March 2022 Summaries

Fintech companies face significant operational demands that necessitate robust observability to manage risks and maintain agility, especially given their critical role in supporting financial institutions and consumers. The rapidly evolving financial landscape requires these companies to adapt quickly, using technologies like cloud, microservices, and Kubernetes, while managing the inherent complexity to maintain release velocity. Observability provides essential cloud visibility and data context, enabling effective troubleshooting across distributed services and multiple cloud accounts, which is crucial for compliance, security, and reliability. Long-term data retention is vital for regulatory compliance and security incident investigations, making it important to avoid discarding valuable data due to cost concerns. Observability tools also enhance security by providing comprehensive visibility across IT stacks, enabling proactive maintenance and ensuring customer experiences are not the first alert system for issues. Ultimately, effective observability strategies can transform data into actionable insights that improve reliability, security, and customer satisfaction.

OPAL, or the Observe Processing and Analysis Language, is a versatile data processing language created to simplify the modeling and analysis of observability data for Observe users. It allows users to generate actionable insights with ease, either through the UI or by writing OPAL scripts, which are composed of inputs, verbs, functions, and outputs in a pipeline format. This structure enables users to chain operations without worrying about the order, focusing instead on the desired outcome. OPAL is designed to perform declarative transformations without side effects, allowing users to experiment and reanalyze data non-destructively. An essential aspect of OPAL is the distinction between streamable and unstreamable verbs, affecting how datasets can be accelerated for performance. Streamable verbs produce datasets that can be accelerated, which is crucial for building foundational datasets for further analysis. OPAL also includes various types of verbs and functions, categorized by the actions they perform on data. While users can benefit from default UI-generated OPAL scripts, they have the flexibility to customize queries for more precise data visualization and modeling, with resources like an OPAL reference guide and support channels available for deeper exploration.