Skill UI

This guide provides expert patterns for building robust, observable infrastructure for database migrations. It covers real-time monitoring using Change Data Capture (CDC), metrics collection (Prometheus/Grafana), and anomaly detection. Use this skill when setting up CDC pipelines (e.g., Debezium with Kafka) or implementing comprehensive observability for database schema changes and data synchronization processes.

Specializes in generating production-grade SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, and SQL Server. Provides comprehensive plans covering data integrity, detailed rollback procedures, performance optimization, and validation checks for critical schema changes.

A specialized skill for optimizing database performance and scalability across PostgreSQL and MySQL. It guides users through analyzing slow queries, interpreting execution plans (EXPLAIN ANALYZE), designing optimal indexing strategies, tuning configuration parameters, and resolving bottlenecks like lock contention. Essential for DevOps engineers, database administrators, and backend developers.

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.

This skill provides a comprehensive guide on implementing robust data governance patterns within Delta Lake, leveraging Unity Catalog. It covers essential compliance topics such as data classification tagging, handling GDPR 'right-to-deletion' requests for PII, and establishing automated data retention policies. Use this when designing secure, compliant, and auditable data pipelines in a data lake environment.

A comprehensive guide detailing the setup process for Databricks CLI v2 and Python SDK. It covers essential steps for connecting to various Databricks workspaces, including configuring Personal Access Tokens (PAT), interactive OAuth U2M flows, and secure OAuth M2M service principals for CI/CD pipelines. Essential for developers integrating Databricks into their projects.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

This skill guides the setup and management of Databricks resources across development, staging, and production environments. It covers best practices for using isolated workspaces, managing environment-specific secrets, implementing Asset Bundles, and integrating with CI/CD pipelines to ensure consistent and reliable data deployments.

This skill provides comprehensive observability for the Databricks platform. It enables users to monitor job run histories, track cluster utilization and associated costs (DBU), detect slow SQL queries, and audit access logs. Use it for setting up robust monitoring dashboards, ensuring pipeline health, and optimizing resource consumption across the entire data stack.

A comprehensive guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

A comprehensive guide to production-ready patterns for the Databricks Python SDK. This skill covers essential best practices including singleton client initialization, structured error handling with typed exceptions, context-managed cluster lifecycle control (ensuring cleanup), and implementing type-safe job definitions. It is essential for building robust, maintainable, and idiomatic integrations with the Databricks platform.

Guides using Rube MCP’s Composio toolkit to discover current Datagma tools, verify connections, and execute workflows while adhering to live schemas, session reuse, and required memory parameters.