Results for “bpmp-dtb”

Explains MariaDB Connector/Python's DB API 2.0 behavior, including qmark placeholders, autocommit, prepared statements, buffered cursors, connection pooling, and error handling, for writing and reviewing Python code that uses the mariadb module.

Document end-to-end business processes in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time.

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Generates kernel-DT overlay fragments to enable 25G/10G/1G MGBE QSFP interfaces on Jetson Thor, verifying pinmux and integrating with the BSP customization workflow.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Guides the setup and use of dbt for extract-transform-load workflows.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

Explains MariaDB-specific defaults and traps of the mariadb-dump client for dev workflows, including consistent backups, routines/events flags, diffable dumps, idempotent seeding, and restore via the mariadb client.

Covers MariaDB-specific behavior of the mariadb command-line client, including batch mode output, delimiter handling, safe-updates, charset detection, and TLS defaults, for writing or reviewing shell commands and scripts.

Provides dbt patterns for data transformation and analytics engineering, including model structures, incremental models, and testing.

Guides installation of dbt Core or dbt Cloud CLI and points to official documentation for getting started with dbt.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.