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Automated Backup and Recovery Pipeline for a Distributed Database Processing Application

Description

This project implements an automated backup and recovery solution for a distributed database processing application. It protects data across distributed database servers by regularly creating and managing backups, maintaining centralized recovery copies, and enabling database restoration during failures, corruption, or accidental data loss. The solution is designed to improve data protection, recovery reliability, and database availability.

Aim

To implement an automated backup and recovery architecture that protects distributed database workloads and enables reliable database restoration during failures or data-loss events.

Objectives

01 Automate database backup operations.
02 Protect distributed PostgreSQL databases.
03 Store backups in centralized cloud storage.
04 Support scheduled full and incremental backups.
05 Maintain WAL-based recovery capability.
06 Monitor backup and database health.
07 Detect backup failures automatically.
08 Automate database restoration.
09 Validate recovered database consistency.
10 Reduce manual recovery effort.

Application Workflow

01

Stage 1 – Database Registration

Process

Register distributed PostgreSQL database instances that require backup protection.

Tools
FastAPI PostgreSQL
Implementation

The application records database information, backup requirements, retention settings, and recovery configuration.

02

Stage 2 – Backup Scheduling

Process

Create scheduled backup jobs for registered databases.

Tools
Apache Airflow
Implementation

Airflow schedules and triggers database backup workflows according to the defined backup policy.

03

Stage 3 – Database Backup

Process

Create consistent backups of distributed PostgreSQL databases.

Tools
pgBackRest PostgreSQL
Implementation

pgBackRest performs full, differential, or incremental backups and manages required WAL information.

04

Stage 4 – Backup Storage

Process

Store backup data in centralized cloud storage.

Tools
pgBackRest Cloud S3
Implementation

pgBackRest transfers backup repositories to S3 and applies the configured retention policy.

05

Stage 5 – Backup Monitoring

Process

Monitor backup jobs, database health, storage usage, and failures.

Tools
Prometheus Grafana
Implementation

Prometheus collects backup and infrastructure metrics, while Grafana provides backup-status and database-health dashboards.

06

Stage 6 – Recovery and Restoration

Process

Restore a failed or corrupted database using the latest valid backup.

Tools
pgBackRest Ansible
Implementation

Python identifies the recovery requirement, pgBackRest restores the selected backup, and Ansible automates the required server/database configuration. pgBackRest also supports point-in-time recovery using archived WAL.

07

Stage 7 – Recovery Validation

Process

Verify that the restored database is available and data is consistent.

Tools
PostgreSQL Prometheus
Implementation

Python performs recovery validation, PostgreSQL checks database availability, and Prometheus confirms system health.

Cloud Infrastructure and Tools

Cloud Compute Infrastructure Cloud EC2

Provides compute resources for database processing, backup, and recovery services.

Cloud Object Storage Cloud S3

Stores database backup repositories and recovery data.

Cloud Networking Cloud VPC

Provides the isolated network environment for database and backup workloads.

Persistent Block Storage Cloud EBS

Provides persistent storage for database and recovery workloads.

Identity and Access Management Cloud IAM

Controls access to database backup and cloud resources.

Network Security Security Groups + Network ACLs

Controls network traffic to database and backup infrastructure.

Database PostgreSQL

Provides the distributed database workloads that require backup and recovery.

Backup and Recovery pgBackRest

Performs PostgreSQL backup, WAL archiving, restore, and recovery operations.

Workflow Orchestration Apache Airflow

Schedules and orchestrates automated backup and recovery workflows.

Metrics Collection Prometheus

Collects backup, database, application, and infrastructure metrics.

Monitoring and Visualization Grafana

Provides dashboards for backup status, database health, and recovery monitoring.

Container Platform Docker

Packages backup management and supporting application services into containers.

Container Orchestration Kubernetes

Deploys and manages containerized backup management services.

Infrastructure Provisioning OpenTofu

Automates provisioning of cloud infrastructure resources.

Configuration Management Ansible

Automates database server configuration, backup configuration, and recovery deployment.

Implementation Process

01
Step 1 – Analyze Existing Database Environment
  • Identify all distributed PostgreSQL databases and understand how they are currently being processed.
  • Identify critical databases, tables, and data that must be protected.
  • Review the existing backup and recovery methods and identify their limitations.
  • Define backup frequency, retention period, RPO, and RTO based on business requirements.
  • Define the storage, network, and recovery requirements for the target backup architecture.
02
Step 2 – Create Centralized Backup Infrastructure
  • Create the Cloud VPC and required network resources for the backup environment.
  • Provision EC2 and EBS resources for backup management and database recovery.
  • Configure Cloud S3 as centralized storage for database backups.
  • Configure IAM, Security Groups, and Network ACLs to secure backup access.
  • Use OpenTofu and Ansible to automate infrastructure provisioning and configuration.
03
Step 3 – Implement Automated Backup Pipeline
  • Configure pgBackRest on the PostgreSQL servers and connect it to the backup repository.
  • Configure full, differential, and incremental backup schedules according to requirements.
  • Configure PostgreSQL WAL archiving to support database recovery and point-in-time recovery.
  • Use Apache Airflow to automatically schedule and execute backup workflows.
  • Store completed backups in S3, apply retention policies, and verify backup availability.
04
Step 4 – Implement Recovery and Monitoring
  • Configure Prometheus to monitor database health, backup jobs, storage, and system resources.
  • Create Grafana dashboards to monitor backup status, failures, and database health.
  • Develop Python-based recovery logic to identify recovery requirements and initiate recovery tasks.
  • Use Ansible to configure the recovery environment and automate restoration activities.
  • Use pgBackRest to restore the required backup and WAL data, then validate the recovered database.
05
Step 5 – Test and Validate the Complete Solution
  • Simulate database failure, data corruption, and accidental data deletion scenarios.
  • Execute the automated backup and recovery workflow for each failure scenario.
  • Verify database structure, records, WAL recovery, and overall data consistency after restoration.
  • Measure backup and recovery time and compare the results with the defined RPO and RTO.
  • Optimize the backup schedule, storage, recovery process, and automation based on test results.

Proposed Solution

The proposed solution uses an Automated Database Backup and Recovery Architecture. The distributed PostgreSQL databases are protected using pgBackRest. Apache Airflow automatically schedules and manages backup workflows, while backup repositories are stored in cloud S3. Prometheus and Grafana provide continuous visibility into backup and database health. When a database failure or data-loss event occurs, Python initiates the recovery process, pgBackRest restores the required backup and WAL data, and Ansible configures the recovery environment. The restored PostgreSQL database is then validated before returning to normal operation. This architecture reduces manual backup activities and provides a repeatable process for database protection, restoration, and recovery validation.

Benefits

Automated Backups : Reduces manual backup effort.
Centralized Storage : Stores backups in cloud storage.
Data Protection : Protects critical database data.
Fast Recovery : Enables structured database restoration.
Point-in-Time Recovery : Recovers data to a specific time when required.
Continuous Monitoring : Tracks database and backup health.
Failure Detection : Identifies backup and recovery failures.
Scalability : Supports multiple distributed databases.

Challenges

Large Data Volume : Requires significant storage and processing resources.
Backup Performance : Backups can affect database performance.
Data Consistency : Backups must contain consistent recoverable data.
Storage Management : Long-term retention increases storage usage.
Recovery Time : Large databases can take longer to restore.
Network Dependency : Backup transfers depend on reliable connectivity.
Backup Failures : Failed backups must be detected and handled quickly.
Security : Backup data requires strong access control and protection.
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