{"id":2824,"date":"2026-05-20T11:39:03","date_gmt":"2026-05-20T11:39:03","guid":{"rendered":"https:\/\/oka.how\/?p=2824"},"modified":"2026-07-01T06:15:41","modified_gmt":"2026-07-01T06:15:41","slug":"extending-oka-with-data-enhancers-real-world-examples-from-okas-open-source-library","status":"publish","type":"post","link":"https:\/\/oka.how\/index.php\/2026\/05\/20\/extending-oka-with-data-enhancers-real-world-examples-from-okas-open-source-library\/","title":{"rendered":"Extending OKA with Data Enhancers: real-world examples from OKA’s open-source library"},"content":{"rendered":"
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Extending OKA with Data Enhancers: real-world examples from OKA’s open-source library<\/h1>\n

Introduction<\/h2>\n

Managing an HPC cluster without proper observability is like flying blind. You know jobs are running, but you don’t know what they cost, how much energy they consume, or what their carbon footprint looks like. OKA<\/a>, developed by UCit, was built to solve exactly this problem. It is an all-in-one HPC analytics platform that ships with over 20 native dashboards covering resource utilization, user behavior, job efficiency, sustainability reporting, and cost tracking \u2014 all without requiring custom development from your team.<\/p>\n

Out of the box, OKA ingests data from your job scheduler (Slurm, PBS, LSF, \u2026) and immediately surfaces actionable insights. But no two HPC centers are identical. Your energy monitoring stack, your pricing model, your project accounting conventions, and your sustainability commitments are unique to your organization. That is where Data Enhancers<\/strong> come in.<\/p>\n

Data Enhancers are lightweight Python plugins that run inside OKA’s processing pipeline and add custom columns to your job data, or allow you to compute important metrics such as cost or carbon footprint. They can pull data from external APIs, query internal databases, invoke CLI tools, or simply reformat existing fields. The result is that OKA becomes tailored to your environment, surfacing metrics that matter specifically to you \u2014 carbon footprint per project, real electricity cost per user, power draw per job \u2014 without modifying OKA’s core.<\/p>\n

This article walks through the open-source Data Enhancer library we maintain at UCit. Each enhancer addresses a concrete use case. Whether you are ready to adopt one as-is or use it as a starting point for your own implementation, these examples cover the most common enrichment needs we encounter at HPC centers.<\/p>\n

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The source code for all enhancers discussed here is available in the oka_dataenhancers<\/code> repository<\/strong><\/a>.<\/p>\n<\/blockquote>\n

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OKA provides 20+ HPC-native dashboards, built by cluster operations experts,
giving both IT staff and end-users instant visibility into the cluster\u2019s behavior.<\/figcaption><\/figure>\n<\/div>\n
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Anatomy of a Data Enhancer<\/h2>\n

Before diving into specific examples, let’s understand what a Data Enhancer looks like and how OKA manages it.<\/p>\n

At its core, a Data Enhancer is a Python class that inherits from OKA’s DataEnhancer<\/code> base class and implements a single run<\/code> method. The method receives a Pandas DataFrame \u2014 one row per job, one column per scheduler field \u2014 and returns it enriched with whatever additional columns you add.<\/p>\n

Here is the skeleton of a complete, working enhancer:<\/p>\n

Python<\/span><\/p>\n