api-development
FastAPI, REST APIs, GraphQL, data service design, and API best practices
Discover Business skills, compare source repositories and read the instructions for each workflow.
FastAPI, REST APIs, GraphQL, data service design, and API best practices
Statistics, probability, linear algebra, and mathematical foundations for data science
Data pipeline architecture, ETL/ELT patterns, data modeling, and production data platform design
Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture
Terraform, Pulumi, CloudFormation, and infrastructure as code for data platforms
Master Python fundamentals, OOP, data structures, async programming, and production-grade scripting for data engineering
Prometheus, Grafana, logging, alerting, and data pipeline observability
Portfolio building, technical interviews, job search strategies, and continuous learning
pytest, data validation, Great Expectations, and quality assurance for data systems
Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML
SQL query optimization, schema design, indexing strategies, and relational database mastery for production data systems
MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications
MLflow, model versioning, experiment tracking, model registry, and production ML systems
Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines
GitHub Actions, GitLab CI, Jenkins, and automated deployment pipelines
Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions
Git workflows, branching strategies, collaboration, and code management
PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production
Write Arcade expressions for dynamic calculations in popups, renderers, labels, and field calculations. Use for data-driven styling, custom labels, and computed fields.
Handle user interaction with map features including popups, editing, sketching, and event handling. Use for creating interactive map applications with feature selection, editing workflows, and custom interactio...
Work with temporal data using TimeSlider, TimeExtent, and time-aware layers. Use for animating data over time, filtering by date ranges, and visualizing temporal patterns.
Add and manage data layers in ArcGIS maps. Use for FeatureLayer, TileLayer, SceneLayer, GeoJSONLayer, and all other layer types. Covers layer configuration, queries, and management.
Configure SceneView environment settings including shadows, lighting, backgrounds, underground navigation, and elevation modes. Use for realistic 3D visualizations.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.