Loading...
Loading...
Found 14 Skills
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"
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.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.
Use when asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.
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.
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions
Generate synthetic test data with edge cases for ETL pipeline testing.