OPAL: A Primer

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.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.