data▌

Senior-level data engineering expertise for building scalable pipelines, ETL systems, and production data infrastructure. \n \n Covers advanced patterns across data pipeline architecture, modeling, and DataOps with distributed computing frameworks (Spark, Airflow, dbt, Kafka) and modern data stack tools (Databricks, BigQuery, Snowflake) \n Includes production deployment patterns for scalable data processing, ML model serving with low latency, and real-time inference with auto-scaling and monitor

Related Skills:

Provides patterns for data fetching in Next.js App Router: server-side fetching, SWR/React Query integration, ISR, revalidation, error boundaries, and loading states.

Systematic methodology for profiling datasets, assessing data quality, discovering patterns, and understanding schemas.

When this skill does NOT apply: For concurrent access to data structures (mutexes, atomic operations), see go-concurrency. For defensive copying at API boundaries, see go-defensive. For pre-sizing capacity for performance, see go-performance.

Expert Core Data guidance for iOS/macOS stack setup, migrations, threading, and CloudKit sync. \n \n Covers stack initialization, fetch requests, NSFetchedResultsController, saving patterns, and merge conflict resolution across all Core Data store types \n Provides threading best practices for both traditional perform/performAndWait patterns and Swift Concurrency with actors and Sendable types \n Includes batch operations, persistent history tracking, lightweight and staged migrations, and perfo

Chart selection guidance, Python code patterns, and design principles for effective data visualizations. \n \n Comprehensive chart selection table covering 13+ chart types with guidance on when to use each and common anti-patterns to avoid (pie charts, 3D, dual-axis) \n Ready-to-use Python code examples for line charts, bar charts, histograms, heatmaps, small multiples, and interactive Plotly visualizations with professional styling \n Design principles covering color theory (sequential, divergi

Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL. \n \n Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries \n Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables \n Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn \n Generates structured markdown reports with datase

Category: service

Category: test