Results for “poi”

Configures PostgreSQL replication including streaming, logical, cascading, delayed, failover, connection pooling, and backup with point-in-time recovery.

Analyzes multi-step conversion funnels in Adobe Analytics to identify visitor drop-off points, compute step conversion rates, and pinpoint the worst leakage steps.

Best practices for MariaDB system-versioned (temporal) tables, covering creation, querying historical data, managing history growth, and handling ALTER TABLE operations.

Decodes and replays MariaDB binary logs with correct flags: commented pseudo-SQL vs. executable BINLOG blobs, ANNOTATE_ROWS events, flashback undo SQL, and safe replay via the mariadb client.

Points developers to the canonical WordPress Presence API sources and explains its ephemeral-table-plus-TTL architectural pattern, so they avoid inventing postmeta-based presence implementations.

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.

Covers MariaDB-specific behavior of the Connector/C++ sql:: API, including driver and connection setup, 1-based indexes, placeholder syntax, ResultSet iteration, raw pointer ownership, SQLString, autocommit, and exception handling.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for fast in-memory analysis and pandas migration.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for faster pandas-style workflows.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Query, analyze, and tune PostgreSQL databases through your AI agent with safe access controls, including index tuning recommendations, query plan analysis, health monitoring, and schema intelligence.

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

Biblioteca DataFrame rápida (Apache Arrow). Selecione, filtre, group_by, joins, avaliação preguiçosa, I/O CSV/Parquet, expression API, para fluxos de trabalho de análise de dados de alto desempenho.

Audits codebases for GDPR and CCPA/CPRA compliance by inventorying PII fields, mapping data collection points, reviewing consent mechanisms, verifying data subject rights, tracing third-party data sharing, and checking retention policies.

Process in-memory datasets with Polars' expression API, lazy evaluation, and parallel execution, including pandas migration patterns and I/O for CSV, Parquet, and JSON.

Forecast any univariate time series (sales, sensors, energy, vitals, weather) zero-shot using Google's TimesFM foundation model, with point forecasts and prediction intervals from CSV, DataFrame, or array inputs.

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom model. Part of the AlterLab Academic Skills suite.

Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.