
During October 2025, Ashtarkb developed an execution time estimation feature for data flows in the Red-Hat-AI-Innovation-Team’s sdg_hub repository. This enhancement enables users to predict processing durations and LLM request counts before running full datasets, supporting more accurate workload planning and resource allocation. The implementation involved Python for backend development and leveraged data engineering principles to provide a detailed time breakdown using Rich tables. Ashtarkb expanded test coverage to ensure stability across configurations and introduced new options to the dry_run method, such as enable_time_estimation and max_concurrency, resulting in a robust, well-documented, and thoroughly tested solution.

Month: 2025-10 Concise monthly summary focused on delivering a measurable business and technical impact for the sdg_hub repository under Red-Hat-AI-Innovation-Team. The primary objective this month was to empower upfront workload planning for data flows by adding execution time estimation and strengthening test coverage, enabling safer dry runs and better resource budgeting.
Month: 2025-10 Concise monthly summary focused on delivering a measurable business and technical impact for the sdg_hub repository under Red-Hat-AI-Innovation-Team. The primary objective this month was to empower upfront workload planning for data flows by adding execution time estimation and strengthening test coverage, enabling safer dry runs and better resource budgeting.
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