Results for “data-tables”

Guides setup and operation of the aws_dynamodb_cdc input in Redpanda Connect, which streams change data capture from AWS DynamoDB into Redpanda or Kafka using DynamoDB Streams. Covers enabling streams, IAM policies, checkpoint tables, snapshot modes, table discovery, and operational constraints.

Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

Creates a MariaDB REST Service by configuring the REST metadata schema, creating the service and REST schema, and exposing tables, views, and routines as REST endpoints via SQL DDL in mariadb-shell.

Supabase-first database schema design for Nick's app stack. Generate production-ready Postgres schemas, Supabase SQL migrations, RLS policies, TypeScript types, seed data, role-aware access patterns, and schema checklists from plain English requirements. Use when starting any new project database, designing or reviewing schemas, adding tables, planning migrations, or turning product requirements into Supabase-ready data models. Bias toward product-led schema design, MVP discipline, core-workflow-first modeling, and avoiding premature table/role sprawl.

Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions; producing pivots, charts, forecasts, or summary workbooks; repairing malformed tables; or validating that spreadsheet edits and calculations are accurate.

Scaffold a MariaDB Schema Management (MSM) project and author the initial schema in the development folder, creating the project with msm.create_project and writing the first version's tables, objects, and grants into the MSM sections of <schema>_next.sql.

Structured data + RAG. NL2SQL hybrid patterns (text-to-SQL then execute vs embed rows), table embedding strategies (row-level, schema-level, hybrid), semantic layer integration (Cube, dbt metrics), LangChain SQLDatabaseChain, LlamaIndex PandasQueryEngine, safe SQL execution (read-only, sandboxed), schema-aware retrieval. Full PostgreSQL + pgvector hybrid code. USE WHEN: user mentions "tabular RAG", "NL2SQL", "text to SQL", "RAG on tables", "database RAG", "SQL RAG", "semantic layer", "structured data RAG" DO NOT USE FOR: unstructured doc RAG - use `rag-architecture`; metadata filtering only - use `self-querying-retriever`; KG retrieval - use `graph-rag`

Use for writing, reviewing, debugging, or testing Python code that inspects, edits, extracts, validates, preserves, or generates Excel .xlsx or .xlsm workbooks. Trigger on workbook contracts, formulas and cached values, Excel Tables, defined names, OOXML parts, types and precision, macros, charts, hidden sheets, external links, and semantic workbook verification. Do not use for CSV-only work, dataframe computation with no workbook boundary, Excel UI automation, recalculation, connection refresh, or macro execution.