Databricks Disaster Recovery Kit

$69

Complete DR and business continuity with active-passive/active-active patterns, Terraform templates, failover automation, and RTO/RPO calculator.

📁 17 files🏷 v1.0.0

PythonTerraformMarkdownJSONAWSAzureGCPDatabricksDelta LakeRedis

📁 File Structure 17 files

databricks-disaster-recovery-kit/ ├── README.md ├── architecture/ │ ├── active_active.md │ ├── active_passive.md │ └── backup_restore.md ├── communication/ │ ├── postincident_review.md │ └── stakeholder_templates.md ├── cost/ │ └── dr_cost_model.md ├── scripts/ │ ├── delta_replication.py │ ├── failover_automation.py │ ├── secret_recovery.py │ └── unity_catalog_backup.py ├── terraform/ │ └── dr-workspace/ │ ├── main.tf │ ├── outputs.tf │ └── variables.tf ├── testing/ │ └── dr_test_plan.md └── tools/ └── rto_rpo_calculator.py

📖 Documentation Preview README excerpt

Databricks Disaster Recovery Kit

Product ID: databricks-disaster-recovery-kit

Version: 1.0.0

Author: [Datanest Digital](https://datanest.dev)

Price: $69 USD

Category: Enterprise

---

Overview

The Databricks Disaster Recovery Kit is a comprehensive, production-ready toolkit for

planning, implementing, and testing disaster recovery strategies across Databricks

deployments. It covers the full DR lifecycle — from architecture selection through

automated failover to post-incident review.

Whether you are running a single workspace or a multi-region lakehouse, this kit

provides the Terraform modules, Python automation scripts, architecture guides, cost

models, and test plans you need to protect your data platform against regional outages,

corruption events, and infrastructure failures.

What's Included

Architecture Guides

| Document | Description |

|----------|-------------|

| architecture/active_passive.md | Active-passive DR with warm standby workspace |

| architecture/active_active.md | Active-active multi-region with live traffic splitting |

| architecture/backup_restore.md | Cold standby with automated rebuild from backups |

Infrastructure as Code

| Module | Description |

|--------|-------------|

| terraform/dr-workspace/ | Complete Terraform module for provisioning a DR workspace in a secondary region, including networking, Unity Catalog, cluster policies, and IAM |

Automation Scripts

| Script | Description |

|--------|-------------|

| scripts/delta_replication.py | Delta Lake cross-region replication via deep clone and streaming sync |

| scripts/unity_catalog_backup.py | Unity Catalog metadata backup and restore procedures |

| scripts/secret_recovery.py | Secret scope and credential recovery automation |

| scripts/failover_automation.py | End-to-end pipeline failover: detect, switch, validate |

Tools

| Tool | Description |

|------|-------------|

| tools/rto_rpo_calculator.py | CLI tool mapping business SLAs to DR architecture recommendations |

Testing

| Document | Description |

|----------|-------------|

| testing/dr_test_plan.md | Quarterly DR test procedures with success criteria and runbooks |

Communication

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

scripts/delta_replication.py """Delta Lake Cross-Region Replication for Disaster Recovery. Databricks notebook for replicating Delta tables between primary and DR regions using deep clone operations and streaming Change Data Feed (CDF) sync. Supports: - Full deep clone for initial replication and periodic refresh - Streaming CDF-based incremental replication for near-real-time sync - Parallel table replication with configurable concurrency - Replication lag monitoring and alerting - Data validation between source and target Usage: Configure the widgets at the top of the notebook, then run all cells. Can also be scheduled as a Databricks workflow job. Datanest Digital — https://datanest.dev """ from __future__ import annotations import json import logging import time from concurrent.futures import ThreadPoolExecutor, as_completed from dataclasses import dataclass, field from datetime import datetime, timezone from enum import Enum from typing import Any, Optional # Databricks notebook environment provides `spark` and `dbutils` globally. # These imports are for type hints only — they resolve at runtime on the cluster. try: from pyspark.sql import DataFrame, SparkSession from pyspark.sql import functions as F except ImportError: pass # Running outside Databricks; types only logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") logger = logging.getLogger("delta_replication") # --------------------------------------------------------------------------- # Configuration # --------------------------------------------------------------------------- class ReplicationMode(str, Enum): """Supported replication modes.""" # ... 519 more lines ...

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