Results for “dacl”

Executa consultas, inspeção de schema e mutações seguras (DML/DDL com dry-run) no PostgreSQL via servidor MCP, exigindo confirmação explícita para alterações.

Accelerate pandas workflows with GPU DataFrames using cuDF and dask-cuDF for ETL, joins, groupby, and large-scale data processing.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines

Extract Warehouse DDL & Metadata

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Runs SQL queries and data conversions on CSV, Parquet, and JSON files using the DuckDB command-line interface, including schema inspection and output formatting.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Reference for MariaDB control-flow functions and operators, including IF(), IFNULL(), NULLIF(), COALESCE(), CASE, NVL2(), and DECODE_ORACLE, with guidance on correct usage and common pitfalls.

Manage Amazon Aurora DSQL clusters, schemas, and migrations with MCP tools and CLI scripts, including multi-tenant patterns and IAM-based authentication.