Total 30,774 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Expert-level Power BI, DAX, M language, data modeling, Power Query, report design, and paginated reports
Convert JSON rows with WKT geometry strings into a GeoJSON FeatureCollection using raw PostGIS SQL.
Convert JSON rows with latitude/longitude fields into a GeoJSON FeatureCollection using raw PostGIS SQL.
Complete DataForSEO API integration for SEO data and analysis. Use when the user asks for keyword research, search volume, SERP analysis, backlink audits, competitor analysis, rank tracking, domain authority, technical SEO audits, content monitoring, Google Trends, or any SEO-related data queries. Covers all DataForSEO APIs including SERP, Keywords Data, DataForSEO Labs, Backlinks, OnPage, Domain Analytics, Content Analysis, Business Data, Merchant, App Data, and AI Optimization APIs. Outputs CSV files.
Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.
Materialize documentation for SQL syntax, data ingestion, concepts, and best practices. Use when users ask about Materialize queries, sources, sinks, views, or clusters.
Expert data analysis and manipulation for customer support operations using pandas
Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
Database operations including querying, schema exploration, and data analysis. Activates for tasks involving PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Elasticsearch, or ClickHouse databases.
查询各类数据信息，包括汇率、农历、历史事件、百科、油价、金价和化学元素。Use when users need exchange rates, lunar calendar, historical events, encyclopedia, commodity prices, or chemical element information.
Subscribe to AI and tech RSS feeds and persist normalized metadata into SQLite using mature Python tooling (feedparser + sqlite3). Use when adding feed URLs/OPML sources, running incremental sync with deduplication, and storing entry metadata without full-text extraction or summarization.