March 2024 Summaries

Observe has achieved a significant milestone by raising $115 million in Series B funding, led by Sutter Hill Ventures, with additional support from Capital One Ventures, Madrona Ventures, and Snowflake Ventures. The company, founded six years ago, was built on the idea of consolidating all telemetry data such as logs, metrics, and traces into a single database to improve troubleshooting for SRE, DevOps, and Engineering teams. By using a modern cloud-native architecture, Observe has demonstrated the ability to reduce costs and streamline user experience by allowing seamless transitions between different data types without leaving the platform. Unlike other vendors, Observe provides both technical and business context, tailored to the specific needs of various customers, thereby addressing the issue of siloed data. The company's FY24 results have been impressive, with ARR and ACV nearly tripling and NRR at 174%, highlighting its potential to become a major player in the observability tooling market. With a vision to consolidate tooling and solve data navigation issues, Observe is supported by long-term investors and is poised to emerge as a category leader.

Arthur Dayton from Observe describes a method to integrate Atlassian Jira with Observe for efficient data management and reporting. By utilizing Jira's Python library and Observe's Python sender class, users can easily query Jira issues using JQL and send the data to Observe, bypassing Jira's built-in reporting tools. The integration allows for the creation of custom dashboards and enables more robust data analysis using OPAL within Observe. Dayton also highlights using GitHub Actions to schedule the execution of the data integration script, facilitating a straightforward and secure workflow by storing necessary API tokens as secrets. This approach provides a flexible, automated solution to manage and visualize Jira data efficiently without the overhead of complex infrastructure.

Observe offers a robust Observability Cloud that enables teams to manage their software effectively at an internet scale by providing insights from extensive machine and user data. Central to its operation is the self-monitoring approach known as "Observe on Observe" (O2), which enhances the platform's quality by using its own services for monitoring. The platform aggregates performance and business data into an interactive Data Graph, facilitating comprehensive analysis through datasets that represent business and infrastructure entities. This interconnected data model allows for easy navigation and multivariate analysis, aiding in performance investigation and customer usage evaluation. The O2 system supports rapid model evolution to adapt to new features and requirements, with dashboards offering detailed analytics on product usage and customer interactions. The platform's schema-on-demand capability ensures flexibility and scalability, while providing valuable insights to product managers and engineers for informed decision-making and product optimization. Future installments will delve into technical aspects like user activity tracking and service provider monitoring, further enhancing the platform's observability capabilities.

Observe's approach to AI observability tackles the unique challenges posed by AI-powered applications that traditional monitoring metrics fail to address. The company employs AI features like O11y GPT Help and O11y Co-Pilot to enhance observability by providing insights that other tools struggle to offer. These AI integrations utilize cutting-edge technologies such as vector databases and custom AI model training, necessitating a more complex data capture approach to evaluate system performance comprehensively. Observe uses its own OPAL data modeling to build analytics products, capturing full service payloads and structured logs to monitor system behavior in real-time. The platform's architecture includes a variety of clients and gateways using different observability techniques such as OpenTelemetry tracing, Prometheus metrics, and structured logging, which allows for a holistic view of the system's performance. Different data streams categorize and analyze events, enabling detailed analysis of user interactions, AI model performance, and system efficiency. This setup supports diverse use cases, from product management to engineering and data science, by providing real-time insights that facilitate continuous improvement and optimization of AI models and features.

Observe provides a highly scalable and available Observability Cloud that enables global teams to monitor software at an Internet scale by consolidating siloed data into a single data lake, transforming it into a connected graph of datasets. Using its own platform, "Observe on Observe" (O2), Observe self-monitors its system to ensure quality, providing insights into infrastructure, application, and DevOps performance. The platform employs a range of technologies such as Kafka, Snowflake, and Kubernetes, and offers prebuilt applications for AWS, GCP, and Azure, among others, along with custom configuration options using Terraform. The O2 environment is not only used for real-time analytics and infrastructure monitoring but also serves as a development playground where new features are tested and validated before wider deployment. This self-observation provides valuable data that enhances both the technical and business aspects of the service, ensuring efficient resource use, optimal customer performance, and facilitating continuous improvement and innovation.