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    Databricks CI/CD Deployment Accelerator (Bundles + Automation Framework)

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    Sold by: CUBEANGLE 
    CUBEANGLE provides a comprehensive CI/CD automation solution for Databricks deployments, built to streamline and standardize the end-to-end release process across multiple workspaces and environments (Dev/Test/Prod).

    Overview

    Using AWS Databricks Asset Bundles combined with configurable automation scripts, our framework eliminates the manual and error-prone effort of deploying AWS Databricks solutions at scale.

    It enables organizations to implement repeatable, governed, and production-ready deployment pipelines that accelerate delivery while ensuring compliance and consistency.

    This solution is designed for teams managing complex AWS Databricks implementations across business domains and environments.

    Key Capabilities Include:

    • End-to-End Databricks Workspace Deployment Automation
    • Deployment of Databricks platform objects, including:
      • Jobs
      • Delta Live Tables Pipelines
      • Schemas and Catalog structures
      • Volumes and Storage Objects
      • Clusters and Compute Configurations
      • External Locations
    • Database Object Deployment, including:
      • Tables
      • Views
      • User-Defined Functions (UDFs)
    • Automated Permissions and Access Control Setup
      • Permissions
      • Unity Catalog governance alignment
    • Static Configuration Data Synchronization
      • Repeatable syncing of reference/config datasets across catalogs and environments
    • Fully Configurable Execution Framework
      • Each deployment step can be enabled or disabled based on client needs
      • Modular design supports phased adoption and customization

    Benefits

    • Accelerates Databricks release cycles and environment promotion
    • Reduces deployment risk through automation and consistency
    • Enables scalable multi-workspace governance and compliance
    • Eliminates manual deployment effort for data engineering and platform teams
    • Establishes a foundation for enterprise-grade Databricks DevOps practices

    Ideal Use Cases

    • Databricks platform rollout across Dev/Test/Prod workspaces
    • Enterprise Lakehouse deployments requiring controlled releases
    • Teams adopting Databricks Asset Bundles as a standard deployment method
    • Organizations needing automated Unity Catalog security and permissions propagation
    • Regulated industries with strong governance and audit requirements

    Outcomes

    • A repeatable, production-ready CI/CD deployment framework for Databricks
    • Automated promotion of notebooks, jobs, pipelines, and database objects
    • Standardized deployment and governance across environments
    • Reduced time-to-value for Databricks engineering and operations teams

    Highlights

    • End-to-End Databricks Deployment Automation Streamlines the complete deployment lifecycle across workspaces, including Jobs, Pipelines, Schemas, Clusters, Volumes, and External Locations.
    • Unified CI/CD for Platform and Database Objects Supports automated promotion of Databricks assets alongside database objects such as Tables, Views, and User-Defined Functions (UDFs) for consistent environment rollout.
    • Configurable, Governance-Ready Framework Provides modular deployment steps with automated permission management and static configuration synchronization across catalogs, enabling secure and repeatable enterprise releases.

    Details

    Delivery method

    Deployed on AWS
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    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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    Vendor Support

    CUBEANGLE Our provides a broad range of cloud, data, and AI services to help organizations modernize and scale their technology.

    As an AWS Partner, we use leading tools and best practices to deliver secure, high-performance solutions tailored to your industry.

    We support clients across sectors such as financial services, automotive, hospitality, and retail.

    Our services include:

    • Artificial Intelligence (AI)
    • Machine Learning (ML)
    • Data Governance
    • Data Migration
    • Data Processing & Analytics
    • Data Estate Modernization
    • DevOps

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