Data Engineer | Python, SQL, AWS, Azure, GCP, Databricks | Building Scalable Data Pipelines, ML & Generative AI Solutions
Day 13 in AWS – Managed Databases with Amazon RDS! 🗄️⚡ Today, I explored Amazon RDS, AWS’s fully managed relational database service. Instead of spending hours on backups, patching, and scaling, RDS lets you focus on data and applications. 🎯 Why RDS? Managing databases manually is time-consuming and error-prone. RDS automates administration tasks while still giving you the power of relational databases like MySQL, PostgreSQL, MariaDB, SQL Server, and Oracle. 🧭 What I explored today: ✅ Launching an RDS Instance – Spun up a PostgreSQL database with just a few clicks. ✅ Backups & Snapshots – Learned how RDS automates backups and enables point-in-time recovery. ✅ High Availability (Multi-AZ) – Understood how RDS provides failover support for production-grade reliability. ✅ Read Replicas – Explored horizontal scaling for read-heavy workloads. 💡 Tip of the Day: Always enable Multi-AZ deployment for production workloads. It’s a small cost compared to the reliability and uptime you gain. 🧠 Key Takeaway: RDS takes away the undifferentiated heavy lifting of database management so you can focus on building applications and pipelines that use the data. 🔜 What’s Next? Tomorrow, I’ll dive into Amazon Aurora, AWS’s cloud-optimized relational database that takes RDS to the next level in performance and scalability. 👉 Question for you: Which RDS engine do you prefer (Postgres, MySQL, etc.)—and why? #AWS #DataEngineering #RDS #CloudDatabases #100DaysOfCloud #LearnInPublic #Day13
