogicMonitor secures $800M investment
☁️ AWS enhances ML workload monitoring with Datadog
🏢 Salesforce scales to 1T monthly transactions
Expert Takes:
👨💼 Matt Kennedy on AI's role in log analysis
📊 Stripe's journey with AWS-managed services
💡 Salesforce's approach to zero-code instrumentation
Bottom Line : The rise of platform engineering and AI-driven observability tools is reshaping the industry, presenting new opportunities and challenges for observability professionals.
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A Week in Observability
and Tech Resilience
THIS WEEKS SPOTLIGHT
The Kubernetes job market is experiencing a significant shift, with platform engineers now commanding 20% higher salaries than their DevOps counterparts. This trend reflects the increasing complexity and strategic importance of platform engineering roles in modern tech stacks.
Platform engineering positions are also proving more remote-friendly, with 49% of roles offering remote work compared to 32% for DevOps. This shift aligns with the growing demand for specialized skills in managing complex, distributed systems and the need for organizations to attract top talent regardless of location.
The salary disparity and remote work opportunities highlight the critical role platform engineers play in bridging the gap between development and operations, particularly in Kubernetes-centric environments. As we continue to see the evolution of cloud-native architectures, the demand for professionals who can design, implement, and maintain scalable, efficient platforms is likely to grow further.
While this data validates the trends many of us have observed in the field, it also raises questions about the future of observability roles. As platform complexity increases, we can expect to see a similar upward trajectory in the value placed on observability expertise, particularly in leadership and strategy positions.
The New Stack: Kubernetes Job Market Analysis (5 mins)
WHAT TO WATCH
Hugging Face has released Observers, an open-source Python library providing comprehensive observability for generative AI APIs. This development highlights the growing importance of observability in AI applications, offering tools for monitoring, logging, and analyzing the performance of large language models. The library's features include request/response logging, latency tracking, and integration with popular observability platforms, making it easier for developers to gain insights into their AI systems' behaviour and performance. Discover Observers for AI observability (5 mins)
MARKET NEWS
LogicMonitor has secured an $800 million investment while Vista Equity Partners maintains majority control. This significant funding demonstrates continued confidence in observability platforms during market uncertainty. The investment is likely to fuel LogicMonitor's growth and innovation in the observability space, potentially leading to new features and expanded market reach. Read about LogicMonitor's funding (3 mins)
Palo Alto Networks has reported strong revenue growth, surpassing expectations in its latest financial results. This success underscores the increasing demand for cybersecurity solutions and their integration with observability practices. As organizations face growing security challenges, the convergence of security and observability is becoming more critical. Explore Palo Alto's financial performance (4 mins)
LATEST PRODUCT UPDATES
AWS has enhanced observability for AWS Trainium and AWS Inferentia with Datadog integration. This update allows for better monitoring and optimization of machine learning workloads on AWS, providing deeper insights into model performance and resource utilization. Learn about AWS's ML observability enhancements (6 mins)
Grafana Labs has extended its observability capabilities deeper into Kubernetes environments. This expansion offers more granular insights into container orchestration, helping teams better manage complex, distributed systems. Explore Grafana's Kubernetes observability (5 mins)
Cloudsmith has unveiled an advanced observability suite for artifact management. This new offering aims to provide better visibility and control over software supply chains, addressing growing concerns about security and efficiency in software development processes. Discover Cloudsmith's artifact management observability (4 mins)
SECURITY AWARENESS IN OBSERVABILITY
DuploCloud has launched a cost-effective observability suite, focusing on making comprehensive monitoring and security features more accessible to a wider range of organizations. This development highlights the growing importance of integrating security and observability practices, especially for smaller teams and businesses. Explore DuploCloud's observability suite (4 mins)
LEVEL UP
The Cloud Native Computing Foundation (CNCF) has published a guide on implementing cloud-neutral Postgres databases with Kubernetes and CloudNativePG. This resource offers valuable insights for teams looking to enhance their database observability and management in cloud-native environments. Learn about cloud-neutral Postgres with Kubernetes (7 mins)
For those looking to expand their observability vocabulary, Michael Shpilt has compiled a comprehensive observability dictionary. This resource is invaluable for both beginners and experienced practitioners looking to deepen their understanding of key concepts and terminology in the field. Explore the observability dictionary (10 mins)
COMMUNITY-DRIVEN ARTICLES
CodeCraft: Elevate Your Observability Skills
In this section, we've curated expert coding tips and insights to boost your observability prowess:
Kubernetes 1.31 introduces five game-changing features that are set to transform coding practices. These updates include enhancements to pod scheduling, improved security features, and better support for stateful applications, all of which have significant implications for observability implementations. Discover Kubernetes 1.31 features (8 mins)
XLA and PJRT are emerging as powerful tools for machine learning, offering new possibilities for optimizing and scaling ML workloads. Understanding these technologies is crucial for implementing effective observability in ML-driven systems. Learn about XLA and PJRT in machine learning (7 mins)
A guide on performing Kafka operations from a local machine has been published, offering practical insights for developers working with distributed systems. This knowledge is essential for implementing robust observability practices in Kafka-based architectures.Explore local Kafka operations (6 mins)
SUCCESS STORIES
Stripe has shared insights into its observability practices on AWS-managed services. This case study offers valuable lessons on implementing scalable observability solutions in a cloud-native environment, demonstrating how effective monitoring can drive operational excellence. Learn from Stripe's observability practices (6 mins)
Salesforce has revealed how its observability platform scales to handle one trillion transactions monthly. Their approach, leveraging zero-code instrumentation and open standards, provides a blueprint for implementing observability at a massive scale. Explore Salesforce's observability at scale (8 mins)
EXPERT VOICES
Mike Kennedy explores how AI log analysis is shaping the future of observability. His insights highlight the growing role of artificial intelligence in enhancing observability practices, offering new ways to derive insights from vast amounts of data. Discover AI's impact on log analysis (7 mins)
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WHAT’S ON MY MIND
This week's developments in platform engineering and AI-driven observability highlight the rapid evolution of our field. As we see the rise of specialized roles and tools, it's clear that the observability landscape is becoming more nuanced and sophisticated. The surge in platform engineering salaries and the integration of AI into observability practices suggest a growing recognition of the strategic importance of these skills.
However, as we embrace these advancements, we must also consider their broader implications. How will the rise of AI in observability impact traditional roles? What new skills will we need to develop to stay relevant in this changing landscape? As we continue to push the boundaries of what's possible with observability, these are questions we'll need to grapple with as a community.
What's your take on these developments? How do you see the role of AI and specialized engineering positions shaping the future of observability? Share your thoughts and let's keep the conversation going!
Keep observing!
Allan
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