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3 repositorios

Awesome GitHub RepositoriesData Observability Profilings

Automated profiling of data distributions to detect shifts and trigger reliability alerts.

Distinct from Observability and Profiling: Candidates were either too broad (awesome lists) or focused on system telemetry rather than data-specific profiling.

Explore 3 awesome GitHub repositories matching data & databases · Data Observability Profilings. Refine with filters or upvote what's useful.

Awesome Data Observability Profilings GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • open-metadata/openmetadataAvatar de open-metadata

    open-metadata/OpenMetadata

    14,213Ver en GitHub↗

    OpenMetadata is an enterprise data catalog, metadata platform, and governance suite that functions as a knowledge graph for data assets. It serves as an AI-ready metadata layer, providing governed context and organizational memory to large language model agents via the Model Context Protocol. The platform distinguishes itself by capturing institutional knowledge, linking conversations, decisions, and remediation notes directly to data assets to preserve tribal knowledge. It integrates AI agents to automate metadata governance, such as suggesting descriptions and identifying sensitive data thr

    Runs profiling checks to detect distribution shifts and triggers alerts for root-cause analysis.

    TypeScriptcontextcontext-layerdata-catalog
    Ver en GitHub↗14,213
  • data-centric-ai-community/fg-data-profilingAvatar de Data-Centric-AI-Community

    Data-Centric-AI-Community/fg-data-profiling

    13,609Ver en GitHub↗

    This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns. The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation. The software covers a broad an

    Provides automated profiling of data distributions to detect shifts and quality issues in Pandas DataFrames.

    Python
    Ver en GitHub↗13,609
  • pytorch/executorchAvatar de pytorch

    pytorch/executorch

    4,296Ver en GitHub↗

    ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,

    ExecuTorch captures profiling and debugging data from model execution and exposes it through a structured dump for post-run analysis.

    Pythondeep-learningembeddedgpu
    Ver en GitHub↗4,296
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  2. Data & Databases
  3. Data Observability Profilings

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  • Runtime Profiling Data ExtractorsCaptures profiling and debugging data from model execution and exposes it through a structured dump for post-run analysis. **Distinct from Data Observability Profilings:** Distinct from Data Observability Profilings: focuses on extracting performance data from ML model runtime execution, not profiling data distributions in databases.