ELT Schedule Management System

Introduction

The ELT Schedule Management System is designed to streamline and automate the management and execution of ETL (Extract, Transform, Load) jobs. These jobs are essential in data processing pipelines, where data is extracted from various sources, transformed into the required format, and loaded into a target database or data warehouse. The system enhances the efficiency, reliability, and manageability of these processes.

Features

• Automate ETL Processes: Schedule and execute ETL jobs automatically to ensure consistent data updates.
• Efficient Job Management: Create, configure, and manage ETL jobs and tasks, ensuring organized workflows.
• Kafka Integration: Utilize Kafka for reliable task communication and coordination.
• Flexible Execution: Support both manual and automatic job execution.
• Job Monitoring and Logging: Maintain detailed logs for monitoring, troubleshooting, and ensuring data integrity.
• User Control: Run, skip, or cancel jobs as needed.
• Scalability and Reliability: Handle large data volumes and concurrent users with high availability.
• Security and Compliance: Enforce user authentication and role-based access control.
• Enhanced Data Quality: Ensure regular, reliable data updates.
• Simplify Complex Workflows: User-friendly interface for configuring and linking tasks.

System Overview

Architecture

1. Frontend (Angular): User interface for job management and monitoring.
2. Backend (Java, Spring Boot): Business logic, task management with Kafka, database interactions.
3. Database (PostgreSQL): Storage for users, jobs, tasks, logs, schedules.

Key Components

• User Interface: Dashboard, Job Management, Task Configuration, Logs.
• Job Scheduler: Manual and automatic job execution.
• Task Manager: Task configuration and linking using Kafka.
• Job Execution and Monitoring: Real-time job monitoring, detailed logging.
• User Management: Role-based access control, user profiles.

Functionalities

• Create and manage ETL jobs.
• Configure source tasks with Kafka.
• Schedule jobs for automatic or manual execution.
• Monitor jobs and maintain logs.
• User control to run, skip, or cancel jobs.

System Benefits

• Automation: Reduces manual intervention by scheduling and executing ETL jobs automatically.
• Efficiency: Streamlines job creation, configuration, and management.
• Integration: Utilizes Kafka for task communication and PostgreSQL for data storage.
• Flexibility: Supports both manual and automatic job execution.
• Monitoring: Provides real-time job monitoring and detailed logging.
• User Control: Allows users to run, skip, or cancel jobs.
• Scalability: Designed to handle large data volumes and multiple concurrent users.
• Reliability: Ensures high availability with minimal downtime.
• Security: Enforces encrypted data and role-based access.
• Maintainability: Features modular code and comprehensive documentation.

Motivation

• Efficiency: Automates and streamlines ETL workflows.
• Reliability: Ensures consistent, error-free data processing.
• Real-Time Monitoring: Immediate oversight and detailed logging.
• Scalability: Handles large data volumes and numerous concurrent users.
• Flexibility: Offers manual and automatic job execution.
• High Availability: Designed for 99.9% uptime.
• Security: Implements encrypted data and role-based access control.
• User Control: Empowers users to manage job execution.
• Integration: Leverages Kafka and PostgreSQL.
• Operational Efficiency: Enhances overall data integrity and efficiency.

Problem Statement

• Manual ETL Processes: Significant manual intervention is required, leading to inefficiencies and errors.
• Lack of Automation: Limited automation capabilities hinder consistent and reliable ETL job execution.
• Poor Monitoring and Logging: Difficult to track job status and troubleshoot issues.
• Scalability Issues: Struggles to handle increasing data volumes and concurrent users.
• Inflexibility: Difficulty adapting ETL schedules and execution methods.
• Inadequate Integration: Challenges in integrating task management with Kafka.
• Security Concerns: Risk of data breaches and unauthorized access.

Objectives

• Automation: Implement automated scheduling and execution of ETL jobs.
• Efficiency: Streamline ETL workflows.
• Monitoring and Logging: Enhance monitoring and logging capabilities.
• Scalability: Handle increasing data volumes and concurrent users.
• Flexibility: Allow flexible scheduling and execution options.
• Integration: Integrate with Kafka.
• Security: Implement stringent security measures.
• User Control: Provide granular control over job execution.

Project Scope

• Develop a Scheduler Management System for ETL, integrating with Kafka and PostgreSQL.
• Implement job creation, task configuration, scheduling, monitoring, and logging functionalities.
• Ensure automation of ETL processes.
• Focus on scalability and security measures.
• Aim to streamline ETL workflows and maintain data integrity.

Assumptions and Dependencies

• Users have access to a Kafka cluster and PostgreSQL database.
• The system will be deployed in a secure network environment.