Skill UI

Offers automated guidance for implementing data lineage trackers across ETL and streaming pipelines, including best practices on transformations, orchestration, and validation for production environments.

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.

This skill provides production patterns for implementing robust data quality validation using Great Expectations, dbt tests, and formal data contracts. It is essential for building reliable, automated, and verifiable data pipelines, ensuring data integrity throughout the entire ETL/ELT process, and establishing clear data governance rules.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.

A comprehensive guide detailing optimization techniques for Databricks environments. It covers optimal cluster sizing for various workloads (ETL, ML, Streaming), advanced Spark configurations (AQE, memory management), and deep data optimization strategies for Delta Lake, including Z-Ordering and Liquid Clustering. It also provides methods for query plan analysis and join optimization.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

This comprehensive guide provides structured steps and code examples for upgrading Databricks Runtime versions and performing critical data migrations. It covers best practices for version compatibility checking, transitioning from Hive Metastore to Unity Catalog using methods like SYNC and CTAS, and updating deprecated API endpoints, ensuring a smooth platform evolution for data pipelines.

Automates dbt test creation within data pipelines, offering guided best practices, code generation, and validation to keep ETL, transformation, and streaming workflows reliable.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.