Results for “dblp”

Drafts a LaTeX paper section by section from an outline, with venue-specific templates and citation management.

Deploys endpoint Data Loss Prevention (DLP) controls to detect and prevent sensitive data exfiltration through email, USB, cloud storage, and printing using Microsoft Purview or Symantec DLP.

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.

Configures sensitivity labels, DLP policies, and endpoint data protection rules in Microsoft Purview to safeguard sensitive information across Exchange, SharePoint, OneDrive, Teams, and endpoints.

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Scaffold a New DirectLake Semantic Model (TMDL) (preview)

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Search scientific papers via the BGPT MCP server and retrieve structured experimental data — methods, results, conclusions, quality scores, and 25+ metadata fields per paper.

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 when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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.

DCLM: DataComp for Language Models

Scaffold a New Power BI Semantic Model (TMDL)

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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.

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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.

Guides users through installing dbt Core or the dbt Cloud CLI and points to official dbt documentation and community resources.

Debug Python: pdb REPL + debugpy remote (DAP).

Save the current diagnosis state to disk for cross-session recall, including conclusions, rejected directions, and next steps.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

Scans codebases for GDPR/DSGVO compliance issues, generates DPIA documentation, and tracks data subject rights requests with deadline management.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Analyze core dumps, debug live processes, and investigate crashes, deadlocks, and memory issues in C/C++ programs using GDB with source code correlation.