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Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

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.

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 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 guide outlines a production-ready lakehouse architecture using Unity Catalog, Delta Lake, and the Medallion pattern. It provides best practices for organizing Databricks workspaces, implementing three-level governance, defining compute strategies, and adopting CI/CD best practices with Asset Bundles for robust team collaboration.

This comprehensive guide sets up a robust CI/CD pipeline for Deepgram integrations using GitHub Actions. It covers unit tests (mocked SDK), full integration tests against the live Deepgram API (including transcription, diarization, and TTS), and smoke tests. Use this when establishing automated testing, ensuring deployment stability, or implementing continuous delivery workflows for Deepgram-powered applications.

Expert in modern CI/CD pipelines, GitOps workflows, and platform engineering. Specializes in designing and implementing secure, zero-downtime deployment strategies for enterprise-scale applications. Proficient with Kubernetes, ArgoCD, Terraform, and advanced security compliance practices.

Guides production-ready deployment workflows covering CI/CD pipelines, Docker multi-stage builds, health checks, rollback tactics, and GitHub Actions automation for web services.

This guide outlines advanced architectural patterns for building multi-stage CI/CD pipelines. It covers best practices for implementing robust deployment gates, including manual approvals, time-based triggers, and multi-approver workflows. It details various deployment strategies like Rolling, Blue-Green, and Canary releases, ensuring controlled, zero-downtime, and highly reliable software releases.

Automatically analyzes CI/CD performance metrics, compares them against baselines and thresholds, and flags statistical regressions so teams catch response-time or throughput degradations before deployment.

A comprehensive, end-to-end workflow bundle guiding developers through the entire software development lifecycle. It covers project scaffolding, frontend development (React/Next.js), robust backend architecture, database design, rigorous testing (TDD, E2E), code quality review, and modern CI/CD deployment practices for web and mobile applications.

Blueprint for designing GitHub Actions-powered CI/CD pipelines that leverage Docker multi-stage builds, Helm chart environments, ArgoCD GitOps, Kubernetes manifests, and monitoring best practices to keep deployments reliable across teams.