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Found 41 Skills
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Ingest and transform large data files (CSV/JSON) into Elasticsearch indices. Stream-based processing for files up to 30GB, cross-version migration (ES 8.x ↔ 9.x), custom JavaScript transformations, and reindexing with transforms. Use when you need to load data into Elasticsearch, migrate indices, or transform data during ingestion.
Integrate external APIs and services with error handling, retry logic, and data transformation. Use when connecting to payment processors, messaging services, analytics platforms, or other third-party providers.
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
Guide for creating GreptimeDB Pipeline, by which user can add a process layer to GreptimeDB between ingestion and storage, to transform data.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
dbt (data build tool) patterns for model organization, incremental strategies, and testing.
Guide for creating Nushell plugins in Rust using nu_plugin and nu_protocol crates. Use when users want to build custom Nushell commands, extend Nushell with new functionality, create data transformations, or integrate external tools/APIs into Nushell. Covers project setup, command implementation, streaming data, custom values, and testing.
Advanced Juicebox data migration strategies. Use when migrating from other recruiting platforms, performing bulk data imports, or implementing complex data transformation pipelines. Trigger with phrases like "juicebox data migration", "migrate to juicebox", "juicebox import", "juicebox bulk migration".
Create and manage Infrahub transforms. Use when building data transformations, config generation, or any workflow that converts Infrahub data into a different format (JSON, text, CSV, device configs) using Python or Jinja2 templates.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Design an end-to-end MotherDuck pipeline. Use when choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, publication targets, or whether DuckLake is actually required.