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.

Guides engineers through data partitioner setups within ETL workflows, offering best practice advice, ready-to-run code, and validation for streaming and batch pipelines.

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 comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse 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.

Guides you through generating dbt models inside data pipelines, covering setup, code generation, configuration, and validation while following best practices for ETL, orchestration, and streaming workloads.

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

Automates file format conversion guidance within data pipelines, covering ETL, workflow orchestration, data transformation, and streaming standards with production-ready examples.

Automated assistance for Flink job creation in data pipelines, triggered by Flink job creator requests. Guides ETL, streaming, and workflow orchestration, generates production-ready code/configurations, and validates outputs against industry standards.

This skill provides comprehensive data handling capabilities specifically for the Guidewire platform. It covers robust entity management (CRUD operations using Gosu), complex data migration using ETL tools and batch processing, and advanced data governance practices. Key features include implementing GDPR data purge rules and anonymization techniques for secure testing environments. Ideal for developers managing core insurance platform data.

This skill provides a comprehensive deep dive into migrating from legacy or self-managed insurance systems to Guidewire Cloud. It covers the entire migration lifecycle, including initial system assessment, product configuration, complex data migration (policies, claims, accounts via ETL), API integration cutover, and final UAT to go-live stages. It is designed for technical teams planning a major platform upgrade.