Results for “load-data-infile”

Ingests music into a LanceDB via ncmdump-rs and sf-cli, searching and downloading from Netease Cloud Music or Bilibili, decrypting local NCM files, and extracting metadata, lyrics, and cover art.

Load sharded datasets (npy, Parquet, HDF5, raw binary) into distributed cuPyNumeric arrays using manual partitioning and Legate task launches.

Merges CSV/TSV data with an InDesign template to generate personalized PDFs like certificates, badges, and catalogs.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-use-scenario-builder.

Build ETL pipelines with data ingestion, cleaning, and validation steps. Use when ingesting sources, transforming formats, validating data, or scheduling loads.

Extracts and analyzes malicious VBA macros, XLM macros, DDE, and remote template injections in Microsoft Office documents using olevba, oledump, and deobfuscation techniques to identify download cradles, payload execution, and persistence mechanisms.

Generates a comprehensive, editable schema reference file for a data warehouse, including table metadata, row counts, and codebase context, to enable instant concept-to-table lookups.

Bulk-load text files into MariaDB tables using the mariadb-import client, covering table-name-from-filename rules, TAB defaults, --local vs server-side reads, duplicate handling, and parallel loads.