Results for “data-structure”

Extract public web data, search engine results, and structured data from platforms like Google, Amazon, and Zillow using HasData APIs.

Detect, validate, and generate Schema.org structured data in JSON-LD format for SEO and rich results.

Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".

Redis database management. Key-value operations, caching, pub/sub, and data structure commands.

Generate JSON-LD structured data for AEM Edge Delivery Services pages by analyzing page content and metadata to determine schema.org types, extract properties, and produce validated snippets for head.html or scripts.js.

Adds, fixes, and optimizes schema.org structured data (JSON-LD) for rich results, including validation and implementation guidance.

Generate charts and visualizations from structured data using D3.js and Chart.js.

Implement schema.org structured data markup to help search engines understand content and enable rich results in search.

Design and evaluate programmatic SEO strategies for creating SEO-driven pages at scale using templates and structured data.

Detects, validates, and generates Schema.org structured data, preferring JSON-LD, with guidance on supported types and common errors.

Extract structured fields from p45 documents.

Extract structured fields from p60 documents.

Analyzes and optimizes content structure including header hierarchy, suggests schema markup, and internal linking opportunities. Creates search-friendly content organization. Use PROACTIVELY for content structuring.

Extract structured fields from 1098 documents.

Provides guidance on using Apache Spark RDDs, including creation, transformations, actions, and performance considerations.

Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.

Audit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.

Analyzes project folder structures, auto-detects technology stacks, and generates detailed blueprints with naming conventions, file placement patterns, and templates for consistent code organization.

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`

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

**Structure:**

When the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," or "site navigation." Use this whenever someone is planning what pages a website should have and how they connect. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.

Extract structured fields from p11d documents.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

结构化业务数据分析：附件读取与口径核验、定向筛选、规则/阈值判定、指标异动归因、漏斗/留存/实验分析、经营复盘及可审计报告。当用户提供 Excel、CSV、PDF、图片或多份业务材料，要求查数、判异常、解释变化、比较方案或形成行动建议时使用。Use for evidence-grounded analysis of structured business data, including filtering, rule checks, reconciliation, diagnostics, experiments, and decision reports.

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.

Use HasData APIs for web scraping and structured web data extraction.

Design reliable, scalable, and maintainable data systems by applying principles from storage engines, replication, partitioning, transactions, and consistency models.