Results for “dblp”

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.

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Guides OLTP database schema design through six stages: domain and access patterns, entities and relationships, keys and constraints, normalization trade-offs, indexing and performance, and operations and evolution. Includes a final review checklist and tips for effective guidance.

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

Use for writing, reviewing, debugging, testing, or optimizing Python code that embeds DuckDB, executes analytical SQL, manages DuckDB connections and transactions, builds DuckDB relations, queries Parquet/CSV/Arrow/pandas/Polars inputs, or exports query results. Trigger on connection scope, parameters, replacement scans, materialization, concurrency, extensions, and query plans. Do not use for generic SQL with another engine, DuckDB CLI-only work, server-database administration, dbt-only projects, or dataframe work that does not call DuckDB.

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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

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

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

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

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

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.

SQL with dbt. SQL queries.

Transforms data in warehouses using dbt with SQL models, tests, and documentation. Use for analytics engineering and data transformation.

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation.

Connects AI agents to SurrealDB via built-in MCP (SurrealDB 3.1+) or standalone surrealmcp for database operations.

Use for creating, reviewing, debugging, or testing dbt SQL projects involving models, sources, ref, tests, macros, materializations, incremental models, snapshots, seeds, selectors, compiled SQL, or dbt artifacts. Do not use for generic SQL with no dbt graph, warehouse administration, semantic-layer product configuration, or orchestration outside dbt.

Oracle Database guidance for SQL, PL/SQL, SQLcl, ORDS, administration, app development, performance, security, migrations, and agent-safe database workflows. Use when the user asks to write, edit, rewrite, review, format, debug, tune, or explain SQL; create or refactor PL/SQL; use SQLcl, Liquibase, ORDS, JDBC, node-oracledb, Python, Java, .NET, or database frameworks; troubleshoot queries, sessions, locks, waits, indexes, optimizer plans, AWR, ASH, migrations, schemas, users, roles, privileges, backup, recovery, Data Guard, RAC, multitenant, containers, monitoring, auditing, encryption, VPD, or safe agent database operations.