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Found 32 Skills
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"
End-to-end ETL pipeline and analytics application for Harvard Art Museums API with Streamlit dashboards
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
DataWorks data development Skill. Create, configure, validate, deploy, update, move, and rename nodes and workflows. Manage components, file resources, and UDF functions. Covers 150+ node types: Shell, SQL, Python, DI, Flink, EMR, etc. Supports scheduled and manual workflow orchestration via aliyun CLI or Python SDK. WARNING: Supports mutating operations (Move, Rename) requiring explicit user confirmation. Delete operations are NOT supported by this skill. Triggers: DataWorks, data development nodes, workflows, FlowSpec, scheduling tasks, data integration, ETL pipelines, .spec.json. Also triggers for Alibaba Cloud data development, scheduling node configuration, FlowSpec format, or DI task orchestration.
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
ETL pipeline and analytics application for Harvard Art Museums API with SQL storage and Streamlit visualization
This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.
AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions
Generate synthetic test data with edge cases for ETL pipeline testing.