Total 54,366 skills, Data Processing has 2782 skills
Showing 12 of 2782 skills
Create and manage Kibana Dashboards and Lens visualizations. Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.
Analytics tracking, interpretation, funnel analysis, product metrics, and ROI measurement. Use when setting up GA4/GTM tracking, interpreting analytics data, analyzing conversion funnels, calculating ROI, or measuring product engagement. Triggers on "analytics," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "tracking plan," "funnel analysis," "conversion rates," "user flow," "cohort analysis," "retention," "product metrics," "North Star metric," "ROI," "break-even," "payback period," "investment analysis," "validate my funnel," "why isn't my funnel converting," or "executive financial report." For A/B test setup, see ab-test-setup.
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.
Ingestion pipeline architecture overview and convention reference. Use when you need a quick orientation to the pipeline framework or want to know which doctor agent to use for a specific concern.
Fetch analytics from Umami. Use when the user asks about umami, analytics, website traffic, daily stats, pageviews, visitors, how is my site doing, traffic report, site performance, bounce rate, visitor count, active users, who is on my site, or website statistics.
Write Milvus application-level Jupyter notebook examples using a Markdown-first workflow with jupyter-switch for format conversion.
Tinybird Python SDK for defining datasources, pipes, and queries in Python. Use when working with tinybird-sdk, Python Tinybird projects, or data ingestion and queries in Python.
Create an Extruct company table from user-provided data, upload rows, and optionally add enrichment columns. Handles the full flow: parse input (CSV, pasted list, or structured data), create or reuse a table, upload domains in batches, add agent columns, and trigger enrichment. Triggers on: "create table", "upload companies", "add to extruct", "new extruct table", "import companies", "upload list to extruct".
Learn about football analytics concepts and explore provider documentation. Use when the user asks what a metric means (xG, PPDA, expected threat, xT), wants learning resources, papers, or courses, is new to football analytics, or wants a learning path. Also use when the user asks about data provider documentation — qualifier IDs, coordinate systems, event types, API schemas, field mappings — or wants to compare providers, look something up in the docs, or find out what data a provider offers.
Actian integration. Manage data, records, and automate workflows. Use when the user wants to interact with Actian data.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.