On-Premise to Cloud Data Migration and Streaming Analytics
A comprehensive data engineering project focused on secure data migration and real-time data streaming, establishing seamless cloud integration and efficient data processing for IoT analytics.
Technologies Used
Key Features
- • Secure Data Migration
- • Real-time Streaming
- • IoT Analytics
Security & Performance
- • IAM Roles & Policies
- • Auto-scaling
- • Network Optimization
Project Overview
I worked on a data engineering project focused on secure data migration and real-time data streaming. The goal was to establish an on-premise source connection to transfer data to the cloud and create a streaming pipeline for real-time IoT data analytics.
Key Objectives
- • Migrate data from an on-premise file server to AWS cloud storage securely
- • Design and implement a real-time streaming pipeline for continuous data ingestion and processing
- • Ensure data integrity, security, and scalability by leveraging AWS services
Implementation Details
1. On-Premise to Cloud Data Migration
- • Configured an AWS Storage Gateway to enable secure data transfer from an on-premise file server to Amazon S3
- • Used AWS CloudFormation for Infrastructure as Code (IaC) to automate deployment of necessary services
- • Set up monitoring and alerts with AWS CloudWatch to track data transfer and system health
2. Streaming Data Pipeline for IoT Analytics
- • Built a real-time data streaming pipeline using Amazon Kinesis for ingesting IoT data
- • Integrated AWS Lambda for real-time processing and transformation of incoming data
- • Stored structured data in Amazon DynamoDB for efficient querying and analytics
- • Used Amazon EC2 instances to run additional processing workloads and facilitate system scalability
- • Configured AWS SNS (Simple Notification Service) to trigger alerts based on system health and failures
3. Security and Performance Optimization
- • Implemented IAM roles and policies to control data access and security
- • Optimized network configurations for seamless data transfer with minimal latency
- • Set up auto-scaling to handle fluctuations in streaming data volume
Outcome & Impact
This project delivered a secure, scalable, and real-time data pipeline, enabling seamless cloud integration and efficient data processing for IoT analytics. The solution enhanced operational efficiency, ensuring data availability and integrity while adhering to best AWS practices.
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